limitations of a research article

How to present limitations in research

Last updated

30 January 2024

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Limitations don’t invalidate or diminish your results, but it’s best to acknowledge them. This will enable you to address any questions your study failed to answer because of them.

In this guide, learn how to recognize, present, and overcome limitations in research.

• What is a research limitation?

Research limitations are weaknesses in your research design or execution that may have impacted outcomes and conclusions. Uncovering limitations doesn’t necessarily indicate poor research design—it just means you encountered challenges you couldn’t have anticipated that limited your research efforts.

Does basic research have limitations?

Basic research aims to provide more information about your research topic . It requires the same standard research methodology and data collection efforts as any other research type, and it can also have limitations.

• Common research limitations

Researchers encounter common limitations when embarking on a study. Limitations can occur in relation to the methods you apply or the research process you design. They could also be connected to you as the researcher.

Methodology limitations

Not having access to data or reliable information can impact the methods used to facilitate your research. A lack of data or reliability may limit the parameters of your study area and the extent of your exploration.

Your sample size may also be affected because you won’t have any direction on how big or small it should be and who or what you should include. Having too few participants won’t adequately represent the population or groups of people needed to draw meaningful conclusions.

Research process limitations

The study’s design can impose constraints on the process. For example, as you’re conducting the research, issues may arise that don’t conform to the data collection methodology you developed. You may not realize until well into the process that you should have incorporated more specific questions or comprehensive experiments to generate the data you need to have confidence in your results.

Constraints on resources can also have an impact. Being limited on participants or participation incentives may limit your sample sizes. Insufficient tools, equipment, and materials to conduct a thorough study may also be a factor.

Common researcher limitations

Here are some of the common researcher limitations you may encounter:

Time: some research areas require multi-year longitudinal approaches, but you might not be able to dedicate that much time. Imagine you want to measure how much memory a person loses as they age. This may involve conducting multiple tests on a sample of participants over 20–30 years, which may be impossible.

Bias: researchers can consciously or unconsciously apply bias to their research. Biases can contribute to relying on research sources and methodologies that will only support your beliefs about the research you’re embarking on. You might also omit relevant issues or participants from the scope of your study because of your biases.

Limited access to data : you may need to pay to access specific databases or journals that would be helpful to your research process. You might also need to gain information from certain people or organizations but have limited access to them. These cases require readjusting your process and explaining why your findings are still reliable.

• Why is it important to identify limitations?

Identifying limitations adds credibility to research and provides a deeper understanding of how you arrived at your conclusions.

Constraints may have prevented you from collecting specific data or information you hoped would prove or disprove your hypothesis or provide a more comprehensive understanding of your research topic.

However, identifying the limitations contributing to your conclusions can inspire further research efforts that help gather more substantial information and data.

• Where to put limitations in a research paper

A research paper is broken up into different sections that appear in the following order:

Introduction

Methodology

The discussion portion of your paper explores your findings and puts them in the context of the overall research. Either place research limitations at the beginning of the discussion section before the analysis of your findings or at the end of the section to indicate that further research needs to be pursued.

What not to include in the limitations section

Evidence that doesn’t support your hypothesis is not a limitation, so you shouldn’t include it in the limitation section. Don’t just list limitations and their degree of severity without further explanation.

• How to present limitations

You’ll want to present the limitations of your study in a way that doesn’t diminish the validity of your research and leave the reader wondering if your results and conclusions have been compromised.

Include only the limitations that directly relate to and impact how you addressed your research questions. Following a specific format enables the reader to develop an understanding of the weaknesses within the context of your findings without doubting the quality and integrity of your research.

Identify the limitations specific to your study

You don’t have to identify every possible limitation that might have occurred during your research process. Only identify those that may have influenced the quality of your findings and your ability to answer your research question.

Explain study limitations in detail

This explanation should be the most significant portion of your limitation section.

Link each limitation with an interpretation and appraisal of their impact on the study. You’ll have to evaluate and explain whether the error, method, or validity issues influenced the study’s outcome and how.

Propose a direction for future studies and present alternatives

In this section, suggest how researchers can avoid the pitfalls you experienced during your research process.

If an issue with methodology was a limitation, propose alternate methods that may help with a smoother and more conclusive research project . Discuss the pros and cons of your alternate recommendation.

Describe steps taken to minimize each limitation

You probably took steps to try to address or mitigate limitations when you noticed them throughout the course of your research project. Describe these steps in the limitation section.

• Limitation example

“Approaches like stem cell transplantation and vaccination in AD [Alzheimer’s disease] work on a cellular or molecular level in the laboratory. However, translation into clinical settings will remain a challenge for the next decade.”

The authors are saying that even though these methods showed promise in helping people with memory loss when conducted in the lab (in other words, using animal studies), more studies are needed. These may be controlled clinical trials, for example. 

However, the short life span of stem cells outside the lab and the vaccination’s severe inflammatory side effects are limitations. Researchers won’t be able to conduct clinical trials until these issues are overcome.

• How to overcome limitations in research

You’ve already started on the road to overcoming limitations in research by acknowledging that they exist. However, you need to ensure readers don’t mistake weaknesses for errors within your research design.

To do this, you’ll need to justify and explain your rationale for the methods, research design, and analysis tools you chose and how you noticed they may have presented limitations.

Your readers need to know that even when limitations presented themselves, you followed best practices and the ethical standards of your field. You didn’t violate any rules and regulations during your research process.

You’ll also want to reinforce the validity of your conclusions and results with multiple sources, methods, and perspectives. This prevents readers from assuming your findings were derived from a single or biased source.

• Learning and improving starts with limitations in research

Dealing with limitations with transparency and integrity helps identify areas for future improvements and developments. It’s a learning process, providing valuable insights into how you can improve methodologies, expand sample sizes, or explore alternate approaches to further support the validity of your findings.

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Organizing Your Social Sciences Research Paper

• Limitations of the Study
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
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The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE :   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
• Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods. Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Writing Limitations of Research Study — 4 Reasons Why It Is Important!

It is not unusual for researchers to come across the term limitations of research during their academic paper writing. More often this is interpreted as something terrible. However, when it comes to research study, limitations can help structure the research study better. Therefore, do not underestimate significance of limitations of research study.

Allow us to take you through the context of how to evaluate the limits of your research and conclude an impactful relevance to your results.

Table of Contents

What Are the Limitations of a Research Study?

Every research has its limit and these limitations arise due to restrictions in methodology or research design.  This could impact your entire research or the research paper you wish to publish. Unfortunately, most researchers choose not to discuss their limitations of research fearing it will affect the value of their article in the eyes of readers.

However, it is very important to discuss your study limitations and show it to your target audience (other researchers, journal editors, peer reviewers etc.). It is very important that you provide an explanation of how your research limitations may affect the conclusions and opinions drawn from your research. Moreover, when as an author you state the limitations of research, it shows that you have investigated all the weaknesses of your study and have a deep understanding of the subject. Being honest could impress your readers and mark your study as a sincere effort in research.

Why and Where Should You Include the Research Limitations?

The main goal of your research is to address your research objectives. Conduct experiments, get results and explain those results, and finally justify your research question . It is best to mention the limitations of research in the discussion paragraph of your research article.

At the very beginning of this paragraph, immediately after highlighting the strengths of the research methodology, you should write down your limitations. You can discuss specific points from your research limitations as suggestions for further research in the conclusion of your thesis.

1. Common Limitations of the Researchers

Limitations that are related to the researcher must be mentioned. This will help you gain transparency with your readers. Furthermore, you could provide suggestions on decreasing these limitations in you and your future studies.

2. Limited Access to Information

Your work may involve some institutions and individuals in research, and sometimes you may have problems accessing these institutions. Therefore, you need to redesign and rewrite your work. You must explain your readers the reason for limited access.

3. Limited Time

All researchers are bound by their deadlines when it comes to completing their studies. Sometimes, time constraints can affect your research negatively. However, the best practice is to acknowledge it and mention a requirement for future study to solve the research problem in a better way.

4. Conflict over Biased Views and Personal Issues

Biased views can affect the research. In fact, researchers end up choosing only those results and data that support their main argument, keeping aside the other loose ends of the research.

Types of Limitations of Research

Before beginning your research study, know that there are certain limitations to what you are testing or possible research results. There are different types that researchers may encounter, and they all have unique characteristics, such as:

1. Research Design Limitations

Certain restrictions on your research or available procedures may affect your final results or research outputs. You may have formulated research goals and objectives too broadly. However, this can help you understand how you can narrow down the formulation of research goals and objectives, thereby increasing the focus of your study.

2. Impact Limitations

Even if your research has excellent statistics and a strong design, it can suffer from the influence of the following factors:

• Presence of increasing findings as researched
• Being population specific
• A strong regional focus.

3. Data or statistical limitations

In some cases, it is impossible to collect sufficient data for research or very difficult to get access to the data. This could lead to incomplete conclusion to your study. Moreover, this insufficiency in data could be the outcome of your study design. The unclear, shabby research outline could produce more problems in interpreting your findings.

How to Correctly Structure Your Research Limitations?

There are strict guidelines for narrowing down research questions, wherein you could justify and explain potential weaknesses of your academic paper. You could go through these basic steps to get a well-structured clarity of research limitations:

• Declare that you wish to identify your limitations of research and explain their importance,
• Provide the necessary depth, explain their nature, and justify your study choices.
• Write how you are suggesting that it is possible to overcome them in the future.

In this section, your readers will see that you are aware of the potential weaknesses in your business, understand them and offer effective solutions, and it will positively strengthen your article as you clarify all limitations of research to your target audience.

Know that you cannot be perfect and there is no individual without flaws. You could use the limitations of research as a great opportunity to take on a new challenge and improve the future of research. In a typical academic paper, research limitations may relate to:

1. Formulating your goals and objectives

If you formulate goals and objectives too broadly, your work will have some shortcomings. In this case, specify effective methods or ways to narrow down the formula of goals and aim to increase your level of study focus.

2. Application of your data collection methods in research

If you do not have experience in primary data collection, there is a risk that there will be flaws in the implementation of your methods. It is necessary to accept this, and learn and educate yourself to understand data collection methods.

3. Sample sizes

This depends on the nature of problem you choose. Sample size is of a greater importance in quantitative studies as opposed to qualitative ones. If your sample size is too small, statistical tests cannot identify significant relationships or connections within a given data set.

You could point out that other researchers should base the same study on a larger sample size to get more accurate results.

4. The absence of previous studies in the field you have chosen

Writing a literature review is an important step in any scientific study because it helps researchers determine the scope of current work in the chosen field. It is a major foundation for any researcher who must use them to achieve a set of specific goals or objectives.

However, if you are focused on the most current and evolving research problem or a very narrow research problem, there may be very little prior research on your topic. For example, if you chose to explore the role of Bitcoin as the currency of the future, you may not find tons of scientific papers addressing the research problem as Bitcoins are only a new phenomenon.

It is important that you learn to identify research limitations examples at each step. Whatever field you choose, feel free to add the shortcoming of your work. This is mainly because you do not have many years of experience writing scientific papers or completing complex work. Therefore, the depth and scope of your discussions may be compromised at different levels compared to academics with a lot of expertise. Include specific points from limitations of research. Use them as suggestions for the future.

Have you ever faced a challenge of writing the limitations of research study in your paper? How did you overcome it? What ways did you follow? Were they beneficial? Let us know in the comments below!

Frequently Asked Questions

Setting limitations in our study helps to clarify the outcomes drawn from our research and enhance understanding of the subject. Moreover, it shows that the author has investigated all the weaknesses in the study.

Scope is the range and limitations of a research project which are set to define the boundaries of a project. Limitations are the impacts on the overall study due to the constraints on the research design.

Limitation in research is an impact of a constraint on the research design in the overall study. They are the flaws or weaknesses in the study, which may influence the outcome of the research.

1. Limitations in research can be written as follows: Formulate your goals and objectives 2. Analyze the chosen data collection method and the sample sizes 3. Identify your limitations of research and explain their importance 4. Provide the necessary depth, explain their nature, and justify your study choices 5. Write how you are suggesting that it is possible to overcome them in the future

Excellent article ,,,it has helped me big

This is very helpful information. It has given me an insight on how to go about my study limitations.

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the topic is well covered

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What are the limitations in research and how to write them?

Learn about the potential limitations in research and how to appropriately address them in order to deliver honest and ethical research.

It is fairly uncommon for researchers to stumble into the term research limitations when working on their research paper. Limitations in research can arise owing to constraints on design, methods, materials, and so on, and these aspects, unfortunately, may have an influence on your subject’s findings.

In this Mind The Graph’s article, we’ll discuss some recommendations for writing limitations in research , provide examples of various common types of limitations, and suggest how to properly present this information.

What are the limitations in research?

The limitations in research are the constraints in design, methods or even researchers’ limitations that affect and influence the interpretation of your research’s ultimate findings. These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity both internally and externally. 

Researchers are usually cautious to acknowledge the limitations of their research in their publications for fear of undermining the research’s scientific validity. No research is faultless or covers every possible angle. As a result, addressing the constraints of your research exhibits honesty and integrity .

Why should include limitations of research in my paper?

Though limitations tackle potential flaws in research, commenting on them at the conclusion of your paper, by demonstrating that you are aware of these limitations and explaining how they impact the conclusions that may be taken from the research, improves your research by disclosing any issues before other researchers or reviewers do . 

Additionally, emphasizing research constraints implies that you have thoroughly investigated the ramifications of research shortcomings and have a thorough understanding of your research problem. 

Limits exist in any research; being honest about them and explaining them would impress researchers and reviewers more than disregarding them. 

Remember that acknowledging a research’s shortcomings offers a chance to provide ideas for future research, but be careful to describe how your study may help to concentrate on these outstanding problems .

Possible limitations examples

Here are some limitations connected to methodology and the research procedure that you may need to explain and discuss in connection to your findings.

Methodological limitations

Sample size.

The number of units of analysis used in your study is determined by the sort of research issue being investigated. It is important to note that if your sample is too small, finding significant connections in the data will be challenging, as statistical tests typically require a larger sample size to ensure a fair representation and this can be limiting. 

Lack of available or reliable data

A lack of data or trustworthy data will almost certainly necessitate limiting the scope of your research or the size of your sample, or it can be a substantial impediment to identifying a pattern and a relevant connection.

Lack of prior research on the subject

Citing previous research papers forms the basis of your literature review and aids in comprehending the research subject you are researching. Yet there may be little if any, past research on your issue.

The measure used to collect data

After finishing your analysis of the findings, you realize that the method you used to collect data limited your capacity to undertake a comprehensive evaluation of the findings. Recognize the flaw by mentioning that future researchers should change the specific approach for data collection.

Issues with research samples and selection

Sampling inaccuracies arise when a probability sampling method is employed to choose a sample, but that sample does not accurately represent the overall population or the relevant group. As a result, your study suffers from “sampling bias” or “selection bias.”

Limitations of the research

When your research requires polling certain persons or a specific group, you may have encountered the issue of limited access to these interviewees. Because of the limited access, you may need to reorganize or rearrange your research. In this scenario, explain why access is restricted and ensure that your findings are still trustworthy and valid despite the constraint.

Time constraints

Practical difficulties may limit the amount of time available to explore a research issue and monitor changes as they occur. If time restrictions have any detrimental influence on your research, recognize this impact by expressing the necessity for a future investigation.

Due to their cultural origins or opinions on observed events, researchers may carry biased opinions, which can influence the credibility of a research. Furthermore, researchers may exhibit biases toward data and conclusions that only support their hypotheses or arguments.

The structure of the limitations section 

The limitations of your research are usually stated at the beginning of the discussion section of your paper so that the reader is aware of and comprehends the limitations prior to actually reading the rest of your findings, or they are stated at the end of the discussion section as an acknowledgment of the need for further research.

The ideal way is to divide your limitations section into three steps: 

1. Identify the research constraints; 

2. Describe in great detail how they affect your research; 

3. Mention the opportunity for future investigations and give possibilities. 

By following this method while addressing the constraints of your research, you will be able to effectively highlight your research’s shortcomings without jeopardizing the quality and integrity of your research.

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Limitations in Medical Research: Recognition, Influence, and Warning

Douglas e. ott.

Mercer University, Macon, Georgia, USA.

Background:

As the number of limitations increases in a medical research article, their consequences multiply and the validity of findings decreases. How often do limitations occur in a medical article? What are the implications of limitation interaction? How often are the conclusions hedged in their explanation?

To identify the number, type, and frequency of limitations and words used to describe conclusion(s) in medical research articles.

Search, analysis, and evaluation of open access research articles from 2021 and 2022 from the Journal of the Society of Laparoscopic and Robotic Surgery and 2022 Surgical Endoscopy for type(s) of limitation(s) admitted to by author(s) and the number of times they occurred. Limitations not admitted to were found, obvious, and not claimed. An automated text analysis was performed for hedging words in conclusion statements. A limitation index score is proposed to gauge the validity of statements and conclusions as the number of limitations increases.

A total of 298 articles were reviewed and analyzed, finding 1,764 limitations. Four articles had no limitations. The average was between 3.7% and 6.9% per article. Hedging, weasel words and words of estimative probability description was found in 95.6% of the conclusions.

Conclusions:

Limitations and their number matter. The greater the number of limitations and ramifications of their effects, the more outcomes and conclusions are affected. Wording ambiguity using hedging or weasel words shows that limitations affect the uncertainty of claims. The limitation index scoring method shows the diminished validity of finding(s) and conclusion(s).

INTRODUCTION

As the number of limitations in a medical research article increases, does their influence have a more significant effect than each one considered separately, making the findings and conclusions less reliable and valid? Limitations are known variables that influence data collection and findings and compromise outcomes, conclusions, and inferences. A large body of work recognizes the effect(s) and consequence(s) of limitations. 1 – 77 Other than the ones known to the author(s), unknown and unrecognized limitations influence research credibility. This study and analysis aim to determine how frequently and what limitations are found in peer-reviewed open-access medical articles for laparoscopic/endoscopic surgeons.

This research is about limitations, how often they occur and explained and/or justified. Failure to disclose limitations in medical writing limits proper decision-making and understanding of the material presented. All articles have limitations and constraints. Not acknowledging limitations is a lack of candor, ignorance, or a deliberate omission. To reduce the suspicion of invalid conclusions limitations and their effects must be acknowledged and explained. This allows for a clearer more focused assessment of the article’s subject matter without explaining its findings and conclusions using hedging and words of estimative probability. 78 , 79

An evaluation of open access research/meta-analysis/case series/methodologies/review articles published in the Journal of the Society of Laparoendoscopic and Robotic Surgery ( JSLS ) for 2021 and 2022 (129) and commentary/guidelines/new technology/practice guidelines/review/SAGES Masters Program articles in Surgical Endoscopy ( Surg Endosc ) for 2022 (169) totaling 298 were read and evaluated by automated text analysis for limitations admitted to by the paper’s authors using such words as “limitations,” “limits,” “shortcomings,” “inadequacies,” “flaws,” “weaknesses,” “constraints,” “deficiencies,” “problems,” and “drawbacks” in the search. Limitations not mentioned were found by reading the paper and assigning type and frequency. The number of hedging and weasel words used to describe the conclusion or validate findings was determined by reading the article and adding them up.

For JSLS , there were 129 articles having 63 different types of limitations. Authors claimed 476, and an additional 32 were found within the article, totaling 508 limitations (93.7% admitted to and 6.3% discovered that were not mentioned). This was a 3.9 limitation average per article. No article said it was free of limitations. The ten most frequent limitations and their rate of occurrence are in Table 1 . The total number of limitations, frequency, and visual depictions are seen in Figures 1A and ​ and 1B 1B .

( A ) Visual depiction of the ranked frequency of limitations for JSLS articles reviewed.

The Ten Most Frequent Limitations Found in JSLS and Surg Endosc Articles

There were 169 articles for Surg Endosc , with 78 different named limitations the authors claimed for a total of 1,162. An additional 94 limitations were found in the articles, totaling 1,256, or 7.4 per article. The authors explicitly stated 92.5% of the limitations, and an additional 7.5% of additional limitations were found within the article. Five claimed zero limitations (5/169 = 3%). The ten most frequent limitations and their rate of occurrence are in Table 1 . The total number of limitations and frequency is shown in Figures 1A and ​ and 1B 1B .

Conclusions were described in hedged, weasel words or words of estimative probability 95.6% of the time (285/298).

A research hypothesis aims to test the idea about expected relationships between variables or to explain an occurrence. The assessment of a hypothesis with limitations embedded in the method reaches a conclusion that is inherently flawed. What is compromised by the limitation(s)? The result is an inferential study in the presence of uncertainty. As the number of limitations increases, the validity of information decreases due to the proliferation of uncertain information. Information gathered and conclusions made in the presence of limitations can be functionally unsound. Hypothesis testing of spurious conditions with limitations and then claiming a conclusion is not a reliable method for generating factual evidence. The authors’ reliance on limitation gathered “evidence” data and asserting that this is valid is spurious reasoning. The bridge between theory and evidence is not through limitations that unquestionably accept findings. A range of conclusion possibilities exists being some percent closer to either more correct or incorrect. Relying on leveraging the pursuit of “fact” in the presence of limitations as the safeguard is akin to the fox watching the hen house. Acknowledgment of the uncertainty limitations create in research and discounting the finding’s reliability would give more credibility to the effort. Shortcomings and widespread misuses of research limitation justifications make findings suspect and falsely justified in many instances.

The JSLS instructions to authors say that in the discussion section of the paper the author(s) must “Comment on any methodological weaknesses of the study” ( http://jsls.sls.org/guidelines-for-authors/ ). In their instructions for authors, Surg Endosc says that in the discussion of the paper, “A paragraph discussing study limitations is required” ( https://www.springer.com/journal/464/submission-guidelines ). A comment for a written article about a limitation should express an opinion or reaction. A paragraph discussing limitations, especially, if there is more than one, requires just that: a paragraph and discussion. These requirements were not met or enforced by JSLS 86% (111/129) of the time and 92.3% (156/169) for Surg Endosc . This is an error in peer reviewing, not adhering to established research publication best practices, and the journals needing to adhere to their guidelines. The International Committee of Medical Journal Editors, uniform requirements for manuscripts recommends that authors “State the limitations of your study, and explore the implications of your findings for future research and for clinical practice or policy. Discuss the influence or association of variables, such as sex and/or gender, on your findings, where appropriate, and the limitations of the data.” It also says, “describe new or substantially modified methods, give reasons for using them, and evaluate their limitations” and “Include in the Discussion section the implications of the findings and their limitations, including implications for future research” and “give references to established methods, including statistical methods (see below); provide references and brief descriptions for methods that have been published but are not well known; describe new or substantially modified methods, give reasons for using them, and evaluate their limitations.” 65 “Reporting guidelines (e.g., CONSORT, 1 ARRIVE 2 ) have been proposed to promote the transparency and accuracy of reporting for biomedical studies, and they often include discussion of limitations as a checklist item. Although such guidelines have been endorsed by high-profile biomedical journals, and compliance with them is associated with improved reporting quality, 3 adherence remains suboptimal.” 4 , 5

Limitations start in the methodologic design phase of research. They require troubleshooting evaluations from the start to consider what limitations exist, what is known and unknown, where, and how to overcome them, and how they will affect the reasonableness and assessment of possible conclusions. A named limitation represents a category with numerous components. Each factor has a unique effect on findings and collectively influences conclusion assessment. Even a single limitation can compromise the study’s implementation and adversely influence research parameters, resulting in diminished value of the findings, outcomes, and conclusions. This becomes more problematic as the number of limitations and their components increase. Any limitation influences a research paper. It is unknown how much and to what extent any limitation affects other limitations, but it does create a cascading domino effect of ever-increasing interactions that compromise findings and conclusions. Considering “research” as a system, it has sensitivity and initial conditions (methodology, data collection, analysis, etc.). The slightest alteration of a study due to limitations can profoundly impact all aspects of the study. The presence and influence of limitations introduce a range of unpredictable influences on findings, results, and conclusions.

Researchers and readers need to pay attention to and discount the effects limitations have on the validity of findings. Richard Feynman said in “Cargo cult science” “the first principle is that you must not fool yourself and you are the easiest person to fool.” 73 We strongly believe our own nonsense or wrong-headed reasoning. Buddhist philosophers say we are attached to our ignorance. Researchers are not critical enough about how they fool themselves regarding their findings with known limitations and then pass them on to readers. The competence of findings with known limitations results in suspect conclusions.

Authors should not ask for dismissal, disregard, or indulgence of their limitations. They should be thoughtful and reflective about the implications and uncertainty the limitations create 67 ; their uncertainties, blind spots, and impact on the research’s relevance. A meaningful presentation of study limitations should describe the limitation, explain its effect, provide possible alternative approaches, and describe steps taken to mitigate the limitation. This was largely absent from the articles reviewed.

Authors use synonyms and phrases describing limitations that hide, deflect, downplay, and divert attention from them, i.e., some drawbacks of the study are …, weaknesses of the study are…, shortcomings are…, and disadvantages of the study are…. They then say their finding(s) lack(s) generalizability, meaning the findings only apply to the study participants or that care, sometimes extreme, must be taken in interpreting the results. Which limitation components are they referring to? Are the authors aware of the extent of their limitations, or are they using convenient phrases to highlight the existence of limitations without detailing their defects?

Limitations negatively weigh on both data and conclusions yet no literature exists to provide a quantifiable measure of this effect. The only acknowledgment is that limitations affect research data and conclusions. The adverse effects of limitations are both specific and contextual to each research article and is part of the parameters that affect research. All the limitations are expressed in words, excuses, and a litany of mea culpas asking for forgiveness and without explaining the extent or magnitude of their impact. It is left to the writer and reader to figure out. It is not known what value writers put on their limitations in the 298 articles reviewed from JSLS and Surg Endosc . Listing limitations without comment and effect on the findings and conclusions is a compromising red flag. Therefore, a limitation scoring method was developed and is proposed to assess the level of suspicion generated by the number of limitations.

It is doubtful that a medical research article is so well designed and executed that there are no limitations. This is doubtful since there are unknown unknowns. This study showed that authors need to acknowledge all the limitations when they are known. They acknowledge the ones they know but do not consider other possibilities. There are the known known limitations; the ones the author(s) are aware of and can be measured, some explained, most not. The known unknowns: limitations authors are aware of but cannot explain or quantify. The unknown unknown limitations: the ones authors are not aware of and have unknown influence(s), i.e., the things they do not know they do not know. These are blind spots (not knowing what they do not know or black swan events). And the unknown knowns; the limitations authors may be aware of but have not disclosed, thoroughly reported, understood, or addressed. They are unexpected and not considered. See Table 2 . 74

Limitations of Known and Unknowns as They Apply to Limitations

It is possible that authors did not identify, want to identify, or acknowledge potential limitations or were unaware of what limitations existed. Cumulative complexity is the result of the presence of multiple limitations because of the accumulation and interaction of limitations and their components. Just mentioning a limitation category and not the specific parts that are the limitation(s) is not enough. Authors telling readers of their known research limitations is a caution to discount the findings and conclusions. At what point does the caution for each limitation, its ramifications, and consequences become a warning? When does the piling up of mistakes, bad and missing data, biases, small sample size, lack of generalizability, confounding factors, etc., reach a point when the findings become s uninterpretable and meaningless? “Caution” indicates a level of potential hazard; a warning is more dire and consequential. Authors use the word “caution” not “warning” to describe their conclusions. There is a point when the number of limitations and their cumulative effects surpasses the point where a caution statement is no longer applicable, and a warning statement is required. This is the reason for establishing a limitations risk score.

Limitations put medical research articles at risk. The accumulation of limitations (variables having additional limitation components) are gaps and flaws diluting the probability of validity. There is currently no assessment method for evaluating the effect(s) of limitations on research outcomes other than awareness that there is an effect. Authors make statements warning that their results may not be reliable or generalizable, and need more research and larger numbers. Just because the weight effect of any given limitation is not known, explained, or how it discounts findings does not negate a causation effect on data, its analysis, and conclusions. Limitation variables and the ramifications of their effects have consequences. The relationship is not zero effect and accumulates with each added limitation.

As a result of this research, a limitation index score (LIS) system and assessment tool were developed. This limitation risk assessment tool gives a scores assessment of the relative validity of conclusions in a medical article having limitations. The adoption of the LIS scoring assessment tool for authors, researchers, editors, reviewers, and readers is a step toward understanding the effects of limitations and their causal relationships to findings and conclusions. The objective is cleaner, tighter methodologies, and better data assessment, to achieve more reliable findings. Adjustments to research conclusions in the presence of limitations are necessary. The degree of modification depends on context. The cumulative effect of this burden must be acknowledged by a tangible reduction and questioning of the legitimacy of statements made under these circumstances. The description calculating the LIS score is detailed in Appendix 1 .

A limitation word or phrase is not one limitation; it is a group of limitations under the heading of that word or phrase having many additional possible components just as an individual named influence. For instance, when an admission of selection bias is noted, the authors do not explain if it was an exclusion criterion, self-selection, nonresponsiveness, lost to follow-up, recruitment error, how it affects external validity, lack of randomization, etc., or any of the least 263 types of known biases causing systematic distortions of the truth whether unintentional or wanton. 40 , 76 Which forms of selection bias are they identifying? 63 Limitations have branches that introduce additional limitations influencing the study’s ability to reach a useful conclusion. Authors rarely tell you the effect consequences and extent limitations have on their study, findings, and conclusions.

This is a sample of limitations and a few of their component variables under the rubric of a single word or phrase. See Table 3 .

A Limitation Word or Phrase is a Limitation Having Additional Components That Are Additional Limitations. When an Author Uses the Limitation Composite Word or Phrase, They Leave out Which One of Its Components is Contributory to the Research Limitations. Each Limitation Interacts with Other Limitations, Creating a Cluster of Cross Complexities of Data, Findings, and Conclusions That Are Tainted and Negatively Affect Findings and Conclusions

Limitations rarely occur alone. If you see one there are many you do not see or appreciate. Limitation s components interact with their own and other limitations, leading to complex connections interacting and discounting the reliability of findings. By how much is context dependent: but it is not zero. Limitations are variables influencing outcomes. As the number of limitations increases, the reliability of the conclusions decreases. How many variables (limitations) does it take to nullify the claims of the findings? The weight and influence of each limitation, its aggregate components, and interconnectedness have an unknown magnitude and effect. The result is a disorderly concoction of hearsay explanations. Table 4 is an example of just two single explanation limitations and some of their components illustrating the complex compounding of their effects on each other.

An Example of Interactions between Only Two Limitations and Some of Their Components Causes 16 Interactions

The novelty of this paper on limitations in medical science is not the identification of research article limitations or their number or frequency; it is the recognition of the multiplier effect(s) limitations and the influence they have on diminishing any conclusion(s) the paper makes. It is possible that limitations contribute to the inability of studies to replicate and why so many are one-time occurrences. Therefore, the generalizability statement that should be given to all readers is BEWARE THERE IS A REDUCTION EFFECT ON THE CONCLUSIONS IN THIS ARTICLE BECAUSE OF ITS LIMITATIONS.

Journals accept studies done with too many limitations, creating forking path situations resulting in an enormous number of possible associations of individual data points as multiple comparisons. 79 The result is confusion, a muddled mess caused by interactions of limitations undermining the ability to make valid inferences. Authors know and acknowledge but rarely explain them or their influence. They also use incomplete and biased databases, biased methods, small sample sizes, and not eliminating confounders, etc., but persist in doing research with these circumstances. Why is that? Is it because when limitations are acknowledged, authors feel justified in their conclusions? It wasn’t my poor research design; it was the limitation(s). How do peer reviewers score and analyze these papers without a method to discount the findings and conclusions in the presence of limitations? What are the calculus editors use to justify papers with multiple limitations, reaching compromised or spurious conclusions? How much caution or warning should a journal say must be taken in interpreting article results? How much? Which results? When? Under what circumstance(s)?

Since a critical component of research is its limitations, the quality and rigor of research are largely defined by, 75 these constraints making it imperative that limitations be exposed and explained. All studies have limitations admitted to or not, and these limitations influence outcomes and conclusions. Unfortunately, they are given insufficient attention, accompanied by feeble excuses, but they all matter. The degrees of freedom of each limitation influence every other limitation, magnifying their ramifications and confusion. Limitations of a scientific article must put the findings in context so the reader can judge the validity and strength of the conclusions. While authors acknowledge the limitations of their study, they influence its legitimacy.

Not only are limitations not properly acknowledged in the scientific literature, 8 but their implications, magnitude, and how they affect a conclusion are not explained or appreciated. Authors work at claiming their work and methods “overcome,” “avoid,” or “circumvent” limitations. Limitations are explained away as “Failure to prove a difference does not prove lack of a difference.” 60 Sample size, bias, confounders, bad data, etc. are not what they seem and do not sully the results. The implication is “trust me.” But that’s not science. Limitations create cognitive distortions and framing (misperception of reality) for the authors and readers. Data in studies with limitations is data having limitations. It was real but tainted.

Limitations are not a trivial aspect of research. It is a tangible something, positive or negative, put into a data set to be analyzed and used to reach a conclusion. How did these extra somethings, known unknowns, not knowns, and unknown knowns, affect the validity of the data set and conclusions? Research presented with the vagaries of explicit limitations is intensified by additional limitations and their component effects on top of the first limitation s , quickly diluting any conclusion making its dependability questionable.

This study’s analysis of limitations in medical articles averaged 3.9% per article for JSLS and 7.4% for Surg Endosc . Authors admit to some and are aware of limitations, but not all of them and discount or leave out others. Limitations were often presented with misleading and hedging language. Authors do not give weight or suggest the percent discount limitations have on the reliance of conclusion(s). Since limitations influence findings, reliability, generalizability, and validity without knowing the magnitude of each and their context, the best that can be said about the conclusions is that they are specific to the study described, context-driven, and suspect.

Limitations mean something is missing, added, incorrect, unseen, unaware of, fabricated, or unknown; circumstances that confuse, confound, and compromise findings and information to the extent that a notice is necessary. All medical articles should have this statement, “Any conclusion drawn from this medical study should be interpreted considering its limitations. Readers should exercise caution, use critical judgement, and consult other sources before accepting these findings. Findings may not be generalizable regardless of sample size, composition, representative data points, and subject groups. Methodologic, analytic, and data collection may have introduced biases or limitations that can affect the accuracy of the results. Controlling for confounding variables, known and unknown, may have influenced the data and/or observations. The accuracy and completeness of the data used to draw a conclusion may not be reliable. The study was specific to time, place, persons, and prevailing circumstances. The weight of each of these factors is unknown to us. Their effect may be limited or compounded and diminish the validity of the proposed conclusions.”

This study and findings are limited and constrained by the limitations of the articles reviewed. They have known and unknown limitations not accounted for, missing data, small sample size, incongruous populations, internal and external validity concerns, confounders, and more. See Tables 2 and ​ and 3 . 3 . Some of these are correctible by the author’s awareness of the consequences of limitations, making plans to address them in the methodology phase of hypothesis assessment and performance of the research to diminish their effects.

Limitations in research articles are expected, but they can be reduced in their effect so that conclusions are closer to being valid. Limitations introduce elements of ignorance and suspicion. They need to be explained so their influence on the believability of the study and its conclusions is closer to meeting construct, content, face, and criterion validity. As the number of limitations increases, common sense, skepticism, study component acceptability, and understanding the ramifications of each limitation are necessary to accept, discount, or reject the author’s findings. As the number of hedging and weasel words used to explain conclusion(s) increases, believability decreases, and raises suspicion regarding claims. Establishing a systematic limitation scoring index limitations for authors, editors, reviewers, and readers and recognizing their cumulative effects will result in a clearer understanding of research content and legitimacy.

How to calculate the Limitation Index Score (LIS). See Tables 5 – 5 . Each limitation admitted to by authors in the article equals (=) one (1) point. Limitations may be generally stated by the author as a broad category, but can have multiple components, such as a retrospective study with these limitation components: 1. data or recall not accurate, 2. data missing, 3. selection bias not controlled, 4. confounders not controlled, 5. no randomization, 6. no blinding, 7. difficult to establish cause and effect, and 8. cannot draw a conclusion of causation. For each component, no matter how many are not explained and corrected, add an additional one (1) point to the score. See Table 2 .

The Limitation Scoring Index is a Numeric Limitation Risk Assessment Score to Rank Risk Categories and Discounting Probability of Validity and Conclusions. The More Limitations in a Study, the Greater the Risk of Unreliable Findings and Conclusions

Limitations May Be Generally Stated by the Author but Have Multiple Components, Such as a Retrospective Study Having Disadvantage Components of 1. Data or Recall Not Accurate, 2. Data Missing, 3. Selection Bias Not Controlled, 4. Confounders Not Controlled, 5. No Randomization, 6. No Blinding, 7 Difficult to Establish Cause and Effect, 8. Results Are Hypothesis Generating, and 9. Cannot Draw a Conclusion of Causation. For Each Component, Not Explained and Corrected, Add an Additional One (1) Point Is Added to the Score. Extra Blanks Are for Additional Limitations

An Automatic 2 Points is Added for Meta-Analysis Studies Since They Have All the Retrospective Detrimental Components. 44 Data from Insurance, State, National, Medicare, and Medicaid, Because of Incorrect Coding, Over Reporting, and Under-Reporting, Etc. Each Component of the Limitation Adds One Additional Point. For Surveys and Questionnaires Add One Additional Point for Each Bias. Extra Blanks Are for Additional Limitations

Automatic Five (5) Points for Manufacturer and User Facility Device Experience (MAUDE) Database Articles. The FDA Access Data Site Says Submissions Can Be “Incomplete, Inaccurate, Untimely, Unverified, or Biased” and “the Incidence or Prevalence of an Event Cannot Be Determined from This Reporting System Alone Due to Under-Reporting of Events, Inaccuracies in Reports, Lack of Verification That the Device Caused the Reported Event, and Lack of Information” and “DR Data Alone Cannot Be Used to Establish Rates of Events, Evaluate a Change in Event Rates over Time or Compare Event Rates between Devices. The Number of Reports Cannot Be Interpreted or Used in Isolation to Reach Conclusions” 80

Total Limitation Index Score

Each limitation not admitted to = two (2) points. A meta-analysis study gets an automatic 2 points since they are retrospective and have detrimental components that should be added to the 2 points. Data from insurance, state, national, Medicare, and Medicaid, because of incorrect coding, over-reporting, and underreporting, etc., score 2 points, and each component adds one additional point. Surveys and questionnaires get 2 points, and add one additional point for each bias. See Table 3 .

Manufacturer and User Facility Device Experience (MAUDE) database articles receive an automatic five (5) points. The FDA access data site says, submissions can be “incomplete, inaccurate, untimely, unverified, or biased” and “the incidence or prevalence of an event cannot be determined from this reporting system alone due to underreporting of events, inaccuracies in reports, lack of verification that the device caused the reported event, and lack of information” and “MDR data alone cannot be used to establish rates of events, evaluate a change in event rates over time or compare event rates between devices. The number of reports cannot be interpreted or used in isolation to reach conclusions.” 80 See Table 4 . Add one additional point for each additional limitation noted in the article.

Add one additional point for each additional limitation and one point for each of its components. Extra blanks are for additional

limitations and their component scores.

Funding sources: none.

Disclosure: none.

Conflict of interests: none.

Acknowledgments: Author would like to thank Lynda Davis for her help with data collection.

References:

All references have been archived at https://archive.org/web/

Educational resources and simple solutions for your research journey

How to Present the Limitations of a Study in Research?

The limitations of the study convey to the reader how and under which conditions your study results will be evaluated. Scientific research involves investigating research topics, both known and unknown, which inherently includes an element of risk. The risk could arise due to human errors, barriers to data gathering, limited availability of resources, and researcher bias. Researchers are encouraged to discuss the limitations of their research to enhance the process of research, as well as to allow readers to gain an understanding of the study’s framework and value.

Limitations of the research are the constraints placed on the ability to generalize from the results and to further describe applications to practice. It is related to the utility value of the findings based on how you initially chose to design the study, the method used to establish internal and external validity, or the result of unanticipated challenges that emerged during the study. Knowing about these limitations and their impact can explain how the limitations of your study can affect the conclusions and thoughts drawn from your research. 1

Table of Contents

What are the limitations of a study

Researchers are probably cautious to acknowledge what the limitations of the research can be for fear of undermining the validity of the research findings. No research can be faultless or cover all possible conditions. These limitations of your research appear probably due to constraints on methodology or research design and influence the interpretation of your research’s ultimate findings. 2 These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity internally and externally. But such limitations of the study can impact the whole study or research paper. However, most researchers prefer not to discuss the different types of limitations in research for fear of decreasing the value of their paper amongst the reviewers or readers.

Importance of limitations of a study

Writing the limitations of the research papers is often assumed to require lots of effort. However, identifying the limitations of the study can help structure the research better. Therefore, do not underestimate the importance of research study limitations. 3

• Opportunity to make suggestions for further research. Suggestions for future research and avenues for further exploration can be developed based on the limitations of the study.
• Opportunity to demonstrate critical thinking. A key objective of the research process is to discover new knowledge while questioning existing assumptions and exploring what is new in the particular field. Describing the limitation of the research shows that you have critically thought about the research problem, reviewed relevant literature, and correctly assessed the methods chosen for studying the problem.
• Demonstrate Subjective learning process. Writing limitations of the research helps to critically evaluate the impact of the said limitations, assess the strength of the research, and consider alternative explanations or interpretations. Subjective evaluation contributes to a more complex and comprehensive knowledge of the issue under study.

Why should I include limitations of research in my paper

All studies have limitations to some extent. Including limitations of the study in your paper demonstrates the researchers’ comprehensive and holistic understanding of the research process and topic. The major advantages are the following:

• Understand the study conditions and challenges encountered . It establishes a complete and potentially logical depiction of the research. The boundaries of the study can be established, and realistic expectations for the findings can be set. They can also help to clarify what the study is not intended to address.
• Improve the quality and validity of the research findings. Mentioning limitations of the research creates opportunities for the original author and other researchers to undertake future studies to improve the research outcomes.
• Transparency and accountability. Including limitations of the research helps maintain mutual integrity and promote further progress in similar studies.
• Identify potential bias sources.  Identifying the limitations of the study can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.

Where do I need to add the limitations of the study in my paper

The limitations of your research can be stated at the beginning of the discussion section, which allows the reader to comprehend the limitations of the study prior to reading the rest of your findings or at the end of the discussion section as an acknowledgment of the need for further research.

Types of limitations in research

There are different types of limitations in research that researchers may encounter. These are listed below:

• Research Design Limitations : Restrictions on your research or available procedures may affect the research outputs. If the research goals and objectives are too broad, explain how they should be narrowed down to enhance the focus of your study. If there was a selection bias in your sample, explain how this may affect the generalizability of your findings. This can help readers understand the limitations of the study in terms of their impact on the overall validity of your research.
• Impact Limitations : Your study might be limited by a strong regional-, national-, or species-based impact or population- or experimental-specific impact. These inherent limitations on impact affect the extendibility and generalizability of the findings.
• Data or statistical limitations : Data or statistical limitations in research are extremely common in experimental (such as medicine, physics, and chemistry) or field-based (such as ecology and qualitative clinical research) studies. Sometimes, it is either extremely difficult to acquire sufficient data or gain access to the data. These limitations of the research might also be the result of your study’s design and might result in an incomplete conclusion to your research.

Limitations of study examples

All possible limitations of the study cannot be included in the discussion section of the research paper or dissertation. It will vary greatly depending on the type and nature of the study. These include types of research limitations that are related to methodology and the research process and that of the researcher as well that you need to describe and discuss how they possibly impacted your results.

Common methodological limitations of the study

Limitations of research due to methodological problems are addressed by identifying the potential problem and suggesting ways in which this should have been addressed. Some potential methodological limitations of the study are as follows. 1

• Sample size: The sample size 4 is dictated by the type of research problem investigated. If the sample size is too small, finding a significant relationship from the data will be difficult, as statistical tests require a large sample size to ensure a representative population distribution and generalize the study findings.
• Lack of available/reliable data: A lack of available/reliable data will limit the scope of your analysis and the size of your sample or present obstacles in finding a trend or meaningful relationship. So, when writing about the limitations of the study, give convincing reasons why you feel data is absent or untrustworthy and highlight the necessity for a future study focused on developing a new data-gathering strategy.
• Lack of prior research studies: Citing prior research studies is required to help understand the research problem being investigated. If there is little or no prior research, an exploratory rather than an explanatory research design will be required. Also, discovering the limitations of the study presents an opportunity to identify gaps in the literature and describe the need for additional study.
• Measure used to collect the data: Sometimes, the data gathered will be insufficient to conduct a thorough analysis of the results. A limitation of the study example, for instance, is identifying in retrospect that a specific question could have helped address a particular issue that emerged during data analysis. You can acknowledge the limitation of the research by stating the need to revise the specific method for gathering data in the future.
• Self-reported data: Self-reported data cannot be independently verified and can contain several potential bias sources, such as selective memory, attribution, and exaggeration. These biases become apparent if they are incongruent with data from other sources.

General limitations of researchers

Limitations related to the researcher can also influence the study outcomes. These should be addressed, and related remedies should be proposed.

• Limited access to data : If your study requires access to people, organizations, data, or documents whose access is denied or limited, the reasons need to be described. An additional explanation stating why this limitation of research did not prevent you from following through on your study is also needed.
• Time constraints : Researchers might also face challenges in meeting research deadlines due to a lack of timely participant availability or funds, among others. The impacts of time constraints must be acknowledged by mentioning the need for a future study addressing this research problem.
• Conflicts due to biased views and personal issues : Differences in culture or personal views can contribute to researcher bias, as they focus only on the results and data that support their main arguments. To avoid this, pay attention to the problem statement and data gathering.

Steps for structuring the limitations section

Limitations are an inherent part of any research study. Issues may vary, ranging from sampling and literature review to methodology and bias. However, there is a structure for identifying these elements, discussing them, and offering insight or alternatives on how the limitations of the study can be mitigated. This enhances the process of the research and helps readers gain a comprehensive understanding of a study’s conditions.

• Identify the research constraints : Identify those limitations having the greatest impact on the quality of the research findings and your ability to effectively answer your research questions and/or hypotheses. These include sample size, selection bias, measurement error, or other issues affecting the validity and reliability of your research.
• Describe their impact on your research : Reflect on the nature of the identified limitations and justify the choices made during the research to identify the impact of the study’s limitations on the research outcomes. Explanations can be offered if needed, but without being defensive or exaggerating them. Provide context for the limitations of your research to understand them in a broader context. Any specific limitations due to real-world considerations need to be pointed out critically rather than justifying them as done by some other author group or groups.
• Mention the opportunity for future investigations : Suggest ways to overcome the limitations of the present study through future research. This can help readers understand how the research fits into the broader context and offer a roadmap for future studies.

Frequently Asked Questions

• Should I mention all the limitations of my study in the research report?

Restrict limitations to what is pertinent to the research question under investigation. The specific limitations you include will depend on the nature of the study, the research question investigated, and the data collected.

• Can the limitations of a study affect its credibility?

Stating the limitations of the research is considered favorable by editors and peer reviewers. Connecting your study’s limitations with future possible research can help increase the focus of unanswered questions in this area. In addition, admitting limitations openly and validating that they do not affect the main findings of the study increases the credibility of your study. However, if you determine that your study is seriously flawed, explain ways to successfully overcome such flaws in a future study. For example, if your study fails to acquire critical data, consider reframing the research question as an exploratory study to lay the groundwork for more complete research in the future.

• How can I mitigate the limitations of my study?

Strategies to minimize limitations of the research should focus on convincing reviewers and readers that the limitations do not affect the conclusions of the study by showing that the methods are appropriate and that the logic is sound. Here are some steps to follow to achieve this:

• Use data that are valid.
• Use methods that are appropriate and sound logic to draw inferences.
• Use adequate statistical methods for drawing inferences from the data that studies with similar limitations have been published before.

Admit limitations openly and, at the same time, show how they do not affect the main conclusions of the study.

• Can the limitations of a study impact its publication chances?

Limitations in your research can arise owing to restrictions in methodology or research design. Although this could impact your chances of publishing your research paper, it is critical to explain your study’s limitations to your intended audience. For example, it can explain how your study constraints may impact the results and views generated from your investigation. It also shows that you have researched the flaws of your study and have a thorough understanding of the subject.

• How can limitations in research be used for future studies?

The limitations of a study give you an opportunity to offer suggestions for further research. Your study’s limitations, including problems experienced during the study and the additional study perspectives developed, are a great opportunity to take on a new challenge and help advance knowledge in a particular field.

References:

• Brutus, S., Aguinis, H., & Wassmer, U. (2013). Self-reported limitations and future directions in scholarly reports: Analysis and recommendations.  Journal of Management ,  39 (1), 48-75.
• Ioannidis, J. P. (2007). Limitations are not properly acknowledged in the scientific literature.  Journal of Clinical Epidemiology ,  60 (4), 324-329.
• Price, J. H., & Murnan, J. (2004). Research limitations and the necessity of reporting them.  American Journal of Health Education ,  35 (2), 66.
• Boddy, C. R. (2016). Sample size for qualitative research.  Qualitative Market Research: An International Journal ,  19 (4), 426-432.

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Research Limitations

It is for sure that your research will have some limitations and it is normal. However, it is critically important for you to be striving to minimize the range of scope of limitations throughout the research process.  Also, you need to provide the acknowledgement of your research limitations in conclusions chapter honestly.

It is always better to identify and acknowledge shortcomings of your work, rather than to leave them pointed out to your by your dissertation assessor. While discussing your research limitations, don’t just provide the list and description of shortcomings of your work. It is also important for you to explain how these limitations have impacted your research findings.

Your research may have multiple limitations, but you need to discuss only those limitations that directly relate to your research problems. For example, if conducting a meta-analysis of the secondary data has not been stated as your research objective, no need to mention it as your research limitation.

Research limitations in a typical dissertation may relate to the following points:

1. Formulation of research aims and objectives . You might have formulated research aims and objectives too broadly. You can specify in which ways the formulation of research aims and objectives could be narrowed so that the level of focus of the study could be increased.

2. Implementation of data collection method . Because you do not have an extensive experience in primary data collection (otherwise you would not be reading this book), there is a great chance that the nature of implementation of data collection method is flawed.

3. Sample size. Sample size depends on the nature of the research problem. If sample size is too small, statistical tests would not be able to identify significant relationships within data set. You can state that basing your study in larger sample size could have generated more accurate results. The importance of sample size is greater in quantitative studies compared to qualitative studies.

4. Lack of previous studies in the research area . Literature review is an important part of any research, because it helps to identify the scope of works that have been done so far in research area. Literature review findings are used as the foundation for the researcher to be built upon to achieve her research objectives.

However, there may be little, if any, prior research on your topic if you have focused on the most contemporary and evolving research problem or too narrow research problem. For example, if you have chosen to explore the role of Bitcoins as the future currency, you may not be able to find tons of scholarly paper addressing the research problem, because Bitcoins are only a recent phenomenon.

5. Scope of discussions . You can include this point as a limitation of your research regardless of the choice of the research area. Because (most likely) you don’t have many years of experience of conducing researches and producing academic papers of such a large size individually, the scope and depth of discussions in your paper is compromised in many levels compared to the works of experienced scholars.

You can discuss certain points from your research limitations as the suggestion for further research at conclusions chapter of your dissertation.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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Limitations of the Study – How to Write & Examples

What are the limitations of a study?

The limitations of a study are the elements of methodology or study design that impact the interpretation of your research results. The limitations essentially detail any flaws or shortcomings in your study. Study limitations can exist due to constraints on research design, methodology, materials, etc., and these factors may impact the findings of your study. However, researchers are often reluctant to discuss the limitations of their study in their papers, feeling that bringing up limitations may undermine its research value in the eyes of readers and reviewers.

In spite of the impact it might have (and perhaps because of it) you should clearly acknowledge any limitations in your research paper in order to show readers—whether journal editors, other researchers, or the general public—that you are aware of these limitations and to explain how they affect the conclusions that can be drawn from the research.

In this article, we provide some guidelines for writing about research limitations, show examples of some frequently seen study limitations, and recommend techniques for presenting this information. And after you have finished drafting and have received manuscript editing for your work, you still might want to follow this up with academic editing before submitting your work to your target journal.

Why do I need to include limitations of research in my paper?

Although limitations address the potential weaknesses of a study, writing about them toward the end of your paper actually strengthens your study by identifying any problems before other researchers or reviewers find them.

Furthermore, pointing out study limitations shows that you’ve considered the impact of research weakness thoroughly and have an in-depth understanding of your research topic. Since all studies face limitations, being honest and detailing these limitations will impress researchers and reviewers more than ignoring them.

Where should I put the limitations of the study in my paper?

Some limitations might be evident to researchers before the start of the study, while others might become clear while you are conducting the research. Whether these limitations are anticipated or not, and whether they are due to research design or to methodology, they should be clearly identified and discussed in the discussion section —the final section of your paper. Most journals now require you to include a discussion of potential limitations of your work, and many journals now ask you to place this “limitations section” at the very end of your article. 

Some journals ask you to also discuss the strengths of your work in this section, and some allow you to freely choose where to include that information in your discussion section—make sure to always check the author instructions of your target journal before you finalize a manuscript and submit it for peer review .

Limitations of the Study Examples

There are several reasons why limitations of research might exist. The two main categories of limitations are those that result from the methodology and those that result from issues with the researcher(s).

Common Methodological Limitations of Studies

Limitations of research due to methodological problems can be addressed by clearly and directly identifying the potential problem and suggesting ways in which this could have been addressed—and SHOULD be addressed in future studies. The following are some major potential methodological issues that can impact the conclusions researchers can draw from the research.

Issues with research samples and selection

Sampling errors occur when a probability sampling method is used to select a sample, but that sample does not reflect the general population or appropriate population concerned. This results in limitations of your study known as “sample bias” or “selection bias.”

For example, if you conducted a survey to obtain your research results, your samples (participants) were asked to respond to the survey questions. However, you might have had limited ability to gain access to the appropriate type or geographic scope of participants. In this case, the people who responded to your survey questions may not truly be a random sample.

Insufficient sample size for statistical measurements

When conducting a study, it is important to have a sufficient sample size in order to draw valid conclusions. The larger the sample, the more precise your results will be. If your sample size is too small, it will be difficult to identify significant relationships in the data.

Normally, statistical tests require a larger sample size to ensure that the sample is considered representative of a population and that the statistical result can be generalized to a larger population. It is a good idea to understand how to choose an appropriate sample size before you conduct your research by using scientific calculation tools—in fact, many journals now require such estimation to be included in every manuscript that is sent out for review.

Lack of previous research studies on the topic

Citing and referencing prior research studies constitutes the basis of the literature review for your thesis or study, and these prior studies provide the theoretical foundations for the research question you are investigating. However, depending on the scope of your research topic, prior research studies that are relevant to your thesis might be limited.

When there is very little or no prior research on a specific topic, you may need to develop an entirely new research typology. In this case, discovering a limitation can be considered an important opportunity to identify literature gaps and to present the need for further development in the area of study.

Methods/instruments/techniques used to collect the data

After you complete your analysis of the research findings (in the discussion section), you might realize that the manner in which you have collected the data or the ways in which you have measured variables has limited your ability to conduct a thorough analysis of the results.

For example, you might realize that you should have addressed your survey questions from another viable perspective, or that you were not able to include an important question in the survey. In these cases, you should acknowledge the deficiency or deficiencies by stating a need for future researchers to revise their specific methods for collecting data that includes these missing elements.

Common Limitations of the Researcher(s)

Study limitations that arise from situations relating to the researcher or researchers (whether the direct fault of the individuals or not) should also be addressed and dealt with, and remedies to decrease these limitations—both hypothetically in your study, and practically in future studies—should be proposed.

Limited access to data

If your research involved surveying certain people or organizations, you might have faced the problem of having limited access to these respondents. Due to this limited access, you might need to redesign or restructure your research in a different way. In this case, explain the reasons for limited access and be sure that your finding is still reliable and valid despite this limitation.

Time constraints

Just as students have deadlines to turn in their class papers, academic researchers might also have to meet deadlines for submitting a manuscript to a journal or face other time constraints related to their research (e.g., participants are only available during a certain period; funding runs out; collaborators move to a new institution). The time available to study a research problem and to measure change over time might be constrained by such practical issues. If time constraints negatively impacted your study in any way, acknowledge this impact by mentioning a need for a future study (e.g., a longitudinal study) to answer this research problem.

Conflicts arising from cultural bias and other personal issues

Researchers might hold biased views due to their cultural backgrounds or perspectives of certain phenomena, and this can affect a study’s legitimacy. Also, it is possible that researchers will have biases toward data and results that only support their hypotheses or arguments. In order to avoid these problems, the author(s) of a study should examine whether the way the research problem was stated and the data-gathering process was carried out appropriately.

Steps for Organizing Your Study Limitations Section

When you discuss the limitations of your study, don’t simply list and describe your limitations—explain how these limitations have influenced your research findings. There might be multiple limitations in your study, but you only need to point out and explain those that directly relate to and impact how you address your research questions.

We suggest that you divide your limitations section into three steps: (1) identify the study limitations; (2) explain how they impact your study in detail; and (3) propose a direction for future studies and present alternatives. By following this sequence when discussing your study’s limitations, you will be able to clearly demonstrate your study’s weakness without undermining the quality and integrity of your research.

Step 1. Identify the limitation(s) of the study

• This part should comprise around 10%-20% of your discussion of study limitations.

The first step is to identify the particular limitation(s) that affected your study. There are many possible limitations of research that can affect your study, but you don’t need to write a long review of all possible study limitations. A 200-500 word critique is an appropriate length for a research limitations section. In the beginning of this section, identify what limitations your study has faced and how important these limitations are.

You only need to identify limitations that had the greatest potential impact on: (1) the quality of your findings, and (2) your ability to answer your research question.

Step 2. Explain these study limitations in detail

• This part should comprise around 60-70% of your discussion of limitations.

After identifying your research limitations, it’s time to explain the nature of the limitations and how they potentially impacted your study. For example, when you conduct quantitative research, a lack of probability sampling is an important issue that you should mention. On the other hand, when you conduct qualitative research, the inability to generalize the research findings could be an issue that deserves mention.

Explain the role these limitations played on the results and implications of the research and justify the choice you made in using this “limiting” methodology or other action in your research. Also, make sure that these limitations didn’t undermine the quality of your dissertation .

Step 3. Propose a direction for future studies and present alternatives (optional)

• This part should comprise around 10-20% of your discussion of limitations.

After acknowledging the limitations of the research, you need to discuss some possible ways to overcome these limitations in future studies. One way to do this is to present alternative methodologies and ways to avoid issues with, or “fill in the gaps of” the limitations of this study you have presented.  Discuss both the pros and cons of these alternatives and clearly explain why researchers should choose these approaches.

Make sure you are current on approaches used by prior studies and the impacts they have had on their findings. Cite review articles or scientific bodies that have recommended these approaches and why. This might be evidence in support of the approach you chose, or it might be the reason you consider your choices to be included as limitations. This process can act as a justification for your approach and a defense of your decision to take it while acknowledging the feasibility of other approaches.

P hrases and Tips for Introducing Your Study Limitations in the Discussion Section

The following phrases are frequently used to introduce the limitations of the study:

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”
• “As with the majority of studies, the design of the current study is subject to limitations.”
• “There are two major limitations in this study that could be addressed in future research. First, the study focused on …. Second ….”

For more articles on research writing and the journal submissions and publication process, visit Wordvice’s Academic Resources page.

And be sure to receive professional English editing and proofreading services , including paper editing services , for your journal manuscript before submitting it to journal editors.

Wordvice Resources

Proofreading & Editing Guide

Writing the Results Section for a Research Paper

How to Write a Literature Review

Research Writing Tips: How to Draft a Powerful Discussion Section

How to Captivate Journal Readers with a Strong Introduction

Tips That Will Make Your Abstract a Success!

APA In-Text Citation Guide for Research Writing

Additional Resources

• Diving Deeper into Limitations and Delimitations (PhD student)
• Organizing Your Social Sciences Research Paper: Limitations of the Study (USC Library)
• Research Limitations (Research Methodology)
• How to Present Limitations and Alternatives (UMASS)

Article References

Pearson-Stuttard, J., Kypridemos, C., Collins, B., Mozaffarian, D., Huang, Y., Bandosz, P.,…Micha, R. (2018). Estimating the health and economic effects of the proposed US Food and Drug Administration voluntary sodium reformulation: Microsimulation cost-effectiveness analysis. PLOS. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002551

Xu, W.L, Pedersen, N.L., Keller, L., Kalpouzos, G., Wang, H.X., Graff, C,. Fratiglioni, L. (2015). HHEX_23 AA Genotype Exacerbates Effect of Diabetes on Dementia and Alzheimer Disease: A Population-Based Longitudinal Study. PLOS. Retrieved from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001853

Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

During the process of writing your thesis or dissertation, you might suddenly realize that your research has inherent flaws. Don’t worry! Virtually all projects contain restrictions to your research. However, being able to recognize and accurately describe these problems is the difference between a true researcher and a grade-school kid with a science-fair project. Concerns with truthful responding, access to participants, and survey instruments are just a few of examples of restrictions on your research. In the following sections, the differences among delimitations, limitations, and assumptions of a dissertation will be clarified.

Delimitations

Delimitations are the definitions you set as the boundaries of your own thesis or dissertation, so delimitations are in your control. Delimitations are set so that your goals do not become impossibly large to complete. Examples of delimitations include objectives, research questions, variables, theoretical objectives that you have adopted, and populations chosen as targets to study. When you are stating your delimitations, clearly inform readers why you chose this course of study. The answer might simply be that you were curious about the topic and/or wanted to improve standards of a professional field by revealing certain findings. In any case, you should clearly list the other options available and the reasons why you did not choose these options immediately after you list your delimitations. You might have avoided these options for reasons of practicality, interest, or relativity to the study at hand. For example, you might have only studied Hispanic mothers because they have the highest rate of obese babies. Delimitations are often strongly related to your theory and research questions. If you were researching whether there are different parenting styles between unmarried Asian, Caucasian, African American, and Hispanic women, then a delimitation of your study would be the inclusion of only participants with those demographics and the exclusion of participants from other demographics such as men, married women, and all other ethnicities of single women (inclusion and exclusion criteria). A further delimitation might be that you only included closed-ended Likert scale responses in the survey, rather than including additional open-ended responses, which might make some people more willing to take and complete your survey. Remember that delimitations are not good or bad. They are simply a detailed description of the scope of interest for your study as it relates to the research design. Don’t forget to describe the philosophical framework you used throughout your study, which also delimits your study.

Limitations

Limitations of a dissertation are potential weaknesses in your study that are mostly out of your control, given limited funding, choice of research design, statistical model constraints, or other factors. In addition, a limitation is a restriction on your study that cannot be reasonably dismissed and can affect your design and results. Do not worry about limitations because limitations affect virtually all research projects, as well as most things in life. Even when you are going to your favorite restaurant, you are limited by the menu choices. If you went to a restaurant that had a menu that you were craving, you might not receive the service, price, or location that makes you enjoy your favorite restaurant. If you studied participants’ responses to a survey, you might be limited in your abilities to gain the exact type or geographic scope of participants you wanted. The people whom you managed to get to take your survey may not truly be a random sample, which is also a limitation. If you used a common test for data findings, your results are limited by the reliability of the test. If your study was limited to a certain amount of time, your results are affected by the operations of society during that time period (e.g., economy, social trends). It is important for you to remember that limitations of a dissertation are often not something that can be solved by the researcher. Also, remember that whatever limits you also limits other researchers, whether they are the largest medical research companies or consumer habits corporations. Certain kinds of limitations are often associated with the analytical approach you take in your research, too. For example, some qualitative methods like heuristics or phenomenology do not lend themselves well to replicability. Also, most of the commonly used quantitative statistical models can only determine correlation, but not causation.

Assumptions

Assumptions are things that are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis. In other words, any scholar reading your paper will assume that certain aspects of your study is true given your population, statistical test, research design, or other delimitations. For example, if you tell your friend that your favorite restaurant is an Italian place, your friend will assume that you don’t go there for the sushi. It’s assumed that you go there to eat Italian food. Because most assumptions are not discussed in-text, assumptions that are discussed in-text are discussed in the context of the limitations of your study, which is typically in the discussion section. This is important, because both assumptions and limitations affect the inferences you can draw from your study. One of the more common assumptions made in survey research is the assumption of honesty and truthful responses. However, for certain sensitive questions this assumption may be more difficult to accept, in which case it would be described as a limitation of the study. For example, asking people to report their criminal behavior in a survey may not be as reliable as asking people to report their eating habits. It is important to remember that your limitations and assumptions should not contradict one another. For instance, if you state that generalizability is a limitation of your study given that your sample was limited to one city in the United States, then you should not claim generalizability to the United States population as an assumption of your study. Statistical models in quantitative research designs are accompanied with assumptions as well, some more strict than others. These assumptions generally refer to the characteristics of the data, such as distributions, correlational trends, and variable type, just to name a few. Violating these assumptions can lead to drastically invalid results, though this often depends on sample size and other considerations.

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21 Research Limitations Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Research limitations refer to the potential weaknesses inherent in a study. All studies have limitations of some sort, meaning declaring limitations doesn’t necessarily need to be a bad thing, so long as your declaration of limitations is well thought-out and explained.

Rarely is a study perfect. Researchers have to make trade-offs when developing their studies, which are often based upon practical considerations such as time and monetary constraints, weighing the breadth of participants against the depth of insight, and choosing one methodology or another.

In research, studies can have limitations such as limited scope, researcher subjectivity, and lack of available research tools.

Acknowledging the limitations of your study should be seen as a strength. It demonstrates your willingness for transparency, humility, and submission to the scientific method and can bolster the integrity of the study. It can also inform future research direction.

Typically, scholars will explore the limitations of their study in either their methodology section, their conclusion section, or both.

Research Limitations Examples

Qualitative and quantitative research offer different perspectives and methods in exploring phenomena, each with its own strengths and limitations. So, I’ve split the limitations examples sections into qualitative and quantitative below.

Qualitative Research Limitations

Qualitative research seeks to understand phenomena in-depth and in context. It focuses on the ‘why’ and ‘how’ questions.

It’s often used to explore new or complex issues, and it provides rich, detailed insights into participants’ experiences, behaviors, and attitudes. However, these strengths also create certain limitations, as explained below.

1. Subjectivity

Qualitative research often requires the researcher to interpret subjective data. One researcher may examine a text and identify different themes or concepts as more dominant than others.

Close qualitative readings of texts are necessarily subjective – and while this may be a limitation, qualitative researchers argue this is the best way to deeply understand everything in context.

Suggested Solution and Response: To minimize subjectivity bias, you could consider cross-checking your own readings of themes and data against other scholars’ readings and interpretations. This may involve giving the raw data to a supervisor or colleague and asking them to code the data separately, then coming together to compare and contrast results.

2. Researcher Bias

The concept of researcher bias is related to, but slightly different from, subjectivity.

Researcher bias refers to the perspectives and opinions you bring with you when doing your research.

For example, a researcher who is explicitly of a certain philosophical or political persuasion may bring that persuasion to bear when interpreting data.

In many scholarly traditions, we will attempt to minimize researcher bias through the utilization of clear procedures that are set out in advance or through the use of statistical analysis tools.

However, in other traditions, such as in postmodern feminist research , declaration of bias is expected, and acknowledgment of bias is seen as a positive because, in those traditions, it is believed that bias cannot be eliminated from research, so instead, it is a matter of integrity to present it upfront.

Suggested Solution and Response: Acknowledge the potential for researcher bias and, depending on your theoretical framework , accept this, or identify procedures you have taken to seek a closer approximation to objectivity in your coding and analysis.

3. Generalizability

If you’re struggling to find a limitation to discuss in your own qualitative research study, then this one is for you: all qualitative research, of all persuasions and perspectives, cannot be generalized.

This is a core feature that sets qualitative data and quantitative data apart.

The point of qualitative data is to select case studies and similarly small corpora and dig deep through in-depth analysis and thick description of data.

Often, this will also mean that you have a non-randomized sample size.

While this is a positive – you’re going to get some really deep, contextualized, interesting insights – it also means that the findings may not be generalizable to a larger population that may not be representative of the small group of people in your study.

Suggested Solution and Response: Suggest future studies that take a quantitative approach to the question.

4. The Hawthorne Effect

The Hawthorne effect refers to the phenomenon where research participants change their ‘observed behavior’ when they’re aware that they are being observed.

This effect was first identified by Elton Mayo who conducted studies of the effects of various factors ton workers’ productivity. He noticed that no matter what he did – turning up the lights, turning down the lights, etc. – there was an increase in worker outputs compared to prior to the study taking place.

Mayo realized that the mere act of observing the workers made them work harder – his observation was what was changing behavior.

So, if you’re looking for a potential limitation to name for your observational research study , highlight the possible impact of the Hawthorne effect (and how you could reduce your footprint or visibility in order to decrease its likelihood).

Suggested Solution and Response: Highlight ways you have attempted to reduce your footprint while in the field, and guarantee anonymity to your research participants.

5. Replicability

Quantitative research has a great benefit in that the studies are replicable – a researcher can get a similar sample size, duplicate the variables, and re-test a study. But you can’t do that in qualitative research.

Qualitative research relies heavily on context – a specific case study or specific variables that make a certain instance worthy of analysis. As a result, it’s often difficult to re-enter the same setting with the same variables and repeat the study.

Furthermore, the individual researcher’s interpretation is more influential in qualitative research, meaning even if a new researcher enters an environment and makes observations, their observations may be different because subjectivity comes into play much more. This doesn’t make the research bad necessarily (great insights can be made in qualitative research), but it certainly does demonstrate a weakness of qualitative research.

6. Limited Scope

“Limited scope” is perhaps one of the most common limitations listed by researchers – and while this is often a catch-all way of saying, “well, I’m not studying that in this study”, it’s also a valid point.

No study can explore everything related to a topic. At some point, we have to make decisions about what’s included in the study and what is excluded from the study.

So, you could say that a limitation of your study is that it doesn’t look at an extra variable or concept that’s certainly worthy of study but will have to be explored in your next project because this project has a clearly and narrowly defined goal.

Suggested Solution and Response: Be clear about what’s in and out of the study when writing your research question.

7. Time Constraints

This is also a catch-all claim you can make about your research project: that you would have included more people in the study, looked at more variables, and so on. But you’ve got to submit this thing by the end of next semester! You’ve got time constraints.

And time constraints are a recognized reality in all research.

But this means you’ll need to explain how time has limited your decisions. As with “limited scope”, this may mean that you had to study a smaller group of subjects, limit the amount of time you spent in the field, and so forth.

Suggested Solution and Response: Suggest future studies that will build on your current work, possibly as a PhD project.

8. Resource Intensiveness

Qualitative research can be expensive due to the cost of transcription, the involvement of trained researchers, and potential travel for interviews or observations.

So, resource intensiveness is similar to the time constraints concept. If you don’t have the funds, you have to make decisions about which tools to use, which statistical software to employ, and how many research assistants you can dedicate to the study.

Suggested Solution and Response: Suggest future studies that will gain more funding on the back of this ‘ exploratory study ‘.

9. Coding Difficulties

Data analysis in qualitative research often involves coding, which can be subjective and complex, especially when dealing with ambiguous or contradicting data.

After naming this as a limitation in your research, it’s important to explain how you’ve attempted to address this. Some ways to ‘limit the limitation’ include:

• Triangulation: Have 2 other researchers code the data as well and cross-check your results with theirs to identify outliers that may need to be re-examined, debated with the other researchers, or removed altogether.
• Procedure: Use a clear coding procedure to demonstrate reliability in your coding process. I personally use the thematic network analysis method outlined in this academic article by Attride-Stirling (2001).

Suggested Solution and Response: Triangulate your coding findings with colleagues, and follow a thematic network analysis procedure.

10. Risk of Non-Responsiveness

There is always a risk in research that research participants will be unwilling or uncomfortable sharing their genuine thoughts and feelings in the study.

This is particularly true when you’re conducting research on sensitive topics, politicized topics, or topics where the participant is expressing vulnerability .

This is similar to the Hawthorne effect (aka participant bias), where participants change their behaviors in your presence; but it goes a step further, where participants actively hide their true thoughts and feelings from you.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be non-responsiveness from some participants.

11. Risk of Attrition

Attrition refers to the process of losing research participants throughout the study.

This occurs most commonly in longitudinal studies , where a researcher must return to conduct their analysis over spaced periods of time, often over a period of years.

Things happen to people over time – they move overseas, their life experiences change, they get sick, change their minds, and even die. The more time that passes, the greater the risk of attrition.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be attrition over time.

12. Difficulty in Maintaining Confidentiality and Anonymity

Given the detailed nature of qualitative data , ensuring participant anonymity can be challenging.

If you have a sensitive topic in a specific case study, even anonymizing research participants sometimes isn’t enough. People might be able to induce who you’re talking about.

Sometimes, this will mean you have to exclude some interesting data that you collected from your final report. Confidentiality and anonymity come before your findings in research ethics – and this is a necessary limiting factor.

Suggested Solution and Response: Highlight the efforts you have taken to anonymize data, and accept that confidentiality and accountability place extremely important constraints on academic research.

13. Difficulty in Finding Research Participants

A study that looks at a very specific phenomenon or even a specific set of cases within a phenomenon means that the pool of potential research participants can be very low.

Compile on top of this the fact that many people you approach may choose not to participate, and you could end up with a very small corpus of subjects to explore. This may limit your ability to make complete findings, even in a quantitative sense.

You may need to therefore limit your research question and objectives to something more realistic.

Suggested Solution and Response: Highlight that this is going to limit the study’s generalizability significantly.

14. Ethical Limitations

Ethical limitations refer to the things you cannot do based on ethical concerns identified either by yourself or your institution’s ethics review board.

This might include threats to the physical or psychological well-being of your research subjects, the potential of releasing data that could harm a person’s reputation, and so on.

Furthermore, even if your study follows all expected standards of ethics, you still, as an ethical researcher, need to allow a research participant to pull out at any point in time, after which you cannot use their data, which demonstrates an overlap between ethical constraints and participant attrition.

Suggested Solution and Response: Highlight that these ethical limitations are inevitable but important to sustain the integrity of the research.

For more on Qualitative Research, Explore my Qualitative Research Guide

Quantitative Research Limitations

Quantitative research focuses on quantifiable data and statistical, mathematical, or computational techniques. It’s often used to test hypotheses, assess relationships and causality, and generalize findings across larger populations.

Quantitative research is widely respected for its ability to provide reliable, measurable, and generalizable data (if done well!). Its structured methodology has strengths over qualitative research, such as the fact it allows for replication of the study, which underpins the validity of the research.

However, this approach is not without it limitations, explained below.

1. Over-Simplification

Quantitative research is powerful because it allows you to measure and analyze data in a systematic and standardized way. However, one of its limitations is that it can sometimes simplify complex phenomena or situations.

In other words, it might miss the subtleties or nuances of the research subject.

For example, if you’re studying why people choose a particular diet, a quantitative study might identify factors like age, income, or health status. But it might miss other aspects, such as cultural influences or personal beliefs, that can also significantly impact dietary choices.

When writing about this limitation, you can say that your quantitative approach, while providing precise measurements and comparisons, may not capture the full complexity of your subjects of study.

Suggested Solution and Response: Suggest a follow-up case study using the same research participants in order to gain additional context and depth.

2. Lack of Context

Another potential issue with quantitative research is that it often focuses on numbers and statistics at the expense of context or qualitative information.

Let’s say you’re studying the effect of classroom size on student performance. You might find that students in smaller classes generally perform better. However, this doesn’t take into account other variables, like teaching style , student motivation, or family support.

When describing this limitation, you might say, “Although our research provides important insights into the relationship between class size and student performance, it does not incorporate the impact of other potentially influential variables. Future research could benefit from a mixed-methods approach that combines quantitative analysis with qualitative insights.”

3. Applicability to Real-World Settings

Oftentimes, experimental research takes place in controlled environments to limit the influence of outside factors.

This control is great for isolation and understanding the specific phenomenon but can limit the applicability or “external validity” of the research to real-world settings.

For example, if you conduct a lab experiment to see how sleep deprivation impacts cognitive performance, the sterile, controlled lab environment might not reflect real-world conditions where people are dealing with multiple stressors.

Therefore, when explaining the limitations of your quantitative study in your methodology section, you could state:

“While our findings provide valuable information about [topic], the controlled conditions of the experiment may not accurately represent real-world scenarios where extraneous variables will exist. As such, the direct applicability of our results to broader contexts may be limited.”

Suggested Solution and Response: Suggest future studies that will engage in real-world observational research, such as ethnographic research.

4. Limited Flexibility

Once a quantitative study is underway, it can be challenging to make changes to it. This is because, unlike in grounded research, you’re putting in place your study in advance, and you can’t make changes part-way through.

Your study design, data collection methods, and analysis techniques need to be decided upon before you start collecting data.

For example, if you are conducting a survey on the impact of social media on teenage mental health, and halfway through, you realize that you should have included a question about their screen time, it’s generally too late to add it.

When discussing this limitation, you could write something like, “The structured nature of our quantitative approach allows for consistent data collection and analysis but also limits our flexibility to adapt and modify the research process in response to emerging insights and ideas.”

Suggested Solution and Response: Suggest future studies that will use mixed-methods or qualitative research methods to gain additional depth of insight.

5. Risk of Survey Error

Surveys are a common tool in quantitative research, but they carry risks of error.

There can be measurement errors (if a question is misunderstood), coverage errors (if some groups aren’t adequately represented), non-response errors (if certain people don’t respond), and sampling errors (if your sample isn’t representative of the population).

For instance, if you’re surveying college students about their study habits , but only daytime students respond because you conduct the survey during the day, your results will be skewed.

In discussing this limitation, you might say, “Despite our best efforts to develop a comprehensive survey, there remains a risk of survey error, including measurement, coverage, non-response, and sampling errors. These could potentially impact the reliability and generalizability of our findings.”

Suggested Solution and Response: Suggest future studies that will use other survey tools to compare and contrast results.

6. Limited Ability to Probe Answers

With quantitative research, you typically can’t ask follow-up questions or delve deeper into participants’ responses like you could in a qualitative interview.

For instance, imagine you are surveying 500 students about study habits in a questionnaire. A respondent might indicate that they study for two hours each night. You might want to follow up by asking them to elaborate on what those study sessions involve or how effective they feel their habits are.

However, quantitative research generally disallows this in the way a qualitative semi-structured interview could.

When discussing this limitation, you might write, “Given the structured nature of our survey, our ability to probe deeper into individual responses is limited. This means we may not fully understand the context or reasoning behind the responses, potentially limiting the depth of our findings.”

Suggested Solution and Response: Suggest future studies that engage in mixed-method or qualitative methodologies to address the issue from another angle.

7. Reliance on Instruments for Data Collection

In quantitative research, the collection of data heavily relies on instruments like questionnaires, surveys, or machines.

The limitation here is that the data you get is only as good as the instrument you’re using. If the instrument isn’t designed or calibrated well, your data can be flawed.

For instance, if you’re using a questionnaire to study customer satisfaction and the questions are vague, confusing, or biased, the responses may not accurately reflect the customers’ true feelings.

When discussing this limitation, you could say, “Our study depends on the use of questionnaires for data collection. Although we have put significant effort into designing and testing the instrument, it’s possible that inaccuracies or misunderstandings could potentially affect the validity of the data collected.”

Suggested Solution and Response: Suggest future studies that will use different instruments but examine the same variables to triangulate results.

8. Time and Resource Constraints (Specific to Quantitative Research)

Quantitative research can be time-consuming and resource-intensive, especially when dealing with large samples.

It often involves systematic sampling, rigorous design, and sometimes complex statistical analysis.

If resources and time are limited, it can restrict the scale of your research, the techniques you can employ, or the extent of your data analysis.

For example, you may want to conduct a nationwide survey on public opinion about a certain policy. However, due to limited resources, you might only be able to survey people in one city.

When writing about this limitation, you could say, “Given the scope of our research and the resources available, we are limited to conducting our survey within one city, which may not fully represent the nationwide public opinion. Hence, the generalizability of the results may be limited.”

Suggested Solution and Response: Suggest future studies that will have more funding or longer timeframes.

How to Discuss Your Research Limitations

1. in your research proposal and methodology section.

In the research proposal, which will become the methodology section of your dissertation, I would recommend taking the four following steps, in order:

• Be Explicit about your Scope – If you limit the scope of your study in your research question, aims, and objectives, then you can set yourself up well later in the methodology to say that certain questions are “outside the scope of the study.” For example, you may identify the fact that the study doesn’t address a certain variable, but you can follow up by stating that the research question is specifically focused on the variable that you are examining, so this limitation would need to be looked at in future studies.
• Acknowledge the Limitation – Acknowledging the limitations of your study demonstrates reflexivity and humility and can make your research more reliable and valid. It also pre-empts questions the people grading your paper may have, so instead of them down-grading you for your limitations; they will congratulate you on explaining the limitations and how you have addressed them!
• Explain your Decisions – You may have chosen your approach (despite its limitations) for a very specific reason. This might be because your approach remains, on balance, the best one to answer your research question. Or, it might be because of time and monetary constraints that are outside of your control.
• Highlight the Strengths of your Approach – Conclude your limitations section by strongly demonstrating that, despite limitations, you’ve worked hard to minimize the effects of the limitations and that you have chosen your specific approach and methodology because it’s also got some terrific strengths. Name the strengths.

Overall, you’ll want to acknowledge your own limitations but also explain that the limitations don’t detract from the value of your study as it stands.

2. In the Conclusion Section or Chapter

In the conclusion of your study, it is generally expected that you return to a discussion of the study’s limitations. Here, I recommend the following steps:

• Acknowledge issues faced – After completing your study, you will be increasingly aware of issues you may have faced that, if you re-did the study, you may have addressed earlier in order to avoid those issues. Acknowledge these issues as limitations, and frame them as recommendations for subsequent studies.
• Suggest further research – Scholarly research aims to fill gaps in the current literature and knowledge. Having established your expertise through your study, suggest lines of inquiry for future researchers. You could state that your study had certain limitations, and “future studies” can address those limitations.
• Suggest a mixed methods approach – Qualitative and quantitative research each have pros and cons. So, note those ‘cons’ of your approach, then say the next study should approach the topic using the opposite methodology or could approach it using a mixed-methods approach that could achieve the benefits of quantitative studies with the nuanced insights of associated qualitative insights as part of an in-study case-study.

Overall, be clear about both your limitations and how those limitations can inform future studies.

In sum, each type of research method has its own strengths and limitations. Qualitative research excels in exploring depth, context, and complexity, while quantitative research excels in examining breadth, generalizability, and quantifiable measures. Despite their individual limitations, each method contributes unique and valuable insights, and researchers often use them together to provide a more comprehensive understanding of the phenomenon being studied.

Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research. Qualitative research , 1 (3), 385-405. ( Source )

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J., & Williams, R. A. (2021).  SAGE research methods foundations . London: Sage Publications.

Clark, T., Foster, L., Bryman, A., & Sloan, L. (2021).  Bryman’s social research methods . Oxford: Oxford University Press.

Köhler, T., Smith, A., & Bhakoo, V. (2022). Templates in qualitative research methods: Origins, limitations, and new directions.  Organizational Research Methods ,  25 (2), 183-210. ( Source )

Lenger, A. (2019). The rejection of qualitative research methods in economics.  Journal of Economic Issues ,  53 (4), 946-965. ( Source )

Taherdoost, H. (2022). What are different research approaches? Comprehensive review of qualitative, quantitative, and mixed method research, their applications, types, and limitations.  Journal of Management Science & Engineering Research ,  5 (1), 53-63. ( Source )

Walliman, N. (2021).  Research methods: The basics . New York: Routledge.

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Research Implications | Definition, Examples & Tips

Introduction

What are research implications, why discuss research implications, types of implications in research, how do you present research implications.

Every scientific inquiry is built on previous studies and lays the groundwork for future research. The latter is where discussion of research implications lies. Researchers are expected not only to present what their findings suggest about the phenomenon being studied but also what the findings mean in a broader context.

In this article, we'll explore the nature of research implications as a means for contextualizing the findings of qualitative research and the foundation it sets for further research.

Research implications include any kind of discussion of what a particular study means for its research field and in general terms. Researchers write implications to lay out future research studies, make research recommendations based on proposed theoretical developments, and discuss practical and technological implications that can be applied in the real world.

To put it another way, research implications are intended to answer the question "what does this research mean?". Research implications look forward and out. Once findings are presented and discussed, the researcher lays out what the findings mean in a broader context and how they could guide subsequent research.

An aspect of academic writing that's related to implications is the discussion of the study's limitations. These limitations differ from implications in that they explore already acknowledged shortcomings in a study (e.g., a small sample size, an inherent weakness in a chosen methodological approach), but these limitations can also suggest how future research could address these shortcomings. Both the implications and recommendations are often coupled with limitations in a discussion section to explain the significance of the study's contributions to scientific knowledge.

Strictly speaking, there is a fine line between limitations and implications, one that a traditional approach to the scientific method may not adequately explore. Under the scientific method, the product of any research study addresses its research questions or confirms or challenges its expected outcomes. Fulfilling just this task, however, may overlook a more important step in the research process in terms of demonstrating significance.

One of the more famous research examples can provide useful insight. Galileo's experiments with falling objects allowed him to answer questions raised by Aristotle's understanding about gravity affecting objects of different weights. Galileo had something of a hypothesis - objects should fall at the same speed regardless of weight - based on a critique of then-current scientific knowledge - Aristotle's assertion about gravity - that he wanted to test in research. By conducting different experiments using inclines and pendulums (and supposedly one involving falling objects from the Tower of Pisa), he established a new understanding about gravity and its relationship (or lack thereof) to the weight of objects.

Discussion of that experiment focused on how the findings challenged Aristotle's understanding of physics. It did not, however, pose the next logical question: Why would an object like a feather fall at a much slower rate of descent than an object like a hammer if weight was not a factor?

Galileo's experiment and other similar experiments laid the groundwork for experiments on air resistance, most famously the Apollo 15 experiment on the moon where a feather and hammer fell at the same rate in a vacuum, absent any air resistance. The limitation Galileo had at the time was the inability to create a vacuum to test any theories about gravity and air resistance. The implications of his experiments testing Aristotle's claims include the call to further research that could eventually confirm or challenge his understanding of falling objects.

In formal scientific research, particularly in academic settings where peer review is an essential component, contemporary researchers are supposed to do more than simply report their findings. They are expected to engage in critical reflection in placing their research findings in a broader context. The peer review process in research publication often assesses the quality of a research paper by its ability to detail the significance of a given research study. Without an explicit description of the implications in research, readers may not necessarily know what importance the study and its findings holds for them.

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Breaking down the kinds of implications that your research findings might have will be useful in crafting a clearer and more persuasive presentation. More important than saying that the findings are compelling is arguing in what aspects the findings should prove useful.

There are different types of implications, and the type you should emphasize depends on your target audience.

Theoretical implications

When research findings present novel scientific knowledge, it should have an influence on existing theories by affirming, contradicting, or contextualizing them. This can mean the proposal of a brand new theoretical framework or developments to a existing one.

Keep in mind that, in qualitative research , researchers will often contextualize a theory rather than confirm or refute it. This means that a theory or conceptual framework that is applied to an unfamiliar context (e.g., a theory about adolescent development in a study involving graduate students) will undergo some sort of transformation due to the new analysis.

New understandings will likely develop more complex descriptions of theories as they are interpreted and re-interpreted in new contexts. The discussion of theoretical implications here requires researchers to consider how new theoretical developments might be applied to new data in future research.

Practical implications

More applied forums are interested in how a study's findings can be used in the real world. New developments in psychology could yield discussion of applications in psychiatry, while research in physics can lead to technological innovations in engineering and architecture. While some researchers focus on developing theory, others conduct research to generate actionable insights and tangible results for stakeholders.

Education research, for example, may present pathways to a new teaching method or assessment of learining outcomes. Theories about how students passively and actively develop expertise in subject-matter knowledge could eventually prompt scholars and practitioners to change existing pedagogies and materials that account for more novel understandings of teaching and learning.

Exploring the practical dimensions of research findings may touch on political implications such as policy recommendations, marketable technologies, or novel approaches to existing methods or processes. Discussion of implications along these lines is meant to promote further research and activity in the field to support these practical developments.

Methodological implications

Qualitative research methods are always under constant development and innovation. Moreover, applying research methods in new contexts or for novel research inquiries can lead to unanticipated results that might cause a researcher to reflect on and iterate on their methods of data collection and analysis .

Critical reflections on research methods are not meant to assert that the study was conducted without the necessary rigor . However, rigorous and transparent researchers are expected to argue that further iterations of the research that address any methodological gaps can only bolster the persuasiveness of the findings or generate richer insights.

There are many possible avenues for implications in terms of innovating on methodology. Does the nature of your interview questions change when interviewing certain populations? Should you change certain practices when collecting data in an ethnography to establish rapport with research participants ? How does the use of technology influence the collection and analysis of data?

All of these questions are worth discussing, with the answers providing useful guidance to those who want to base their own study design on yours. As a result, it's important to devote some space in your paper or presentation to how you conducted your study and what you would do in future iterations of your study to bolster its research rigor.

Presenting research implications or writing research implications in a research paper is a matter of answering the following question: Why should scholars read or pay attention to your research? Especially in the social sciences, the potential impact of a study is not always a foregone conclusion. In other words, to make the findings as insightful and persuasive to your audience as they are to you, you need to persuade them beyond the presentation of the analysis and the insights generated.

Here are a few main principles to achieve this task. In broad terms, they focus on what the findings mean to you, what it should mean to others, and what those impacts might mean in context.

Establish importance

Academic research writing tends to follow a structure that narrates a study from the researcher's motivation to conduct the research to why the research's findings matter. While there's seldom a strict requirement for sections in a paper or presentation, understanding commonly used patterns in academic writing will point out where the research implications are discussed.

If you look at a typical research paper abstract in a peer-reviewed journal , for example, you might find that the last sentence or two explicitly establishes why the research is useful to motivate readers to look at the paper more deeply. In the body of the paper, this is further explained in detail towards the end of the introduction and discussion sections and in the conclusion section. These areas are where you should focus on detailing the research implications and explaining how you perceive the impact of your study.

It's essential that you use these spaces to highlight why the findings matter to you. As mentioned earlier, this impact should never be assumed to be understood. Rather, you should explain in detail how your initial motivation to conduct the research has been satisfied and how you might use what you have learned from the research in theoretical and practical terms.

Tailor to your audience

Research is partly about sharing expertise and partly about understanding your audience. Scientific knowledge is generated through consensus, and the more that the researcher ensures their implications are understood by their audience, the more it will resonate in the field.

A good strategy for tailoring your research paper to a particular journal is to read its articles for the implications that are explored in the research. Applied journals will focus on more practical implications while more theoretical publications will emphasize theoretical or conceptual frameworks for other scholars to rely on. As a result, there's no need to detail every single possible implication from your study; simply describing those implications that are most relevant to your audience is often sufficient.

Provide useful examples

One of the easier ways to persuade readers of the potential implications of your research is to provide concrete examples that are simple to understand.

Think about a study that interviews children, for example, where the methodological implications dwell on establishing an emotional connection before collecting data. This might include practical considerations such as bringing toys or conducting the interview in a setting familiar to them like their classroom so they are comfortable during data collection. Explicitly detailing this example can guide scholars in useful takeaways for their research design.

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• University of Michigan Library
• Research Guides

The Library Research Process, Step-by-Step

• Reading Scholarly Articles
• Finding & Exploring a Topic
• Finding Books
• Finding Articles
• Evaluating Sources
• Understanding & Using a Citation Style

Reading Scholarly Articles: Step-by-Step

1. Read the Abstract Section

The first step in reading a scholarly article is to read the abstract or summary of the article. Abstracts are always found at the beginning of an article and provide a basic summary or roadmap to the article. The abstract also introduces the purpose of the article.

Take a few minutes to carefully read the abstract of the practice article. Note that the abstract is not formally labeled "abstract" but is called "background and aims." Any summary at the start of an article is considered the abstract.

The abstract should always be read first to make sure the article is relevant to your topic. However, reading the abstract should never replace reading the entire article as the abstract is too brief to be used to fully understand the article.

2. Read the Conclusion Section Reading the conclusion will help you understand the main points of the article and what the authors are attempting to prove. 

3. Read the Introduction Section Now that you have an overview of the article from the abstract and understand the main points the authors are trying to prove from the conclusion, you will want to read the introduction.

4. Read the Results Section

Read the results section. Here are a couple of suggestions for deciphering results:

• If you are a visual learner, the charts may make sense to you.
• If charts are difficult to understand, look over the narrative and then return to the charts.
• Using the charts can help enhance your understanding of the narrative
• Look for works like "important" or "significant" and make special note of these phrases as these usually are signals from the author of an important result.

5. Read the Methods Section Reading the methods section will help you understand how the study or experiment was conducted. It is necessary for other researchers to understand the methods used so that they can replicate the study.

The methods section can also be difficult to read due to technical language used and density of the section. Try circling words, acronyms, and surveys you are unfamiliar with and look them up as those may be important to fully understand the article and may be necessary for future research. 

6. Read the Discussion & Limitations Section

The discussion section is where you will find the researcher's interpretation of the results. The author should answer the article's research question. Remember, you should evaluate the data to form your own conclusions. Don't just accept the author's conclusions without looking at the data for yourself.

Often authors will include a section detailing the limits to their research and their conclusions. The limitation section will usually explain conclusions that could not be drawn from the research as well as areas that future research is needed.

7. Read Through One More Time  After you have jumped around and read the different sections of the article, go back to the beginning and read the article in order. The article should be easier to read and make more sense as you will already be familiar with the main points in each section.

Watch: How to Read a Scholarly Article

Why Watch This Video? You'll learn essential strategies for reading scientific or scholarly journal articles, including:

• Identifying distinct sections (abstract, introduction, methods, results, discussion) and the purpose of those sections 
• How to effectively skim content using the ADIRM process (Abstract, Discussion, Introduction, Results, Methods), which will help you assess scholarly articles' relevance and validity
• Distinguishing between main points and less relevant sub points within scholarly research articles. 
• Learning about and applying these techniques will save you time and effort when working through your course assignments.

• Open access
• Published: 26 August 2024

Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research

• Elham Nasiri   ORCID: orcid.org/0000-0002-0644-1646 1 &
• Laleh Khojasteh   ORCID: orcid.org/0000-0002-6393-2759 1  

BMC Medical Education volume  24 , Article number:  925 ( 2024 ) Cite this article

15 Accesses

Metrics details

This study investigates the effectiveness of panel discussions, a specific interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, in English for Specific Purposes (ESP) courses for international medical students. This approach aims to simulate professional conference discussions, preparing students for future academic and clinical environments where such skills are crucial. While traditional group presentations foster critical thinking and communication, a gap exists in understanding how medical students perceive the complexities of preparing for and participating in panel discussions within an ESP setting. This qualitative study investigates the perceived advantages and disadvantages of these discussions from the perspectives of both panelists (medical students) and the audience (peers). Additionally, the study explores potential improvements based on insights from ESP instructors. Utilizing a two-phase design involving reflection papers and focus group discussions, data were collected from 46 medical students and three ESP instructors. Thematic analysis revealed that panel discussions offer unique benefits compared to traditional presentations, including enhanced engagement and more dynamic skill development for both panelists and the audience. Panelists reported gains in personal and professional development, including honing critical thinking, communication, and presentation skills. The audience perceived these discussions as engaging learning experiences that fostered critical analysis and information synthesis. However, challenges such as academic workload and concerns about discussion quality were also identified. The study concludes that panel discussions, when implemented effectively, can be a valuable tool for enhancing critical thinking, communication skills, and subject matter knowledge in ESP courses for medical students. These skills are transferable and can benefit students in various academic and professional settings, including future participation in medical conferences. This research provides valuable insights for ESP instructors seeking to integrate panel discussions into their curriculum, ultimately improving student learning outcomes and preparing them for future success in professional communication.

Peer Review reports

Introduction

In the field of medical education, the acquisition and application of effective communication skills are crucial for medical students in today’s global healthcare environment [ 1 ]. This necessitates not only strong English language proficiency but also the ability to present complex medical information clearly and concisely to diverse audiences.

Language courses, especially English for Specific Purposes (ESP) courses for medical students, are highly relevant in today’s globalized healthcare environment [ 2 ]. In non-English speaking countries like Iran, these courses are particularly important as they go beyond mere language instruction to include the development of critical thinking, cultural competence, and professional communication skills [ 3 ]. Proficiency in English is crucial for accessing up-to-date research, participating in international conferences, and communicating with patients and colleagues from diverse backgrounds [ 4 ]. Additionally, ESP courses help medical students understand and use medical terminologies accurately, which is essential for reading technical articles, listening to audio presentations, and giving spoken presentations [ 5 ]. In countries where English is not the primary language, ESP courses ensure that medical professionals can stay current with global advancements and collaborate effectively on an international scale [ 6 ]. Furthermore, these courses support students who may seek to practice medicine abroad, enhancing their career opportunities and professional growth [ 7 ].

Moreover, ESP courses enable medical professionals to communicate effectively with international patients, which is crucial in multicultural societies and for medical tourism, ensuring that patient care is not compromised due to language barriers [ 8 ]. Many medical textbooks, journals, and online resources are available primarily in English, and ESP courses equip medical students with the necessary language skills to access and comprehend these resources, ensuring they are well-informed about the latest medical research and practices [ 9 ].

Additionally, many medical professionals from non-English speaking countries aim to take international certification exams, such as the USMLE or PLAB, which are conducted in English, and ESP courses prepare students for these exams by familiarizing them with the medical terminology and language used in these assessments [ 10 ]. ESP courses also contribute to the professional development of medical students by improving their ability to write research papers, case reports, and other academic documents in English, which is essential for publishing in international journals and contributing to global medical knowledge [ 11 ]. In the increasingly interdisciplinary field of healthcare, collaboration with professionals from other countries is common, and ESP courses facilitate effective communication and collaboration with international colleagues, fostering innovation and the exchange of ideas [ 12 ].

With the rise of telemedicine and online medical consultations, proficiency in English is essential for non-English speaking medical professionals to provide remote healthcare services to international patients, and ESP courses prepare students for these modern medical practices [ 13 ].

Finally, ESP courses often include training on cultural competence, which is crucial for understanding and respecting the cultural backgrounds of patients and colleagues, leading to more empathetic and effective patient care and professional interactions [ 14 ]. Many ESP programs for medical students incorporate group presentations as a vital component of their curriculum, recognizing the positive impact on developing these essential skills [ 15 ].

Group projects in language courses, particularly in ESP for medical students, are highly relevant for several reasons. They provide a collaborative environment that mimics real-world professional settings, where healthcare professionals often work in multidisciplinary teams [ 16 ]. These group activities foster not only language skills but also crucial soft skills such as teamwork, leadership, and interpersonal communication, which are essential in medical practice [ 17 ].

The benefits of group projects over individual projects in language learning are significant. Hartono, Mujiyanto [ 18 ] found that group presentation tasks in ESP courses led to higher self-efficacy development compared to individual tasks. Group projects encourage peer learning, where students can learn from each other’s strengths and compensate for individual weaknesses [ 19 ]. They also provide a supportive environment that can reduce anxiety and increase willingness to communicate in the target language [ 20 ]. However, it is important to note that group projects also come with challenges, such as social loafing and unequal contribution, which need to be managed effectively [ 21 ].

Traditional lecture-based teaching methods, while valuable for knowledge acquisition, may not effectively prepare medical students for the interactive and collaborative nature of real-world healthcare settings [ 22 ]. Panel discussions (hereafter PDs), an interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, are particularly relevant in this context. They simulate professional conference discussions and interdisciplinary team meetings, preparing students for future academic and clinical environments where such skills are crucial [ 23 ].

PDs, also known as moderated discussions or moderated panels, are a specific type of interactive format where a group of experts or stakeholders engage in a facilitated conversation on a particular topic or issue [ 22 ]. In this format, a moderator guides the discussion, encourages active participation from all panelists, and fosters a collaborative environment that promotes constructive dialogue and critical thinking [ 24 ]. The goal is to encourage audience engagement and participation, which can be achieved through various strategies such as asking open-ended questions, encouraging counterpoints and counterarguments, and providing opportunities for audience members to pose questions or share their own experiences [ 25 ]. These discussions can take place in-person or online, and can be designed to accommodate diverse audiences and settings [ 26 ].

In this study, PD is considered a speaking activity where medical students are assigned specific roles to play during the simulation, such as a physician, quality improvement specialist, policymaker, or patient advocate. By taking on these roles, students can gain a better understanding of the diverse perspectives and considerations that come into play in real-world healthcare discussions [ 23 ]. Simulating PDs within ESP courses can be a powerful tool for enhancing medical students’ learning outcomes in multiple areas. This approach improves language proficiency, academic skills, and critical thinking abilities, while also enabling students to communicate effectively with diverse stakeholders in the medical field [ 27 , 28 ].

Theoretical framework

The panel discussions in our study are grounded in the concept of authentic assessment (outlined by Villarroel, Bloxham [ 29 ]), which involves designing tasks that mirror real-life situations and problems. In the context of medical education, this approach is particularly relevant as it prepares students for the complex, multidisciplinary nature of healthcare communication. Realism can be achieved through two means: providing a realistic context that describes and delivers a frame for the problem to be solved and creating tasks that are similar to those faced in real and/or professional life [ 30 ]. In our study, the PDs provide a realistic context by simulating scenarios where medical students are required to discuss and present complex medical topics in a professional setting, mirroring the types of interactions they will encounter in their future careers.

The task of participating in PDs also involves cognitive challenge, as students are required to think critically about complex medical topics, analyze information, and communicate their findings effectively. This type of task aims to generate processes of problem-solving, application of knowledge, and decision-making that correspond to the development of cognitive and metacognitive skills [ 23 ]. For medical students, these skills are crucial in developing clinical reasoning and effective patient communication. The PDs encourage students to go beyond the textual reproduction of fragmented and low-order content and move towards understanding, establishing relationships between new ideas and previous knowledge, linking theoretical concepts with everyday experience, deriving conclusions from the analysis of data, and examining both the logic of the arguments present in the theory and its practical scope [ 24 , 25 , 27 ].

Furthermore, the evaluative judgment aspect of our study is critical in helping students develop criteria and standards about what a good performance means in medical communication. This involves students judging their own performance and regulating their own learning [ 31 ]. In the context of panel discussions, students reflect on their own work, compare it with desired standards, and seek feedback from peers and instructors. By doing so, students can develop a sense of what constitutes good performance in medical communication and what areas need improvement [ 32 ]. Boud, Lawson and Thompson [ 33 ] argue that students need to build a precise judgment about the quality of their work and calibrate these judgments in the light of evidence. This skill is particularly important for future medical professionals who will need to continually assess and improve their communication skills throughout their careers.

The theoretical framework presented above highlights the importance of authentic learning experiences in medical education. By drawing on the benefits of group work and panel discussions, university instructor-researchers aimed to provide medical students with a unique opportunity to engage with complex cases and develop their communication and collaboration skills. As noted by Suryanarayana [ 34 ], authentic learning experiences can lead to deeper learning and improved retention. Considering the advantages of group work in promoting collaborative problem-solving and language development, the instructor-researchers designed a panel discussion task that simulates real-world scenarios, where students can work together to analyze complex cases, share knowledge, and present their findings to a simulated audience.

While previous studies have highlighted the benefits of interactive learning experiences and critical thinking skills in medical education, a research gap remains in understanding how medical students perceive the relevance of PDs in ESP courses. This study aims to address this gap by investigating medical students’ perceptions of PD tasks in ESP courses and how these perceptions relate to their language proficiency, critical thinking skills, and ability to communicate effectively with diverse stakeholders in the medical field. This understanding can inform best practices in medical education, contributing to the development of more effective communication skills for future healthcare professionals worldwide [ 23 ]. The research questions guiding this study are:

What are the perceived advantages of PDs from the perspectives of panelists and the audience?

What are the perceived disadvantages of PDs from the perspectives of panelists and the audience?

How can PDs be improved for panelists and the audience based on the insights of ESP instructors?

Methodology

Aim and design.

For this study, a two-phase qualitative design was employed to gain an understanding of the advantages and disadvantages of PDs from the perspectives of both student panelists and the audience (Phase 1) and to acquire an in-depth understanding of the suggested strategies provided by experts to enhance PPs for future students (Phase 2).

Participants and context of the study

This study was conducted in two phases (Fig.  1 ) at Shiraz University of Medical Sciences (SUMS), Shiraz, Iran.

Participants of the study in two phases

In the first phase, the student participants were 46 non-native speakers of English and international students who studied medicine at SUMS. Their demographic characteristics can be seen in Table  1 .

These students were purposefully selected because they were the only SUMS international students who had taken the ESP (English for Specific Purposes) course. The number of international students attending SUMS is indeed limited. Each year, a different batch of international students joins the university. They progress through a sequence of English courses, starting with General English 1 and 2, followed by the ESP course, and concluding with academic writing. At the time of data collection, the students included in the study were the only international students enrolled in the ESP course. This mandatory 3-unit course is designed to enhance their language and communication skills specifically tailored to their profession. As a part of the Medicine major curriculum, this course aims to improve their English language proficiency in areas relevant to medicine, such as understanding medical terminology, comprehending original medicine texts, discussing clinical cases, and communicating with patients, colleagues, and other healthcare professionals.

Throughout the course, students engage in various interactive activities, such as group discussions, role-playing exercises, and case studies, to develop their practical communication skills. In this course, medical students receive four marks out of 20 for their oral presentations, while the remaining marks are allocated to their written midterm and final exams. From the beginning of the course, they are briefed about PDs, and they are shown two YouTube-downloaded videos about PDs at medical conferences, a popular format for discussing and sharing knowledge, research findings, and expert opinions on various medical topics.

For the second phase of the study, a specific group of participants was purposefully selected. This group consisted of three faculty members from SUMS English department who had extensive experience attending numerous conferences at national and international levels, particularly in the medical field, as well as working as translators and interpreters in medical congresses. Over the course of ten years, they also gained considerable experience in PDs. They were invited to discuss strategies helpful for medical students with PDs.

Panel discussion activity design and implementation

When preparing for a PD session, medical students received comprehensive guidance on understanding the roles and responsibilities of each panel member. This guidance was aimed at ensuring that each participant was well-prepared and understood their specific role in the discussion.

Moderators should play a crucial role in steering the conversation. They are responsible for ensuring that all panelists have an opportunity to contribute and that the audience is engaged effectively. Specific tasks include preparing opening remarks, introducing panelists, and crafting transition questions to facilitate smooth topic transitions. The moderators should also manage the time to ensure balanced participation and encourage active audience involvement.

Panelists are expected to be subject matter experts who bring valuable insights and opinions to the discussion. They are advised to conduct thorough research on the topic and prepare concise talking points. Panelists are encouraged to draw from their medical knowledge and relevant experiences, share evidence-based information, and engage with other panelists’ points through active listening and thoughtful responses.

The audience plays an active role in the PDs. They are encouraged to participate by asking questions, sharing relevant experiences, and contributing to the dialogue. To facilitate this, students are advised to take notes during the discussion and think of questions or comments they can contribute during the Q&A segment.

For this special course, medical students were advised to choose topics either from their ESP textbook or consider current medical trends, emerging research, and pressing issues in their field. Examples included breast cancer, COVID-19, and controversies in gene therapy. The selection process involved brainstorming sessions and consultation with the course instructor to ensure relevance and appropriateness.

To accommodate the PD sessions within the course structure, students were allowed to start their PD sessions voluntarily from the second week. However, to maintain a balance between peer-led discussions and regular course content, only one PD was held weekly. This approach enabled the ESP lecturer to deliver comprehensive content while also allowing students to engage in these interactive sessions.

A basic time structure was suggested for each PD (Fig.  2 ):

Time allocation for panel discussion stages in minutes

To ensure the smooth running of the course and maintain momentum, students were informed that they could cancel their PD session only once. In such cases, they were required to notify the lecturer and other students via the class Telegram channel to facilitate rescheduling and minimize disruptions. This provision was essential in promoting a sense of community among students and maintaining the course’s continuity.

Research tools and data collection

The study utilized various tools to gather and analyze data from participants and experts, ensuring a comprehensive understanding of the research topic.

Reflection papers

In Phase 1 of the study, 46 medical students detailed their perceptions of the advantages and disadvantages of panel discussions from dual perspectives: as panelists (presenters) and as audience members (peers).

Participants were given clear instructions and a 45-minute time frame to complete the reflection task. With approximately 80% of the international language students being native English speakers and the rest fluent in English, the researchers deemed this time allocation reasonable. The questions and instructions were straightforward, facilitating quick comprehension. It was estimated that native English speakers would need about 30 min to complete the task, while non-native speakers might require an extra 15 min for clarity and expression. This time frame aimed to allow students to respond thoughtfully without feeling rushed. Additionally, students could request more time if needed.

Focus group discussion

In phase 2 of the study, a focus group discussion was conducted with three expert participants. The purpose of the focus group was to gather insights from expert participants, specifically ESP (English for Specific Purposes) instructors, on how presentation dynamics can be improved for both panelists and the audience.

According to Colton and Covert [ 35 ], focus groups are useful for obtaining detailed input from experts. The appropriate size of a focus group is determined by the study’s scope and available resources [ 36 ]. Morgan [ 37 ] suggests that small focus groups are suitable for complex topics where specialist participants might feel frustrated if not allowed to express themselves fully.

The choice of a focus group over individual interviews was based on several factors. First, the exploratory nature of the study made focus groups ideal for interactive discussions, generating new ideas and in-depth insights [ 36 ]. Second, while focus groups usually involve larger groups, they can effectively accommodate a limited number of experts with extensive knowledge [ 37 ]. Third, the focus group format fostered a more open environment for idea exchange, allowing participants to engage dynamically [ 36 ]. Lastly, conducting a focus group was more time- and resource-efficient than scheduling three separate interviews [ 36 ].

Data analysis

The first phase of the study involved a thorough examination of the data related to the research inquiries using thematic analysis. This method was chosen for its effectiveness in uncovering latent patterns from a bottom-up perspective, facilitating a comprehensive understanding of complex educational phenomena [ 38 ]. The researchers first familiarized themselves with the data by repeatedly reviewing the reflection papers written by the medical students. Next, an initial round of coding was independently conducted to identify significant data segments and generate preliminary codes that reflected the students’ perceptions of the advantages and disadvantages of presentation dynamics PDs from both the presenter and audience viewpoints [ 38 ].

The analysis of the reflection papers began with the two researchers coding a subset of five papers independently, adhering to a structured qualitative coding protocol [ 39 ]. They convened afterward to compare their initial codes and address any discrepancies. Through discussion, they reached an agreement on the codes, which were then analyzed, organized into categories and themes, and the frequency of each code was recorded [ 38 ].

After coding the initial five papers, the researchers continued to code the remaining 41 reflection paper transcripts in batches of ten, meeting after each batch to review their coding, resolve any inconsistencies, and refine the coding framework as needed. This iterative process, characterized by independent coding, joint reviews, and consensus-building, helped the researchers establish a robust and reliable coding approach consistently applied to the complete dataset [ 40 ]. Once all 46 reflection paper transcripts were coded, the researchers conducted a final review and discussion to ensure accurate analysis. They extracted relevant excerpts corresponding to the identified themes and sub-themes from the transcripts to provide detailed explanations and support for their findings [ 38 ]. This multi-step approach of separate initial coding, collaborative review, and frequency analysis enhanced the credibility and transparency of the qualitative data analysis.

To ensure the trustworthiness of the data collected in this study, the researchers adhered to the Guba and Lincoln standards of scientific accuracy in qualitative research, which encompass credibility, confirmability, dependability, and transferability [ 41 ] (Table  2 ).

The analysis of the focus group data obtained from experts followed the same rigorous procedure applied to the student participants’ data. Thematic analysis was employed to examine the experts’ perspectives, maintaining consistency in the analytical approach across both phases of the study. The researchers familiarized themselves with the focus group transcript, conducted independent preliminary coding, and then collaboratively refined the codes. These codes were subsequently organized into categories and themes, with the frequency of each code recorded. The researchers engaged in thorough discussions to ensure agreement on the final themes and sub-themes. Relevant excerpts from the focus group transcript were extracted to provide rich, detailed explanations of each theme, thereby ensuring a comprehensive and accurate analysis of the experts’ insights.

1. What are the advantages of PDs from the perspective of panelists and the audience?

The analysis of the advantages of PDs from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  2 and 3 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  3 ), the overarching theme was “Personal and Professional Development.” The most frequently reported advantage was knowledge sharing (93.5%), followed closely by increased confidence (91.3%) and the importance of interaction in presentations (91.3%).

Notably, all categories within this theme had at least one code mentioned by over 80% of participants, indicating a broad range of perceived benefits. The category of “Effective teamwork and communication” was particularly prominent, with collaboration (89.1%) and knowledge sharing (93.5%) being among the most frequently cited advantages. This suggests that PDs are perceived as valuable tools for fostering interpersonal skills and collective learning. In the “Language mastery” category, increased confidence (91.3%) and better retention of key concepts (87.0%) were highlighted, indicating that PDs are seen as effective for both language and content learning.

The audience perspective (Table  4 ), encapsulated under the theme “Enriching Learning Experience,” showed similarly high frequencies across all categories.

The most frequently mentioned advantage was exposure to diverse speakers (93.5%), closely followed by the range of topics covered (91.3%) and increased audience interest (91.3%). The “Broadening perspectives” category was particularly rich, with all codes mentioned by over 70% of participants. This suggests that audience members perceive PDs as valuable opportunities for expanding their knowledge and viewpoints. In the “Language practice” category, the opportunity to practice language skills (89.1%) was the most frequently cited advantage, indicating that even as audience members, students perceive significant language learning benefits.

Comparing the two perspectives reveals several interesting patterns:

High overall engagement: Both panelists and audience members reported high frequencies across all categories, suggesting that PDs are perceived as beneficial regardless of the role played.

Language benefits: While panelists emphasized increased confidence (91.3%) and better retention of concepts (87.0%), audience members highlighted opportunities for language practice (89.1%). This indicates that PDs offer complementary language learning benefits for both roles.

Interactive learning: The importance of interaction was highly rated by panelists (91.3%), while increased audience interest was similarly valued by the audience (91.3%). This suggests that PDs are perceived as an engaging, interactive learning method from both perspectives.

Professional development: Panelists uniquely emphasized professional growth aspects such as experiential learning (84.8%) and real-world application (80.4%). These were not directly mirrored in the audience perspective, suggesting that active participation in PDs may offer additional professional development benefits.

Broadening horizons: Both groups highly valued the diversity aspect of PDs. Panelists appreciated diversity and open-mindedness (80.4%), while audience members valued diverse speakers (93.5%) and a range of topics (91.3%).

2. What are the disadvantages of PDs from the perspective of panelists and the audience?

The analysis of the disadvantages of panel discussions (PDs) from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  4 and 5 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  5 ), the theme “Drawbacks of PDs” was divided into two main categories: “Academic Workload Challenges” and “Coordination Challenges.” The most frequently reported disadvantage was long preparation (87.0%), followed by significant practice needed (82.6%) and the time-consuming nature of PDs (80.4%). These findings suggest that the primary concern for panelists is the additional workload that PDs impose on their already demanding academic schedules. The “Coordination Challenges” category, while less prominent than workload issues, still presented significant concerns. Diverse panel skills (78.3%) and finding suitable panelists (73.9%) were the most frequently cited issues in this category, indicating that team dynamics and composition are notable challenges for panelists.

The audience perspective (Table  6 ), encapsulated under the theme “Drawbacks of PDs,” was divided into two main categories: “Time-related Issues” and “Interaction and Engagement Issues.” In the “Time-related Issues” category, the most frequently mentioned disadvantage was the inefficient use of time (65.2%), followed by the perception of PDs as too long and boring (60.9%). Notably, 56.5% of respondents found PDs stressful due to overwhelming workload from other studies, and 52.2% considered them not very useful during exam time. The “Interaction and Engagement Issues” category revealed more diverse concerns. The most frequently mentioned disadvantage was the repetitive format (82.6%), followed by limited engagement with the audience (78.3%) and the perception of PDs as boring (73.9%). The audience also noted issues related to the panelists’ preparation and coordination, such as “Not practiced and natural” (67.4%) and “Coordination and Interaction Issues” (71.7%), suggesting that the challenges faced by panelists directly impact the audience’s experience.

Workload concerns: Both panelists and audience members highlighted time-related issues. For panelists, this manifested as long preparation times (87.0%) and difficulty balancing with other studies (76.1%). For the audience, it appeared as perceptions of inefficient use of time (65.2%) and stress due to overwhelming workload from other studies (56.5%).

Engagement issues: While panelists focused on preparation and coordination challenges, the audience emphasized the quality of the discussion and engagement. This suggests a potential mismatch between the efforts of panelists and the expectations of the audience.

Boredom and repetition: The audience frequently mentioned boredom (73.9%) and repetitive format (82.6%) as issues, which weren’t directly mirrored in the panelists’ responses. This indicates that while panelists may be focused on content preparation, the audience is more concerned with the delivery and variety of the presentation format.

Coordination challenges: Both groups noted coordination issues, but from different perspectives. Panelists struggled with team dynamics and finding suitable co-presenters, while the audience observed these challenges manifesting as unnatural or unpracticed presentations.

Academic pressure: Both groups acknowledged the strain PDs put on their academic lives, with panelists viewing it as a burden (65.2%) and the audience finding it less useful during exam times (52.2%).

3. How can PDs be improved for panelists and the audience from the experts’ point of view?

The presentation of data for this research question differs from the previous two due to the unique nature of the information gathered. Unlike the quantifiable student responses in earlier questions, this data stems from expert opinions and a reflection discussion session, focusing on qualitative recommendations for improvement rather than frequency of responses (Braun & Clarke, 2006). The complexity and interconnectedness of expert suggestions, coupled with the integration of supporting literature, necessitate a more narrative approach (Creswell & Poth, 2018). This format allows for a richer exploration of the context behind each recommendation and its potential implications (Patton, 2015). Furthermore, the exploratory nature of this question, aimed at generating ideas for improvement rather than measuring prevalence of opinions, is better served by a detailed, descriptive presentation (Merriam & Tisdell, 2016). This approach enables a more nuanced understanding of how PDs can be enhanced, aligning closely with the “how” nature of the research question and providing valuable insights for potential implementation (Yin, 2018).

The experts provided several suggestions to address the challenges faced by students in panel discussions (PDs) and improve the experience for both panelists and the audience. Their recommendations focused on six key areas: time management and workload, preparation and skill development, engagement and interactivity, technological integration, collaboration and communication, and institutional support.

To address the issue of time management and heavy workload, one expert suggested teaching students to “ break down the task to tackle the time-consuming nature of panel discussions and balance it with other studies .” This approach aims to help students manage the extensive preparation time required for PDs without compromising their other academic responsibilities. Another expert emphasized “ enhancing medical students’ abilities to prioritize tasks , allocate resources efficiently , and optimize their workflow to achieve their goals effectively .” These skills were seen as crucial not only for PD preparation but also for overall academic success and future professional practice.

Recognizing the challenges of long preparation times and the perception of PDs being burdensome, an expert proposed “ the implementation of interactive training sessions for panelists .” These sessions were suggested to enhance coordination skills and improve the ability of group presenters to engage with the audience effectively. The expert emphasized that such training could help students view PDs as valuable learning experiences rather than additional burdens, potentially increasing their motivation and engagement in the process.

To combat issues of limited engagement and perceived boredom, experts recommended increasing engagement opportunities for the audience through interactive elements like audience participation and group discussions. They suggested that this could transform PDs from passive listening experiences to active learning opportunities. One expert suggested “ optimizing time management and restructuring the format of panel discussions ” to address inefficiency during sessions. This restructuring could involve shorter presentation segments interspersed with interactive elements to maintain audience attention and engagement.

An innovative solution proposed by one expert was “ using ChatGPT to prepare for PDs by streamlining scenario presentation preparation and role allocation. ” The experts collectively discussed the potential of AI to assist medical students in reducing their workload and saving time in preparing scenario presentations and allocating roles in panel discussions. They noted that AI could help generate initial content drafts, suggest role distributions based on individual strengths, and even provide practice questions for panelists, significantly reducing preparation time while maintaining quality.

Two experts emphasized the importance of enhancing collaboration and communication among panelists to address issues related to diverse panel skills and coordination challenges. They suggested establishing clear communication channels and guidelines to improve coordination and ensure a cohesive presentation. This could involve creating structured team roles, setting clear expectations for each panelist, and implementing regular check-ins during the preparation process to ensure all team members are aligned and progressing.

All experts were in agreement that improving PDs would not be possible “ if nothing is done by the university administration to reduce the ESP class size for international students .” They believed that large class sizes in ESP or EFL classes could negatively influence group oral presentations, hindering language development and leading to uneven participation. The experts suggested that smaller class sizes would allow for more individualized attention, increased speaking opportunities for each student, and more effective feedback mechanisms, all of which are crucial for developing strong presentation skills in a second language.

Research question 1: what are the advantages of PDs from the perspective of panelists and the audience?

The results of this study reveal significant advantages of PDs for both panelists and audience members in the context of medical education. These findings align with and expand upon previous research in the field of educational presentations and language learning.

Personal and professional development for panelists

The high frequency of reported benefits in the “Personal and Professional Development” theme for panelists aligns with several previous studies. The emphasis on language mastery, particularly increased confidence (91.3%) and better retention of key concepts (87.0%), supports the findings of Hartono, Mujiyanto [ 42 ], Gedamu and Gezahegn [ 15 ], Li [ 43 ], who all highlighted the importance of language practice in English oral presentations. However, our results show a more comprehensive range of benefits, including professional growth aspects like experiential learning (84.8%) and real-world application (80.4%), which were not as prominently featured in these earlier studies.

Interestingly, our findings partially contrast with Chou [ 44 ] study, which found that while group oral presentations had the greatest influence on improving students’ speaking ability, individual presentations led to more frequent use of metacognitive, retrieval, and rehearsal strategies. Our results suggest that PDs, despite being group activities, still provide significant benefits in these areas, possibly due to the collaborative nature of preparation and the individual responsibility each panelist bears. The high frequency of knowledge sharing (93.5%) and collaboration (89.1%) in our study supports Harris, Jones and Huffman [ 45 ] emphasis on the importance of group dynamics and varied perspectives in educational settings. However, our study provides more quantitative evidence for these benefits in the specific context of PDs.

Enriching learning experience for the audience

The audience perspective in our study reveals a rich learning experience, with high frequencies across all categories. This aligns with Agustina [ 46 ] findings in business English classes, where presentations led to improvements in all four language skills. However, our study extends these findings by demonstrating that even passive participation as an audience member can lead to significant perceived benefits in language practice (89.1%) and broadening perspectives (93.5% for diverse speakers). The high value placed on diverse speakers (93.5%) and range of topics (91.3%) by the audience supports the notion of PDs as a tool for expanding knowledge and viewpoints. This aligns with the concept of situated learning experiences leading to deeper understanding in EFL classes, as suggested by Li [ 43 ] and others [ 18 , 31 ]. However, our study provides more specific evidence for how this occurs in the context of PDs.

Interactive learning and engagement

Both panelists and audience members in our study highly valued the interactive aspects of PDs, with the importance of interaction rated at 91.3% by panelists and increased audience interest at 91.3% by the audience. This strong emphasis on interactivity aligns with Azizi and Farid Khafaga [ 19 ] study on the benefits of dynamic assessment and dialogic learning contexts. However, our study provides more detailed insights into how this interactivity is perceived and valued by both presenters and audience members in PDs.

Professional growth and real-world application

The emphasis on professional growth through PDs, particularly for panelists, supports Li’s [ 43 ] assertion about the power of oral presentations as situated learning experiences. Our findings provide more specific evidence for how PDs contribute to professional development, with high frequencies reported for experiential learning (84.8%) and real-world application (80.4%). This suggests that PDs may be particularly effective in bridging the gap between academic learning and professional practice in medical education.

Research question 2: what are the disadvantages of pds from the perspective of panelists and the audience?

Academic workload challenges for panelists.

The high frequency of reported challenges in the “Academic Workload Challenges” category for panelists aligns with several previous studies in medical education [ 47 , 48 , 49 ]. The emphasis on long preparation (87.0%), significant practice needed (82.6%), and the time-consuming nature of PDs (80.4%) supports the findings of Johnson et al. [ 24 ], who noted that while learners appreciate debate-style journal clubs in health professional education, they require additional time commitment. This is further corroborated by Nowak, Speed and Vuk [ 50 ], who found that intensive learning activities in medical education, while beneficial, can be time-consuming for students.

Perceived value of pds relative to time investment

While a significant portion of the audience (65.2%) perceived PDs as an inefficient use of time, the high frequency of engagement-related concerns (82.6% for repetitive format, 78.3% for limited engagement) suggests that the perceived lack of value may be more closely tied to the quality of the experience rather than just the time investment. This aligns with Dyhrberg O’Neill [ 27 ] findings on debate-based oral exams, where students perceived value despite the time-intensive nature of the activity. However, our results indicate a more pronounced concern about the return on time investment in PDs. This discrepancy might be addressed through innovative approaches to PD design and implementation, such as those proposed by Almazyad et al. [ 22 ], who suggested using AI tools to enhance expert panel discussions and potentially improve efficiency.

Coordination challenges for panelists

The challenges related to coordination in medical education, such as diverse panel skills (78.3%) and finding suitable panelists (73.9%), align with previous research on teamwork in higher education [ 21 ]. Our findings support the concept of the free-rider effect discussed by Hall and Buzwell [ 21 ], who explored reasons for non-contribution in group projects beyond social loafing. This is further elaborated by Mehmood, Memon and Ali [ 51 ], who proposed that individuals may not contribute their fair share due to various factors including poor communication skills or language barriers, which is particularly relevant in medical education where clear communication is crucial [ 52 ]. Comparing our results to other collaborative learning contexts in medical education, Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] measured teamwork competence development in a multidisciplinary project-based learning environment. They found that while teamwork skills improved over time, initial coordination challenges were significant. This aligns with our findings on the difficulties of coordinating diverse panel skills and opinions in medical education settings.

Our results also resonate with Chou’s [ 44 ] study comparing group and individual oral presentations, which found that group presenters often had a limited understanding of the overall content. This is supported by Wilson, Ho and Brookes [ 54 ], who examined student perceptions of teamwork in undergraduate science degrees, highlighting the challenges and benefits of collaborative work, which are equally applicable in medical education [ 52 ].

Quality of discussions and perception for the audience

The audience perspective in our study reveals significant concerns about the quality and engagement of PDs in medical education. The high frequency of issues such as repetitive format (82.6%) and limited engagement with the audience (78.3%) aligns with Parmar and Bickmore [ 55 ] findings on the importance of addressing individual audience members and gathering feedback. This is further supported by Nurakhir et al. [ 25 ], who explored students’ views on classroom debates as a strategy to enhance critical thinking and oral communication skills in nursing education, which shares similarities with medical education. Comparing our results to other interactive learning methods in medical education, Jones et al. [ 26 ] reviewed the use of journal clubs and book clubs in pharmacy education. They found that while these methods enhanced engagement, they also faced challenges in maintaining student interest over time, similar to the boredom issues reported in our study of PDs in medical education. The perception of PDs as boring (73.9%) and not very useful during exam time (52.2%) supports previous research on the stress and pressure experienced by medical students [ 48 , 49 ]. Grieve et al. [ 20 ] specifically examined student fears of oral presentations and public speaking in higher education, which provides context for the anxiety and disengagement observed in our study of medical education. Interestingly, Bhuvaneshwari et al. [ 23 ] found positive impacts of panel discussions in educating medical students on specific modules. This contrasts with our findings and suggests that the effectiveness of PDs in medical education may vary depending on the specific context and implementation.

Comparative analysis and future directions

Our study provides a unique comparative analysis of the challenges faced by both panelists and audience members in medical education. The alignment of concerns around workload and time management between the two groups suggests that these are overarching issues in the implementation of PDs in medical curricula. This is consistent with the findings of Pasandín et al. [ 56 ], who examined cooperative oral presentations in higher education and their impact on both technical and soft skills, which are crucial in medical education [ 52 ]. The mismatch between panelist efforts and audience expectations revealed in our study is a novel finding that warrants further investigation in medical education. This disparity could be related to the self-efficacy beliefs of presenters, as explored by Gedamu and Gezahegn [ 15 ] in their study of TEFL trainees’ attitudes towards academic oral presentations, which may have parallels in medical education. Looking forward, innovative approaches could address some of the challenges identified in medical education. Almazyad et al. [ 22 ] proposed using AI tools like ChatGPT to enhance expert panel discussions in pediatric palliative care, which could potentially address some of the preparation and engagement issues identified in our study of medical education. Additionally, Ragupathi and Lee [ 57 ] discussed the role of rubrics in higher education, which could provide clearer expectations and feedback for both panelists and audience members in PDs within medical education.

Research question 3: how can PDs be improved for panelists and the audience from the experts’ point of view?

The expert suggestions for improving PDs address several key challenges identified in previous research on academic presentations and student workload management. These recommendations align with current trends in educational technology and pedagogical approaches, while also considering the unique needs of medical students.

The emphasis on time management and workload reduction strategies echoes findings from previous studies on medical student stress and academic performance. Nowak, Speed and Vuk [ 50 ] found that medical students often struggle with the fast-paced nature of their courses, which can lead to reduced motivation and superficial learning approaches. The experts’ suggestions for task breakdown and prioritization align with Rabbi and Islam [ 58 ] recommendations for reducing workload stress through effective assignment prioritization. Additionally, Popa et al. [ 59 ] highlight the importance of acceptance and planning in stress management for medical students, supporting the experts’ focus on these areas.

The proposed implementation of interactive training sessions for panelists addresses the need for enhanced presentation skills in professional contexts, a concern highlighted by several researchers [ 17 , 60 ]. This aligns with Grieve et al. [ 20 ] findings on student fears of oral presentations and public speaking in higher education, emphasizing the need for targeted training. The focus on interactive elements and audience engagement also reflects current trends in active learning pedagogies, as demonstrated by Pasandín et al. [ 56 ] in their study on cooperative oral presentations in engineering education.

The innovative suggestion to use AI tools like ChatGPT for PD preparation represents a novel approach to leveraging technology in education. This aligns with recent research on the potential of AI in scientific research, such as the study by Almazyad et al. [ 22 ], which highlighted the benefits of AI in supporting various educational tasks. However, it is important to consider potential ethical implications and ensure that AI use complements rather than replaces critical thinking and creativity.

The experts’ emphasis on enhancing collaboration and communication among panelists addresses issues identified in previous research on teamwork in higher education. Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] noted the importance of measuring teamwork competence development in project-based learning environments. The suggested strategies for improving coordination align with best practices in collaborative learning, as demonstrated by Romero-Yesa et al. [ 61 ] in their qualitative assessment of challenge-based learning and teamwork in electronics programs.

The unanimous agreement on the need to reduce ESP class sizes for international students reflects ongoing concerns about the impact of large classes on language learning and student engagement. This aligns with research by Li [ 3 ] on issues in developing EFL learners’ oral English communication skills. Bosco et al. [ 62 ] further highlight the challenges of teaching and learning ESP in mixed classes, supporting the experts’ recommendation for smaller class sizes. Qiao, Xu and bin Ahmad [ 63 ] also emphasize the implementation challenges for ESP formative assessment in large classes, further justifying the need for reduced class sizes.

These expert recommendations provide a comprehensive approach to improving PDs, addressing not only the immediate challenges of preparation and delivery but also broader issues of student engagement, workload management, and institutional support. By implementing these suggestions, universities could potentially transform PDs from perceived burdens into valuable learning experiences that enhance both academic and professional skills. This aligns with Kho and Ting [ 64 ] systematic review on overcoming oral presentation anxiety among tertiary ESL/EFL students, which emphasizes the importance of addressing both challenges and strategies in improving presentation skills.

This study has shed light on the complex challenges associated with PDs in medical education, revealing a nuanced interplay between the experiences of panelists and audience members. The findings underscore the need for a holistic approach to implementing PDs that addresses both the academic workload concerns and the quality of engagement.

Our findings both support and extend previous research on the challenges of oral presentations and group work in medical education settings. The high frequencies of perceived challenges across multiple categories for both panelists and audience members suggest that while PDs may offer benefits, they also present significant obstacles that need to be addressed in medical education. These results highlight the need for careful consideration in the implementation of PDs in medical education, with particular attention to workload management, coordination strategies, and audience engagement techniques. Future research could focus on developing and testing interventions to mitigate these challenges while preserving the potential benefits of PDs in medical education.

Moving forward, medical educators should consider innovative approaches to mitigate these challenges. This may include:

Integrating time management and stress coping strategies into the PD preparation process [ 59 ].

Exploring the use of AI tools to streamline preparation and enhance engagement [ 22 ].

Developing clear rubrics and expectations for both panelists and audience members [ 57 ].

Incorporating interactive elements to maintain audience interest and participation [ 25 ].

Limitations and future research

One limitation of this study is that it focused on a specific population of medical students, which may limit the generalizability of the findings to other student populations. Additionally, the study relied on self-report data from panelists and audience members, which may introduce bias and affect the validity of the results. Future research could explore the effectiveness of PDs in different educational contexts and student populations to provide a more comprehensive understanding of the benefits and challenges of panel discussions.

Future research should focus on evaluating the effectiveness of these interventions and exploring how PDs can be tailored to the unique demands of medical education. By addressing the identified challenges, PDs have the potential to become a more valuable and engaging component of medical curricula, fostering both academic and professional development. Ultimately, the goal should be to transform PDs from perceived burdens into opportunities for meaningful learning and skill development, aligning with the evolving needs of medical education in the 21st century.

Future research could also examine the long-term impact of PDs on panelists’ language skills, teamwork, and communication abilities. Additionally, exploring the effectiveness of different training methods and tools, such as AI technology, in improving coordination skills and reducing workload stress for panelists could provide valuable insights for educators and administrators. Further research could also investigate the role of class size and audience engagement in enhancing the overall effectiveness of PDs in higher education settings. By addressing these gaps in the literature, future research can contribute to the ongoing development and improvement of PDs as a valuable learning tool for students in higher education.

However, it is important to note that implementing these changes may require significant institutional resources and a shift in pedagogical approaches. Future research could focus on piloting these recommendations and evaluating their effectiveness in improving student outcomes and experiences with PDs.

Data availability

We confirm that the data supporting the findings are available within this article. Raw data supporting this study’s findings are available from the corresponding author, upon request.

Abbreviations

Artificial Intelligence

English as a Foreign Language

English for Specific Purposes

Panel Discussion

Shiraz University of Medical Sciences

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Nasiri, E., Khojasteh, L. Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research. BMC Med Educ 24 , 925 (2024). https://doi.org/10.1186/s12909-024-05911-3

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Paramedics’ experiences and observations: work-related emotions and well-being resources during the initial months of the COVID-19 pandemic—a qualitative study

• Henna Myrskykari 1 , 2 &
• Hilla Nordquist 3  

BMC Emergency Medicine volume  24 , Article number:  152 ( 2024 ) Cite this article

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As first responders, paramedics are an extremely important part of the care chain. COVID-19 significantly impacted their working circumstances. We examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic.

This qualitative study utilized reflective essay material written by experienced, advanced-level Finnish paramedics ( n  = 30). The essays used in this study were written during the fall of 2020 and reflected the period when Finland had declared a state of emergency (on 17.3.2020) and the Emergency Powers Act was implemented. The data was analyzed using an inductive thematic analysis.

The emotions experienced by the EMS personnel in their new working circumstances formed three themes: (1) New concerns arose that were constantly present; (2) Surviving without proper guidance; and (3) Rapidly approaching breaking point. Three themes were formed from work-related factors that were identified as resources for the well-being of the EMS personnel. These were: (1) A high level of organizational efficiency was achieved; (2) Adaptable EMS operations; and (3) Encouraging atmosphere.

Conclusions

Crisis management practices should be more attentive to personnel needs, ensuring that managerial and psychological support is readily available in crisis situations. Preparedness that ensures effective organizational adaptation also supports personnel well-being during sudden changes in working circumstances.

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At the onset of the COVID-19 pandemic, healthcare personnel across the globe faced unprecedented challenges. As initial responders in emergency healthcare, paramedics were quickly placed at the front lines of the pandemic, dealing with a range of emergencies in unpredictable conditions [ 1 ]. The pandemic greatly changed the everyday nature of work [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Those working on the front line were suddenly forced to adjust to personal protective equipment (PPE) requirements [ 9 , 10 ] and rapidly changing instructions that caused significant adjustments to their job description [ 11 , 12 ]. For instance, it has been reported that during the initial stages of the COVID-19 pandemic, Emergency Medical Services (EMS) personnel, including paramedics working in prehospital emergency care, experienced a significant increase in stress [ 10 , 13 ] due to several reasons, such as the lack of protection and support, increased demands, lack of personnel, fear of exposure to COVID-19 during missions, concerns of spreading the virus to family members, and frustration over quickly changing work policies [ 11 , 14 , 15 ].

With the unprecedented challenges posed by the COVID-19 pandemic, some research has been directed toward identifying available resources that help in coping with such situations. For example, Sangal et al. [ 15 ] underscored the association between effective communication and reduced work stress and burnout, and emphasized the critical need for two-way communication, consistent messaging, and the strategic consolidation of information prior to its dissemination. In parallel, Dickson et al. [ 16 ] highlight the pivotal role of leadership strategies in fostering a healthful work environment. These strategies include being relationally engaging, visibly present, open, and caring for oneself and others, while embodying core values such as compassion, empathy, courage, and authenticity. Moreover, Awais et al. [ 14 ] identify essential measures to reduce mental distress and support EMS personnel’s overall well-being in pandemic conditions, such as by providing accessible mental health and peer support, ensuring a transparent information flow, and the implementation of clear, best-practice protocols and guidelines. As a lesson learned from COVID-19, Kihlström et al. (2022) add that crisis communication, flexible working conditions, compensation, and allowing for mistakes should be part of crisis management. They also emphasize the importance of psychological support for employees. [ 12 ]

Overall, the COVID-19 pandemic had a multifaceted impact on EMS personnel, highlighting the necessity for comprehensive support and resilience strategies to safeguard their well-being [ 11 , 17 , 18 ] alongside organizational functions [ 12 , 19 ]. For example, in Finland, it has been noted in the aftermath of COVID-19 that the availability and well-being of healthcare workers are key vulnerabilities of the resilience of the Finnish health system [ 12 ]. Effective preparedness planning and organizational resilience benefit from learning from past events and gaining a deeper understanding of observations across different organizational levels [ 12 , 19 , 20 ]. For these reasons, it is important to study how the personnel experienced the changing working circumstances and to recognize the resources, even unexpected ones, that supported their well-being during the initial phase of the COVID-19 pandemic [ 12 , 19 ].

The aim of this study was to examine the emotions experienced and the resources identified as supportive of work well-being during the initial months of the COVID-19 pandemic, from the perspective of the paramedics. Our research questions were: According to the experiences and observations of paramedics, (1) what kinds of emotions did the EMS personnel experience in the new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic? In this study, emotions are understood as complex responses involving psychological, physiological, and behavioral components, triggered by significant events or situations [ 21 ]. Resources are understood as physical, psychological, social, or organizational aspects of the work that help achieve work goals, reduce demands and associated costs [ 22 ].

Materials and methods

This qualitative study utilized reflective essay material written in the fall of 2020 by experienced, advanced-level paramedics who worked in the Finnish EMS during the early phase of the pandemic, when Finland had declared (March 17, 2020 onward) a state of emergency and implemented the Emergency Powers Act. This allowed for new rules and guidelines from the government to ensure the security of healthcare resources. Some work rules for healthcare personnel changed, and non-urgent services were limited.

Data collection procedures

This study is part of a broader, non-project-based research initiative investigating the work well-being of paramedics from various perspectives, and the data was collected for research purposes from this standpoint. The data collection for this study was conducted at the South-Eastern Finland University of Applied Sciences as part of the Current Issues in EMS Management course. The course participants were experienced, advanced-level Finnish paramedics who were students of the master’s degree program in Development and Management of Emergency Medical Services. A similar data collection method has been utilized in other qualitative studies [for example, 23 , 24 ].

The South-Eastern Finland University of Applied Sciences granted research permission for the data collection on August 20, 2020. The learning platform “Learn” (an adapted version of Moodle [ 25 ]) was used to gather the data. A research notice, privacy statement, and essay writing instructions were published on the platform on August 21, 2020. The paramedics were asked to write about their own experiences and observations regarding how the state of emergency impacted the work well-being of EMS personnel. They were instructed not to use references but only their own reflections. Three guiding questions were asked: “What kind of workloads did EMS personnel experience during the state of emergency?” “How has this workload differed from normal conditions?” and “What effects did this workload have on the well-being of the EMS personnel?”. The assignment did not refer solely to paramedics because the EMS field community may also include individuals with other titles (such as EMS field supervisors or firefighters performing prehospital emergency care); hence the term “EMS personnel” was used.

The essay was part of the mandatory course assignments, but submitting it for research purposes was voluntary. The paramedics were informed that their participation in the study would not affect their course evaluations. They had the freedom to decline, remove parts of, or withdraw the essay before analysis. None of the paramedics exercised these options. They were also informed that the last author removes any identifying details (such as names, places, and organizational descriptions that could reveal their workplace) before sharing the data with other, at the time unnamed, researchers. The last author (female) is a senior researcher specializing in EMS and work well-being topics, a principal lecturer of the respective course, and the head of the respective master’s program, and familiar to all of them through their studies. The paramedics were aware that the essays were graded by the last author on a pass/fail scale as part of the course assessment. However, comprehensive and well-reasoned reflections positively influenced the course grade. The evaluation was not part of this study. The paramedics had the opportunity to ask further questions about the study directly from the last author during and after the essay writing process and the course.

The paramedics wrote the essays between August 23, 2020, and November 30, 2020. Thirty-two paramedics (out of 39) returned their essays using the Learn platform during this timeframe. Thus, seven of the course completions were delayed, and the essays written later were no longer appropriate to include in the data due to the time elapsed since the initial months of the COVID-19 pandemic.

All 32 gave their informed consent for their essays to be included in the study. Essays written by paramedics who had not actively participated in EMS field work during exceptional circumstances were excluded from the material ( n  = 2), because they wrote the essay from a different perspective, as they could not reflect on their own experiences and observations. Thus, a total of 30 essays were included in the study. The total material was 106 pages long and comprised 32,621 words in Finnish.

Study participants

Thirty advanced-level paramedics from Finland participated in this study. They all had a bachelor’s degree in emergency care or nursing with additional emergency care specialization. At the time of the study, they were pursuing their master’s studies. Thirteen of them were women, and seventeen were men. The average age of the participants was 33.5 years among women and 35.9 years among men. Women had an average of 8.7 years of work experience, and men had 8.8 years. All the participating paramedics worked in EMS in different areas across Finland (except northern Finland) during their studies and the early phase of the pandemic.

Data analysis

The data was analyzed with a thematic analysis following the process detailed by Braun & Clarke [ 26 ]. First, the two researchers thoroughly familiarized themselves with the data, and the refined aim and research questions of the study were formulated inductively in collaboration based on the content of the data (see [ 26 ], page 84). After this, a thorough coding process was mainly carried out by the first author (female), who holds a master’s degree, is an advanced-level paramedic who worked in EMS during the pandemic, and at the time of the analysis was pursuing her doctoral studies in a different subject area related to EMS. Generating the initial codes involved making notes of interesting features of anything that stood out or seemed relevant to the research question systematically across the entire dataset. During this process, the original paragraphs and sentences were copied from the essay material into a table in Microsoft Word, with each research question in separate documents and each paragraph or sentence in its own row. The content of these data extracts was then coded in the adjacent column, carefully preserving the original content but in a more concise form. Then, the content was analyzed, and codes were combined to identify themes. After that, the authors reviewed the themes together by moving back and forth between the original material, the data in the Word documents, and the potential themes. During this process, the authors worked closely and refined the themes, allowing them to be separated and combined into new themes. For example, emotions depicting frustration and a shift to indifference formed their own theme in this kind of process. Finally, the themes were defined into main, major and minor themes and named. In the results, the main themes form the core in response to the research questions and include the most descriptions from the data. The major themes are significant but not as central as the main themes. Major themes provide additional depth and context to the results. One minor theme was formed as the analysis process progressed, and it provided valuable insights and details that deepened the response to the research question. All the coded data was utilized in the formed themes. The full content of the themes is reported in the Results section.

The emotions experienced by the EMS personnel in their new working circumstances formed three themes: New concerns arose that were constantly present (main theme); Surviving without proper guidance (major theme); and Rapidly approaching breaking point (major theme) (Fig.  1 ). Work-related factors identified as resources for the well-being of EMS personnel formed three themes: A high level of organizational efficiency was achieved (main theme); Adaptable EMS operations (major theme); and Encouraging atmosphere (minor theme) (Fig.  2 ).

Emotions experienced by the EMS personnel in their new working circumstances

Main theme: New concerns arose that were constantly present

The main theme included several kinds of new concerns. In the beginning, the uncertainty about the virus raised concerns about work safety and the means to prevent the spread of the disease. The initial lack of training and routines led to uncertainty. In addition, the decrease in the number of EMS missions raised fears of units being reduced and unilateral decisions by the management to change the EMS personnel’s work responsibilities. The future was also a source of uncertainty in the early stages. For example, the transition to exceptional circumstances, concerns about management and the supervisors’ familiarity with national guidelines and lack of information related to sickness absence procedures, leave, personal career progression, and even the progress of vaccine development, all contributed to this feeling of uncertainty. The initial uncertainty was described as the most challenging phase, but the uncertainty was also described as long-lasting.

Being on the front line with an unknown, potentially dangerous, and easily transmissible virus caused daily concerns about the personnel’s own health, especially when some patients hid their symptoms. The thought of working without proper PPE was frightening. On the other hand, waiting for a patient’s test result was stressful, as it often resulted in many colleagues being quarantined. A constant concern for the health of loved ones and the fear of contracting the virus and unknowingly bringing it home or transmitting it to colleagues led the EMS personnel to change their behavior by limiting contact.

Being part of a high-risk group , I often wondered , in the case of coronavirus , who would protect me and other paramedics from human vanity and selfishness [of those refusing to follow the public health guidelines]? (Participant 25)

The EMS personnel felt a weight of responsibility to act correctly, especially from the perspective of keeping their skills up to date. The proper selection of PPE and aseptic procedures were significant sources of concern, as making mistakes was feared to lead to quarantine and increase their colleagues’ workloads. At the same time, concerns about the adequacy of PPE weighed on the personnel, and they felt pressure on this matter to avoid wastage of PPEs. The variability in the quality of PPE also caused concerns.

Concerns about acting correctly were also tied to ethical considerations and feelings of inadequacy when the personnel were unable to explain to patients why COVID-19 caused restrictions on healthcare services. The presence of students also provoked such ethical concerns. Recognizing patients’ symptoms correctly also felt distressing due to the immense responsibility. This concern was also closely tied to fear and even made some question their career choices. The EMS personnel were also worried about adequate treatment for the patients and sometimes felt that the patients were left alone at home to cope. A reduction in patient numbers in the early stages of the pandemic raised concerns about whether acutely ill individuals were seeking help. At the same time, the time taken to put on PPE stressed the personnel because it increased delays in providing care. In the early phase of the pandemic, the EMS personnel were stressed that patients were not protected from them.

I’m vexed in the workplace. I felt it was immediately necessary to protect patients from us paramedics as well. It wasn’t specifically called for , mostly it felt like everyone had a strong need to protect themselves. (Participant 30)

All these concerns caused a particularly heavy psychological burden on some personnel. They described feeling more fatigued and irritable than usual. They had to familiarize themselves with new guidelines even during their free time, which was exhausting. The situation felt unjust, and there was a looming fear of the entire healthcare system collapsing. COVID-19 was omnipresent. Even at the base station of the EMS services, movement was restricted and social distancing was mandated. Such segregation, even within the professional community, added to the strain and reduced opportunities for peer support. The EMS personnel felt isolated, and thoughts about changing professions increased.

It was inevitable that the segregation of the work community would affect the community spirit , and a less able work community has a significant impact on the individual level. (Participant 8)

Major theme: Surviving without proper guidance

At the onset of the pandemic, the job description of the EMS personnel underwent changes, and employers could suddenly relocate them to other work. There was not always adequate support for familiarizing oneself with the new roles, leading to a feeling of loss of control. The management was described as commanding and restricting the personnel’s actions. As opportunities to influence one’s work diminished, the sense of job satisfaction and motivation decreased.

Some felt that leadership was inadequate and neglectful, especially when the leaders switched to remote work. The management did not take the situation seriously enough, leaving the EMS personnel feeling abandoned. The lack of consistent leadership and failure to listen to the personnel caused dissatisfaction and reduced occupational endurance. In addition, the reduced contact with colleagues and close ones reduced the amount of peer support. The existing models for psychological support were found to be inadequate.

Particularly in the early stages, guidelines were seen as ambiguous and deficient, causing frustration, irritation, and fear. The guidelines also changed constantly, even daily, and it was felt that the information did not flow properly from the management to the personnel. Changes in protection recommendations also led to skepticism about the correctness of the national guidance, and the lack of consistent guidelines perplexed the personnel. Internalizing the guidelines was not supported adequately, but the necessity to grasp new information was described as immense and cognitively demanding.

At times , it felt like the work was a kind of survival in a jungle of changing instructions , one mission at a time. (Participant 11)

Major theme: Rapidly approaching breaking point

Risking one’s own health at work caused contentious feelings while concurrently feeling angry that management could work remotely. The arrogant behavior of people toward COVID-19 left them frustrated, while the EMS personnel had to limit their contacts and lost their annual leave. There were fears about forced labor.

Incomplete and constantly changing guidelines caused irritation and indifference, as the same tasks had to be performed with different levels of PPE within a short time. Some guidelines were difficult to comply with in practice, which was vexing.

Using a protective mask was described as distressing, especially on long and demanding missions. Communication and operation became more difficult. Some described frustration with cleaning PPE meant for single use.

Ensuring the proper implementation of a work pair’s aseptic and equipment maintenance was burdensome, and explaining and repeating guidelines was exhausting. A feeling of indifference was emphasized toward the end of a long shift.

After the initial stage, many began to slip with the PPE guidelines and found the instructions excessive. COVID-19 information transmitted by the emergency center lost its meaning, and instructions were left unheeded, as there was no energy to believe that the patient would have COVID-19, especially if only a few disease cases had been reported in their area.

It was disheartening to hear personnel being labeled as selfish for demanding higher pay during exceptional circumstances. This lack of recognition eroded professionalism and increased thoughts of changing professions.

However , being a doormat and a human toilet , as well as a lack of appreciation , undermines my professionalism and the prolonged situation has led me to seriously consider a different job , where values other than dedication and constant flexibility carry weight. I have heard similar thoughts from other colleagues. None of us do this for money. (Participant 9)

Work-related factors identified as resources for the well-being of EMS personnel

Main theme: A high level of organizational efficiency was achieved

The main theme held several different efficient functions. In the early stages of the pandemic, some felt that the information flow was active. Organizations informed the EMS personnel about the disease, its spread, and its impact on the workplace and emergency care activities.

Some felt that managers were easily accessible during the pandemic, at least remotely. Some managers worked long days to be able to support their personnel.

The response to hate and uncertainty was that one of the supervisors was always present in the morning and evening meetings. Supervisors worked long hours so as to be accessible via remote access. (Participant 26)

The organizations took effective steps to control infections. Quick access to COVID-19 tests, clear guidelines for taking sick leave, and permission to take sick leave with a low threshold were seen as positive things. The consideration of personnel belonging to risk groups by moving them to other work tasks was also perceived as positive. In addition, efforts were made to prevent the emergence of infection chains by isolating EMS personnel in their own social facilities.

Established guidelines, especially on the correct use of protective measures, made it easier to work. Some mentioned that the guidelines were available in ambulances and on phones, allowing the protection guidelines to be checked before going on a mission.

The employers took into account the need for psychological support in a diverse manner. Some organizations provided psychological support such as peer debriefing activities, talking therapy with mental health professionals, actively inquiring about their personnel’s feelings, and training them as support workers. The pandemic situation also caused organizations to create their own standard operating models to decrease mental load.

Fortunately , the problem has now been addressed actively , as a peer-to-peer defusing model was built up at our workplace during the crisis , and group defusing has started , the purpose of which is to lighten the work-related mental load. (Participant 3)

Major theme: Adaptable EMS operations

There were several different resources that clarified mission activities. The amount of protective and cleaning equipment was ramped up, and the treatment equipment was quickly updated to meet the demands brought about by the pandemic and to enable safety distances for the EMS personnel. In addition, various guidelines were amended to reduce exposure. For example, personnel on the dedicated COVID-19 ambulances were separated to work without physical contact with others, and field supervisors joined the EMS missions less often than before. Moreover, people at the scene were contacted by phone in advance to ensure that there would be no exposure risk, which also allowed other occupational safety risks to be identified. New practices resulted from the pandemic, such as cleaning communication equipment during shift changes and regularly using PPE with infected patients. All of these were seen as positive resources for efficient work.

At the end of each shift , all keys , telephones , etc., were cleaned and handed over to the next shift. This practice was not previously established in our area , but this will become a permanent practice in the future and is perceived by everyone in our work community as a positive thing. (Participant 10)

Some stated that access to PPE was sufficient, especially in areas where the number of COVID-19 infections was low. PPE was upgraded to make it easier to wear. Further, organizations acquired a variety of cleaning equipment to speed up the disinfection of ambulances.

Organizations hired more employees to enable leave and the operation of dedicated COVID-19 ambulances. The overall number of ambulances was also increased. Non-urgent missions were handled through enhanced phone services, reducing the unnecessary exposure of EMS personnel to COVID-19.

Five extra holiday substitutes were hired for EMS so that the employer could guarantee the success of agreed leave , even if the Emergency Preparedness Act had given them opportunities to cancel or postpone it. (Participant 12)

Minor theme: Encouraging atmosphere

Peer support from colleagues, a positive, comfortable, pleasant work environment, and open discussion, as well as smooth cooperation with other healthcare employees were felt to be resources for work well-being by reducing the heavy workload experienced. Due to the pandemic, the appreciation of healthcare was felt to increase slightly, which was identified as a resource.

One factor affecting resilience in the healthcare sector is certainly that in exceptional circumstances , visibility and appreciation have somewhat increased. (Participant 23)

This study examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic. Each research question was answered with three themes.

Previous studies have shown that the pandemic increased the workload of paramedics, prompting changes in their operating models and the function of EMS to align with new pandemic-related requirements [ 9 , 27 ]. Initially, the paramedics in the current study described facing unclear and deficient guidelines and feeling obligated to follow instructions without adequate support to internalize them. Constantly changing instructions were linked to negative emotions in various ways. Moreover, the overwhelming flood of information was heavily connected to this, although the information flow was also perceived as a resource, especially when it was timely and well-structured. The study by Sangal et al. [ 15 ] has raised similar observations and points out the importance of paying special attention to the personnel working in the frontline, as in EMS, who might be more heavily impacted by too much information and anxiety about it. They also discovered that three factors are crucial for addressing the challenges of information overload and anxiety: consolidating information before distributing it, maintaining consistent communication, and ensuring communication is two-way. McAlearney et al. [ 11 ] found that first responders, including EMS personnel, reported frustration regarding COVID-19 information because of inconsistencies between sources, misinformation on social media, and the impact of politics. A Finnish study also recognized that health systems were not sufficiently prepared for the flood of information in the current media environment [ 12 ]. Based on these previous results and our findings, it can be concluded that proper implementation of crisis communication should be an integral part of organizations’ preparedness in the future, ensuring that communication effectively supports employee actions in real-life situations. Secondly, this topic highlights the need for precise guidelines and their implementation. With better preparedness, similar chaos could be avoided in the future [ 17 ].

Many other factors also caused changes in work. The EMS mission profile changed [ 3 , 4 , 5 , 6 ], where paramedics in this study saw concerns. To prevent infection risk, the number of pre-arrival calls increased [ 7 ], the duration of EMS missions increased [ 8 , 9 ], and the continuous use of PPE and enhanced hygiene standards imposed additional burdens [ 9 , 10 ]. In Finland, there was no preparedness for the levels of PPE usage required in the early stages of the pandemic [ 12 ]. In this study, paramedics described that working with potentially inadequate PPE caused fear and frustration, which was increased by a lack of training, causing them to feel a great deal of responsibility for acting aseptically and caring for patients correctly. Conversely, providing adequate PPE, information and training has been found to increase the willingness to work [ 28 ] and the sense of safety in working in a pandemic situation [ 29 ], meaning that the role of precise training, operating instructions and leadership in the use of PPE is emphasized [ 30 ].

The paramedics in this study described many additional new concerns in their work, affecting their lives comprehensively. It has been similarly described that the pandemic adversely affected the overall well-being of healthcare personnel [ 31 ]. The restrictions implemented also impacted their leisure time [ 32 ], and the virus caused concerns for their own and their families’ health [ 11 , 28 ]. In line with this, the pandemic increased stress, burnout [ 10 , 33 ], and anxiety among EMS personnel and other healthcare personnel working on the frontline [ 11 , 14 , 34 , 35 ]. These kinds of results underscore the need for adequate guidance and support, a lack of which paramedics reported experiencing in the current study.

Personnel play a crucial role in the efficient operation of an organization and comprise the main identified resource in this study. Previous studies and summaries have highlighted that EMS personnel did not receive sufficient support during the COVID-19 pandemic [ 11 , 14 , 17 , 18 ]. Research has also brought to light elements of adequate support related to the pandemic, such as a review by Dickson et al. [ 16 ] that presents six tentative theories for healthful leadership, all of which are intertwined with genuine encounter, preparedness, and information use. In this current study, the results showed numerous factors related to these contexts that were identified as resources, specifically underlined by elements of caring, effective operational change, knowledge-based actions, and present leadership, similarly described in a study by Eaton-Williams & Williams [ 18 ]. Moreover, the paramedics in our study highlighted the importance of encouragement and identified peer support from colleagues as a resource, which is in line with studies in the UK and Finland [ 12 , 23 , 37 ].

In the early stages of the pandemic, it was noted that the EMS personnel lacked adequate training to manage their mental health, and there was a significant shortage of psychosocial support measures [ 14 ], although easy access to support would have been significant [ 18 ]. In the current study, some paramedics felt that mental health support was inadequate and delayed, while others observed an increase in mental health support during the pandemic, seeing it as an incentive for organizations to develop standard operating models for mental support, for example. This awakening was identified as a resource. This is consistent, as providing psychological support to personnel has been highlighted as a core aspect of crisis management in a Finnish study assessing health system resilience related to COVID-19 [ 12 ]. In a comprehensive recommendation commentary, Isakov et al. [ 17 ] suggest developing a national strategy to improve resilience by addressing the mental health consequences of COVID-19 and other occupational stressors for EMS personnel. This concept, applicable beyond the US, supports the view that EMS organizations are becoming increasingly aware of the need to prepare for and invest in this area.

A fundamental factor likely underlying all the described emotions was that changes in the job descriptions of the EMS personnel due to the pandemic were significant and, in part, mandated from above. In this study, paramedics described feelings of concern and frustration related to these many changes and uncertainties. According to Zamoum and Gorpe (2018), efficient crisis management emphasizes the importance of respecting emotions, recognizing rights, and making appropriate decisions. Restoring trust is a significant challenge in a crisis situation, one that cannot be resolved without complete transparency and open communication [ 38 ]. This perspective is crucial to consider in planning for future preparedness. Overall, the perspective of employee rights and obligations in exceptional circumstances has been relatively under-researched, but in Australia, grounding research on this perspective has been conducted with paramedics using various approaches [ 39 , 40 , 41 ]. The researchers conclude that there is a lack of clarity about the concept of professional obligation, specifically regarding its boundaries, and the issue urgently needs to be addressed by developing clear guidelines that outline the obligation to respond, both in normal day-to-day operations and during exceptional circumstances [ 39 ].

Complex adaptive systems (CAS) theory recognizes that in a resilient organization, different levels adapt to changing environments [ 19 , 20 ]. Barasa et al. (2018) note that planned resilience and adaptive resilience are both important [ 19 ]. Kihlström et al. (2022) note that the health system’s resilience was strengthened by a certain expectation of crisis, and they also recognized further study needs on how effectively management is responding to weak signals [ 12 ]. This could be directly related to how personnel can prepare for future changes. The results of this study revealed many negative emotions related to sudden changes, but at the same time, effective organizational adaptation was identified as a resource for the well-being of EMS personnel. Dissecting different elements of system adaptation in a crisis has been recognized as a highly necessary area for further research [ 20 ]. Kihlström et al. (2022) emphasize the importance of ensuring a healthy workforce across the entire health system. These frameworks suggest numerous potential areas for future research, which would also enhance effective preparedness [ 12 ].

Limitations of the study

In this study, we utilized essay material written in the fall of 2020, in which experienced paramedics reflected on the early stages of the COVID-19 pandemic from a work-oriented perspective. The essays were approached inductively, meaning that they were not directly written to answer our research questions, but the aim and the research questions were shaped based on the content [ 26 ]. The essays included extensive descriptions that aligned well with the aim of this study. However, it is important to remember when interpreting the results that asking specifically about this topic, for instance, in an interview, might have yielded different descriptions. It can be assessed that the study achieved a tentative descriptive level, as the detailed examination of complex phenomena such as emotions and resources would require various methods and observations.

Although the essays were mostly profound, well-thought-out, and clearly written, their credibility [ 42 ] may be affected by the fact that several months had passed between the time the essays were written and the events described. Memories may have altered, potentially influencing the content of the writings. Diary-like material from the very onset of the pandemic might have yielded more precise data, and such a data collection method could be considered in future research on exceptional circumstances.

The credibility [ 42 ] could also have been enhanced if the paramedics who wrote the essays had commented on the results and provided additional perspectives on the material and analysis through a multi-phase data collection process. This was not deemed feasible in this study, mainly because there was a 2.5-year gap between data collection and the start of the analysis. However, this also strengthened the overall trustworthiness of the study, as it allowed the first author, who had worked in prehospital emergency care during the initial phase of the pandemic, to maintain a distance from the subject, and enabled a comparison of our own findings with previously published research that investigated the same period in different contexts. The comparison was made when writing the discussion, with the analysis itself being inductive and following the thematic analysis process described by Braun & Clarke [ 26 ].

When evaluating credibility [ 42 ], it should also be noted that the participants who wrote the essays, i.e., the data for the study, were experienced paramedics but also students and one of the researchers was their principal lecturer. This could potentially limit credibility if the students, for some reason, did not want to produce truthful content for their lecturer to read. However, this risk can be considered small because the essays’ topics did not concern the students’ academic progress, the essays’ content was quite consistent, and the results aligned with other studies. As a strength, it can be considered that the students shared their experiences without holding back, as the thoughts were not for workplace use, and they could trust the data privacy statement.

To enhance transferability [ 42 ], the context of the study was described in detail, highlighting the conditions prevailing in Finnish prehospital emergency care during the early stages of the pandemic. Moreover, including a diverse range of perspectives from paramedics working in different regions of Finland (except Northern Finland) contributes to the transferability of the study, indicating that the results may be applicable and relevant to a wider context beyond a single specific region.

Dependability [ 42 ] was reinforced by the close involvement of two researchers from different backgrounds in the analysis of the material, but a limitation is that no separate analyses were conducted. However, the original data was repeatedly revisited during the analysis, which strengthened the dependability. Moreover, the first author kept detailed notes throughout the analysis process, and the last author supervised the progress while also contributing to the analysis and reporting. The research process is also reported in detail.

This study highlighted numerous, mainly negative emotions experienced by EMS personnel during the initial months of the COVID-19 pandemic due to new working circumstances. At the same time, several work-related factors were identified as resources for their well-being. The findings suggest that crisis management practices should be more attentive to personnel needs, ensuring that personnel have the necessary support, both managerial and psychological, readily available in crisis situations. Effective organizational adaptation in a crisis situation also supports personnel well-being, emphasizing the importance of effective preparedness. Future research should particularly focus on considering personnel well-being as part of organizational adaptation during exceptional circumstances and utilize these findings to enhance preparedness.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the inclusion of sensitive information and the extent of the informed consent provided by the participants.

Abbreviations

Complex Adaptive Systems (theory)

Coronavirus Disease 2019

Emergency Medical Services

Personal Protective Equipment

United Kingdom

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Acknowledgements

We want to sincerely thank all the paramedics who participated in this study.

Open access funded by Helsinki University Library.

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Henna Myrskykari

Emergency Medical Services, University of Turku and Turku University Hospital, Turku, Finland

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Study design (HM, HN). Data collection (HN). Methodology (HN). Analysis (HM, HN). Writing (HM, HN). Review and editing (HM, HN). Supervision (HN). Both authors read and approved the final manuscript.

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The study followed the good scientific practice defined by the Finnish National Board on Research Integrity TENK [ 43 ]. The study was conducted in accordance with the Helsinki Declaration and applicable national guidelines. Adhering to the Finnish National Board on Research Integrity (TENK) guidelines on ethical principles of research with human participants and ethical review in the human sciences in Finland, an ethical review statement from a human sciences ethics committee was not required for this type of study. The participants consisted of adult students engaged in regular employment. Their involvement in the research was grounded on informed consent. The study did not involve concerns regarding the participants’ physical integrity, nor were they subjected to exceptionally strong stimuli. The potential for causing mental harm was not beyond what is typically encountered in everyday life, and their participation did not pose any safety risks [ 44 ].

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• 23 August 2024

AI analysed 1,500 policies to cut emissions. These ones worked

• Xiaoying You

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Taxes were particularly effective at reducing emissions associated with electricity generation in high-income countries. Credit: Andrew Aitchison/In pictures via Getty

Researchers used machine learning to analyse roughly 1,500 climate policies and identify those that have drastically reduced carbon emissions. Their study, published in Science today, found that policies that combine several tools are more effective in slashing emissions than are stand-alone measures 1 .

The analysis identified 63 interventions in 35 countries that led to significant reductions in emissions, cutting them by 19% on average. Most reductions were linked to two or more policies. Together, the 63 policies cut emissions by between 0.6 and 1.8 gigatonnes (Gt) of CO 2 equivalent.

What’s the best way to tackle climate change? An ‘evidence bank’ could help scientists find answers

Using the right mix of policies is more important than using a lot of policies, says Annika Stechemesser, a co-author and researcher at the Potsdam Institute for Climate Impact Research in Germany. For example, the United Kingdom’s phasing out of coal-fired power stations worked because it was used in tandem with pricing mechanisms, such as a minimum carbon price; in Norway, banning combustion engine cars was most effective when combined with a price incentive that made electric cars cheaper.

“To my knowledge, it is a first-of-its-kind study providing such a global evaluation,” says Jan Minx, an environmental economist with the Mercator Research Institute on Global Commons and Climate Change in Berlin.

Road to reductions

As part of the analysis, Stechemesser and her colleagues used a database of 1,500 climate policies implemented between 1998 and 2022 in 41 countries, including the top three greenhouse gas emitters globally: China, the United States and India . The policies fell into 48 categories, ranging from emission trading schemes to fossil-fuel subsidy reforms.

“Previous evaluations have typically concentrated on a narrow set of prominent policies in selected countries, overlooking the hundreds of other measures,” Stechemesser says.

The authors combined machine learning with a statistical analytical approach to identify large emission reductions in four high-emitting sectors — buildings, electricity, industry and transport. They compared the results with policies in the database to assess which policies and policy combinations led to the biggest emission drops.

“This is a rather clever method,” says Zheng Saina, who has analysed global climate policies at Southeast University in Nanjing, China. The conventional way would have been to review the large number of policies and select the important ones, but that approach is subjective and cumbersome, she adds. “The authors instead used machine learning to detect major emissions changes. It is more objective.”

The results showed that certain policy combinations worked better in specific sectors and economies. In terms of reducing emissions associated with electricity generation, for instance, pricing interventions such as energy taxes were particularly effective in high-income countries, but less so in lower-and-middle income countries.

In the building sector, policy mixes that included phased-out and banned emissions-generating activities more than doubled the reductions resulting from implementing those policies individually.

Taxation was the only policy that achieved nearly equal or larger emission reductions as a stand-alone policy, as opposed to a policy mix, in all four sectors.

Minx says the study’s AI-enhanced approach allowed the researchers, for the first time, to evaluate the effectiveness of a large number of climate policies from a global set of emission inventories covering different countries and sectors.

For other researchers, the paper is alarming. “This study provides a warning to countries around the world that their climate policies have had very limited effects so far,” says Xu Chi, an ecologist at Nanjing University. “Existing polices will need to be re-evaluated, and changes will need to be made,” Xu adds.

The world’s annual emissions are projected to be 15 Gt of CO 2 equivalents higher by 2030 than would be required to keep global warming to less than 2 °C above pre-industrial levels, according to the United Nations.

doi: https://doi.org/10.1038/d41586-024-02717-7

Stechemesser, A. et al. Science 385 , 884–891 (2024).

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• Published: 24 August 2024

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

• Xiaojie Liu 1 , 3 , 4 ,
• Ting Peng 1 , 3 , 4 ,
• Miaochun Xu 1 , 3 , 4 ,
• Shitong Lin 1 , 3 , 4 ,
• Bai Hu 2 , 3 , 4 ,
• Tian Chu 2 , 3 , 4 ,
• Binghan Liu 1 , 3 , 4 ,
• Yashi Xu 1 , 3 , 4 ,
• Wencheng Ding 2 , 3 , 4 ,
• Li Li 2 , 3 , 4 ,
• Canhui Cao 2 , 3 , 4 &
• Peng Wu 1 , 3 , 4  

Journal of Hematology & Oncology volume  17 , Article number:  72 ( 2024 ) Cite this article

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The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Introduction

Single-cell sequencing has been instrumental in providing detailed insights into gene expression at the individual cell level for decades. This technique has revealed the complexity of cellular diversity, exacerbated by processes such as cell proliferation, differentiation, and death, particularly in relation to the local and distant environment of the cell [ 1 ]. Single-cell sequencing can detect cellular heterogeneity, enabling detailed analysis of individual cell behavior, mechanisms, and relationships. The high resolution of these methods has allowed for the extensive exploration and characterization of cell diversity on a large scale. However, despite these advantages, single-cell sequencing often fails to retain critical spatial information about cell populations, resulting in the loss of crucial spatial context [ 1 , 2 ].

To overcome this limitation, spatial multi-omics has emerged as a transformative technology, enabling the precise localization of cells within tissues and the quantitative measurement of gene expression in situ. This advancement marks an important technological breakthrough in life sciences and biomedicine, with wide-ranging applications in neuroscience, developmental biology, and cancer research [ 3 ]. Furthermore, spatial multi-omics allows researchers to investigate the development of multicellular organisms from single totipotent cells, as well as their function, aging, and disease progression. High-throughput multi-omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have also facilitated the mapping of diverse molecular layers, significantly broadening the scope of biological analysis and our understanding of complex biological systems. In the current review, we trace the developmental timeline of spatial multi-omics technologies, highlighting their evolution and substantial contributions to modern science. Furthermore, we discuss the current state of these technologies, their integration into research, and their significant applicative value in enhancing our understanding of biological complexity.

Technologies for spatial omics

Spatial mono-omics, such as spatial transcriptomics, was recognized as the “Technology of the Year 2020” by Nature Methods magazine [ 4 ] (Fig.  1 ). Although single-cell sequencing technology has provided valuable insights into cellular heterogeneity, it lacks spatial context. Spatial multi-omics overcomes this limitation by enabling the precise localization and molecular characterization of individual cells within their tissue environments [ 5 ]. The innovation in spatial multi-omics builds upon foundational spatial mono-omics methods. In this section, we introduce key spatial mono-omics techniques (Table  1 ) and discuss their pivotal role in advancing the field of spatial multi-omics.

Timeline of spatial multi-omics. Transcriptomics, genomics, proteomics, metabolomics, and epigenomics are included. In addition to the frequently used techniques, some emerging methods are mentioned

Spatial transcriptomics

Spatial transcriptomics has significantly enhanced our understanding of cellular organization and intra-tissue interactions based on the systematic measurement of gene expression levels across tissue space. Recent advancements in spatial transcriptomics sequencing have focused on increasing the number of detectable genes or proteins, enhancing sensitivity and resolution, simplifying operation, and expanding the size of the analyzed area. Spatial transcriptomics has been used in various fields, including cancer research [ 6 ], developmental biology [ 7 ], and disease studies [ 8 ]. Fundamentally, spatial transcriptomics technology has the ability to reveal the precise spatial localization of RNA molecules within tissues. In this section, we provide a comprehensive overview of mainstream spatial transcriptomics research strategies and summarize the strengths and limitations of these approaches.

(1) Image-based in situ transcriptomics . Image-based spatial transcriptomics primarily includes fluorescence in situ hybridization (FISH) and in situ sequencing (ISS). Recent advancements feature highly multiplexed single‐molecule FISH (smFISH), which uses reverse complementary oligo probes conjugated with fluorophores [ 9 ] for precise mRNA quantification and localization at the single-cell level [ 10 ]. The specificity of fluorescent probes to their RNA targets is critical for reliable smFISH results [ 11 ]. While smFISH can detect many transcripts due to high hybridization efficiency, signal overlap complicates barcode deconvolution. To address this issue, single-molecule imaging and multiplexed error-robust FISH (MERFISH) (Fig.  2 A) have been developed, allowing the identification of thousands of RNA species in single cells by reducing optical crowding, albeit at the cost of increased imaging rounds and time [ 12 ]. Sequential FISH (seqFISH) [ 13 , 14 ] (Fig.  2 B), an in situ three-dimensional (3D) multiplexed imaging method, also addresses optical crowding by decreasing the number of transcripts per image, requiring additional imaging rounds. Despite these advancements, smFISH is limited by the spectral overlap of fluorophores, restricting its multiplexing capabilities and its effectiveness in analyzing cell heterogeneity in complex tissues [ 15 ]. For example, Long et al. utilized seqFISH to analyze the hippocampus, identifying distinct transcriptional states by quantifying and clustering 249 genes in 16,958 cells [ 14 ], thereby demonstrating the effectiveness of this method for detailed transcriptional profiling in complex tissues.

Technologies of spatial techniques. A The MERFISH technology, a binary barcode scheme that employs different fluorescent probes to sequentially detect each bit. B The seqFISH technology. Complete RNA in cells/tissues was imaged by multiple rounds of hybridization. Each round obtains a coded message, corresponding to a bit in the digital code, and then decodes it to correspond to each RNA. C FISSEQ incorporates amplification after reverse transcription of cellular RNA into cDNA. D STARmap is based on DNA tandem sequencing technology, using complementary pairing principle of DNA and fluorescent dye labeled nucleotide probe for sequence determination. E LCM-seq utilizes a laser beam to microdissect tissue regions under a microscope. F IGS combines in situ sequencing with high-throughput paired-end DNA sequencing. G Slide-DNA-seq is used to fragment genomic DNA in situ by tissue, and barcode connector with spatial information is added for subsequent second-generation sequencing. H CUT and Tag guides Protein A/G-Tn5 transposase to cut the target chromatin region through protein-specific antibodies such as transcription factors. At the same time, sequencing joints are added to both ends of the sequence to form a library for high-throughput sequencing by PCR amplification

Both ISH and ISS provide similar transcriptomic information, with the primary difference being that ISS-based methods directly read nucleotide sequences within tissues to identify a larger number of RNA‐targeting probes, while ISH-based methods image the sequences of barcoded FISH probes [ 9 ]. As a targeted spatial transcriptomics technology, ISS facilitates highly multiplexed in situ gene expression profiling through padlock probes, rolling circle amplification (RCA), and sequencing-by-ligation [ 16 , 17 ] chemistry combined with next-generation sequencing chemistry [ 18 ]. In ISS, reverse transcribed cDNA is hybridized with padlock probes containing gene-specific barcode sequences, which are ligated at the specific hybridization site and amplified by rolling circle amplification (RCA) with a circularized padlock primer probe [ 9 ]. Chatarina et al. developed a method that combines padlock probes with in situ target-primed rolling-circle amplification to detect and genotype individual transcripts, offering deeper insights into mRNA expression heterogeneity within single-cell populations [ 19 ]. Sequential imaging using sequencing-by-ligation allows for the identification of repeatedly amplified barcode sequences in situ, while fluorescent in situ sequencing (FISSEQ) (Fig.  2 C) employs an oligonucleotide ligation and detection substrate (SOLiD) for genome and transcriptome sequencing of DNA amplicons [ 9 , 17 ]. FISSEQ experiences fewer issues with optical crowding compared to ISH-based methods because it is less efficient at converting transcripts into cDNA in situ. However, methods that use padlock probes hybridized with target RNA species require enzyme ligations and have lower detection rates compared to multiplexed FISH methods [ 12 ]. Next-generation FISSEQ [ 20 ] was developed to complement spatially structured sequencing libraries and includes an imaging method capable of resolving amplicons, which is essential for conducting ISS of cellular RNA for gene expression profiling [ 17 ]. RNA is reverse transcribed in fixed cells with tagged random hexamers to generate cDNA amplicons within the cell, which can be repeatedly hybridized with minimal changes in signal-to-noise ratios or position [ 21 ]. RNA sequencing libraries can be visualized in different cell types, tissue sections, and whole-mount embryos, enabling 3D visualization spanning multiple resolution scales [ 17 ]. Spatially resolved transcript amplicon readout mapping (STARmap) [ 22 , 23 ] (Fig.  2 D) employs dynamic annealing and ligation (SEDAL) to reduce sequencing errors. This technology integrates hydrogel tissue chemistry, targeted signal amplification, and ISS [ 22 ], enabling high multiplexing and analysis of thicker tissue slices, although it may detect fewer transcripts in such slices [ 24 ]. BaristaSeq, an optimized padlock probe-based technique compatible with Illumina sequencing, significantly enhances amplification efficiency and sequencing accuracy, achieving at least 97% accuracy and a five-fold increase in amplification efficiency [ 25 ].

Both ISS and ISH-based methods require image processing to generate gene expression matrices. These images are segmented to create cell-level matrices, which can be done manually for small areas or systematically using computational approaches [ 3 , 26 ]. RNA hybridization-based spatial transcriptomics provides exceptional detection sensitivity [ 27 ]; however, the misassignment of mRNAs during cell segmentation is a significant source of error. To address this, the JSTA computational framework utilizes prior knowledge of cell type-specific gene expression to perform joint cell segmentation and cell type annotation, increasing the accuracy of RNA assignment by over 45% [ 28 ]. Spot-based spatial cell-type analysis by multidimensional mRNA density estimation (SSAM) is a robust cell segmentation-free computational framework that identifies cell types and tissue domains in both 2D and 3D [ 29 ].

(2) Oligonucleotide-based spatial barcoding followed by the next-generation sequencing (NGS) [ 20 ]. NGS represents a significant improvement over previous sequencing technologies, offering cost-effective, rapid sequencing with higher throughput, thereby greatly extending our genomic knowledge [ 30 ] and addressing the time and resource-intensive challenges faced by the Human Genome Project [ 31 ]. NGS technologies introduce three main improvements over first-generation sequencing. First, they rely on the preparation of NGS libraries in a cell-free system, eliminating the need for bacterial cloning of DNA fragments [ 32 ]. Second, numerous sequencing reactions are produced in parallel, enhancing efficiency [ 33 ]. Third, sequencing outputs are detected directly, with base interrogation performed cyclically and in parallel [ 34 ]. Several prominent NGS platforms have emerged, including 454 (pyrosequencing) [ 35 ], Illumina/Solexa, and Sequencing by Oligo Ligation Detection (SOLiD) [ 36 ]. The 454 approach involves the clonal amplification of DNA fragments on beads within emulsion droplets, which are then loaded into wells for sequencing using the pyrosequencing protocol [ 37 ]. This approach enables the sequencing of long reads, making it suitable for various applications, although its inherent problem in detecting homopolymers and nucleotide stretches can impact data quality as sequence volume increases [ 38 ]. Illumina/Solexa employs an array-based DNA sequencing-by-synthesis technology with reversible terminator chemistry [ 39 ]. Primers, DNA polymerase, and four differently labeled reversible terminator nucleotides are used, with each nucleotide identified by color, followed by terminator and fluorophore removal, and the cycle repeating [ 34 ]. This platform currently offers the highest throughput and lowest per-base cost, making it the leading NGS platform. In contrast, the SOLiD platform prepares sequencing libraries by emulsion polymerase chain reaction (PCR) and sequences through successive cycles of ligation [ 39 ], exhibiting the lowest error rate among the three platforms. However, NGS methods have several drawbacks, notably short reads that fail to cover full-length transcripts in eukaryotic genomes and challenges in detecting larger structural variations. Additionally, the reliance on PCR amplification can lead to difficulties in regions with extreme GC content [ 40 ]. The advent of single-molecule, third-generation sequencing technologies, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), has resolved these issues. PacBio and ONT offer read lengths exceeding 15 kb and 30 kb, respectively, surpassing the length necessary to capture most RNA molecules in eukaryotes. Furthermore, ONT long-read sequencing does not require PCR amplification, thereby reducing potential bias [ 41 , 42 ].

(3) Laser capture microdissection (LCM) . A key challenge in transcriptomics is precise segmentation of tissues and accurate assignment of individual cells to specific locations, often resulting in the loss of spatial information [ 12 ]. LCM (Fig.  2 E), a powerful, microscope-guided cutting system that uses ultraviolet (UV) light as a contact- and contamination-free knife [ 43 ], enables accurate isolation of specific tissues or cells of interest from complex tissue structures. The combination of smart-3SEQ and LCM overcomes various experimental design challenges posed by conventional single-cell RNA-sequencing (scRNA-seq). For instance, formalin-fixed, paraffin-embedded archival clinical tissues, which are unsuitable for conventional RNA-seq due to their inability to be physically dissociated, and fresh or frozen non-archival tissue samples that lack sufficient material for clinical studies can be effectively analyzed using the LCM smart-3SEQ technique [ 11 ].

Spatial genomics

The proper functioning of tissues relies on the precise spatial organization of cell types, which is influenced by both intrinsic genetic factors and the external cellular environment. In cancer, tumor cells exhibit multiple DNA mutations and large chromosomal rearrangements, resulting in intratumor genetic heterogeneity [ 44 ]. Additionally, cells within the tumor microenvironment (TME) interact with each other, forming spatial neighborhoods with distinct biochemical and biomechanical properties. Quantifying these genetic aberrations and environmental cues within tumors is critical for understanding cancer progression and improving treatment [ 45 ]. In situ genome sequencing (IGS) (Fig.  2 F) and slide-DNA-seq (Fig.  2 G) are two exciting methods that promise to fuel the spatial genomics revolution [ 46 ]. IGS expands non-targeted genomic samples in a natural spatial environment, creating an in situ sequencing library in a fixed sample using in vitro transposon technology to fragment DNA. Hairpin DNA splices are then connected to DNA fragments to form circular DNA, which is amplified via rolling circle replication mediated by Phi29 DNA polymerase. Sequencing is performed at both in situ and ectopic sites on the circular DNA fragments [ 47 ]. IGS spatially locates paired-end sequences of the whole genome in an endogenous environment, combining sequencing and imaging to construct a genome map [ 47 ]. This technique specializes in high-resolution imaging of chromosome structure, allowing detailed analysis of tissue sections. Slide-DNA-seq enables spatially resolved sequencing of DNA from intact tissues. The process begins with generating a spatial index array composed of 3 mm beads, each containing unique DNA barcodes corresponding to specific spatial locations. This array is then read through chemical sequencing [ 48 ]. Next, a single 10 μm thick fresh-frozen tissue slice is transferred onto the sequencing bead array. Spatial barcoding is performed through photolysis, and the proximal genome fragments are attached and amplified via PCR to create a DNA sequencing library [ 49 ]. Following library construction, high-throughput paired-end sequencing is carried out, associating each genome fragment with its spatial location on the bead array using DNA barcoding [ 45 ]. Slide-DNA-seq enables detection of clonal heterogeneity, characterization of copy number variations in each clone, and analysis of their spatial distribution within tissue. This technique is particularly useful for large-scale mapping of tumor evolution, providing essential spatial context to the study of clonal heterogeneity [ 45 , 50 ]. Current methods for characterizing chromatin states or DNA within tissues on a large spatial scale are still in their infancy. The integration of spatial multi-omics technologies aims to achieve spatially resolved whole-exome or whole-genome sequencing. Ultimately, integrating various spatially resolved omics technologies will mark the beginning of the era of molecular anatomy, offering unprecedented insights into tissue organization and function [ 46 ].

Spatial proteomics

Proteomics involves the large-scale study of proteins, encompassing their expression levels, post-translational modifications, and protein–protein interactions, thereby providing a comprehensive understanding of processes such as disease occurrence and cell metabolism at the protein level. Proteins, whether in their native or modified forms, are functional units within the body, making the direct study of proteomics more valuable than relying on transcripts. Targeted localization of proteins within eukaryotic cells can redirect existing proteins to various transport pathways, including nuclear, mitochondrial, ciliary, peroxisomal, endomembrane, and vesicular transport [ 51 ], enabling rapid changes in local protein functions. Conversely, protein mislocalization is frequently associated with cellular dysfunction and diseases such as neurodegeneration, cancer [ 52 ], cystic fibrosis [ 53 ], and metabolic disorders. Therefore, researching protein localization at the subcellular level and capturing subcellular dynamics are crucial for a complete understanding of cell biology. Two primary approaches are used to acquire large-scale spatial proteomic data, including mass spectrometry (MS) and imaging-based methods.

(1) Mass spectrometry-based methods : These approaches offer accurate, proteome-wide identification and quantification of proteins. In subcellular proteomics, specific subcellular compartments are often isolated through biochemical fractionation or proximity labeling before MS analysis [ 54 ]. Key processes involve the enrichment and quantification of proteins through biochemical fractionation across different stages using MS [ 55 ]. Organelles are separated based on properties such as size, density, membrane solubility, or charge, with differential and density centrifugation being common strategies. These methods typically achieve high sensitivity and proteome coverage, although contamination from non-target proteins can occur. Ensuring adequate enrichment of the target organelle is crucial for accurate analysis. Once purified, the distribution profiles of proteins specific to different organelles can reveal the subcellular localization or complex binding of uncharacterized proteins [ 54 ]. MS analysis, combined with multivariate statistics and machine learning (ML), is widely used to handle the complex data generated in spatial proteomics [ 56 ]. These techniques compare the abundance distribution of proteins with known organelle markers to infer protein locations and trafficking pathways [ 57 ]. They can identify trends in organelle protein distribution, even in the presence of structural alterations. Proteins, which can have different morphologies and modified states, function as essential units within cells. The relationship between mRNA and corresponding protein expression is highly regulated and non-linear, making RNA expression an unreliable predictor of protein levels. Unlike the more random expression of transcripts, proteins exhibit a much lower coefficient of variation than their homologous mRNA counterparts. Therefore, directly studying proteins at the single-cell level is far more informative than using transcripts as proxies [ 58 ]. Deep visual proteomics (DVP) combines artificial intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity MS. This technique associates protein abundance with complex cellular or subcellular phenotypes while preserving spatial context [ 59 ]. To achieve this, an ultra-sensitive liquid chromatography-mass spectrometry (LC/MS) workflow has been developed, enhancing sensitivity by up to two orders of magnitude to enable true single-cell state proteomic analysis. The data generated by DVP provide molecular insights into proteomic variation at the phenotypic level while retaining complete spatial information.

(2) Imaging-based methods : These approaches allow for the visualization of proteins in situ without requiring cell lysis or the physical separation of compartments or organelles. Unlike MS methods, which are faster and suitable for large‐scale quantitative analysis, imaging-based approaches visualize the interactions between proteins and affinity reagents. Modern microscopes can simultaneously analyze up to 50 proteins, but each protein of interest requires specific and validated antibodies, limiting high-throughput detection. To minimize the loss of soluble proteins during cellular permeability, it is best to use non-specific crosslinking in proteome-wide studies. It is increasingly evident that protein expression varies even among genetically identical cells. Imaging-based methods can capture this variation by targeting the spatial distribution of proteins at single-cell resolution [ 60 ]. However, the number of published global spatial proteomic studies remains small due to the high cost and time-consuming production of affinity reagents for entire proteomes.

MS and imaging methods each have unique advantages and disadvantages and can complement each other. MS offers high sensitivity, high resolution, and powerful quantitative analysis but involves complex and costly sample preparation. In contrast, imaging methods provide high spatial resolution and dynamic observation capabilities, enabling visualization of protein distribution but have limited quantitative abilities and cover only a small number of proteins. Combining these two techniques allows for comprehensive global protein analysis, enabling the observation of the spatial distribution and dynamic changes of key proteins for a more complete understanding of protein spatial organization.

Spatial epigenomics

Spatial epigenomics examines modifications to the DNA sequence and chromatin structure that regulate gene activity without altering the genetic code itself [ 61 ]. Nucleosomes, the fundamental units of chromosomes, are organized into higher-order chromatin structures. Epigenetic modifications, such as histone acetylation, methylation, phosphorylation, ubiquitination, and DNA methylation, play crucial roles in regulating chromatin structure and DNA accessibility [ 62 , 63 ]. These modifications impact key cellular processes, including gene transcription, DNA replication, recombination, and repair [ 61 ]. Unlike other omics fields, epigenomics relies heavily on bioinformatics to uncover the mechanisms by which the epigenome operates at the molecular level. Developing powerful, repeatable, and process-based techniques is essential for generating data that can be integrated into existing omics databases. The ultimate goal is to create a comprehensive picture of the epigenome by combining information on DNA methylation, chromatin dynamics, accessibility, and gene expression [ 64 ]. Epigenomic MERFISH combined with the recently developed Cleavage Under Targets and Tagmentation (CUT&Tag) approach (Fig.  2 H) enables the mapping of more than 100 epigenomic loci in tissues [ 65 ]. These maps can be used to study patterns of active and silent promoters and potential enhancers, providing deeper insights into the spatial organization and regulation of the epigenome [ 65 ].

Spatial metabolomics

Metabolites play a crucial role in various cellular activities, such as cell signaling, energy transfer, and intercellular communication [ 66 ]. Metabolomics is an emerging discipline that involves the qualitative and quantitative analysis of all low-molecular-weight metabolites within an organism or cell during specific physiological states [ 67 ]. Analyzing metabolites presents challenges due to their dynamic nature and susceptibility to environmental influences during cellular processes [ 68 ]. Spatial metabolomics involves the initial detection and quantification of metabolites present in biological material [ 69 ]. Depending on experimental objectives, researchers can employ either targeted approaches, focusing on quantifying specific analytes, or untargeted approaches, focusing on biomarker discovery and global metabolite profiling [ 70 ].

(1) Targeted metabolomics [ 71 ]: This approach analyzes specific subsets of compounds to address particular biochemical questions or hypotheses. The two primary methods include Fourier transfer mass spectrometry (FT-MS) [ 72 ] and nuclear magnetic resonance (NMR) [ 73 ], both of which offer significant advantages in data acquisition due to their specificity and quantitative reproducibility. FT-MS generates mass data for infused samples, allowing for the identification and matching of metabolites with entries in metabolomics databases. The major drawback of this method is that it does not establish a one-to-one correspondence relationship between entities, which means that a single data point can potentially match with multiple metabolites. NMR produces signals based on the chemical environment of protons present in each metabolite, enabling tentative identification [ 74 ]. The development of triple quadrupole (QqQ) MS provides a robust and sensitive method for high-throughput measurement of a substantial number of biologically significant metabolites. This technique is particularly effective for quantifying low-concentration metabolites that are difficult to detect using NMR [ 75 ].

(2) Untargeted metabolomics : Untargeted metabolomics aims to globally analyze biological compounds, permitting the simultaneous detection of as many metabolites as possible and the exploration of cellular biochemical pathways. LC/MS is the most commonly used platform for untargeted metabolomics [ 76 , 77 ], producing numerous signals during the detection of biological samples. The structural diversity of metabolites is vast, and the acquired data often include both known and unknown metabolites. When searching metabolomics databases for the mass–charge ratio of each detected feature, only a small percentage match the database entries, making the identification of unknown metabolites challenging [ 78 ]. The number of detected unknown metabolites is often overestimated due to several factors. A high concentration of 13 C can cause a mass shift, leading to the detection of multiple features for a single metabolite through naturally occurring isotopes. Additionally, a single metabolite can be ionized into various adducts, including isomers, increasing the demand for selective analytical techniques. Furthermore, metabolites can fragment or form non-covalent interactions with other metabolites upon entering the mass spectrometer. These factors collectively increase the complexity and diversity of detected metabolites [ 79 ].

LC/MS data analysis addresses the complexity of metabolite detection through two main approaches: (1) grouping metabolites with similar features and (2) annotating the type of ion species. These steps facilitate the identification of excimer ions, which are essential for further metabolite identification, such as determining elemental composition or conducting tandem MS based on accurate mass and isotope patterns. CAMERA (an integrated strategy for compound spectral extraction and annotation of LC/MS datasets) can effectively identify most features corresponding to isotopes, adducts, and fragments [ 80 ]. Isotopic labeling methods can also be used to identify and analyze isotope ratio outliers [ 81 ]. Despite these advancements, many metabolites remain uncharacterized. Variations in metabolites within the cellular environment are closely linked to health and disease development. Metabolomics enhances disease analysis at the genomic and protein levels by providing semi-quantitative and quantitative measurements of metabolite levels, which serve as chemical mediators defining specific phenotypes [ 70 ]. The rapid expansion of omics technologies has provided holistic molecular information, enabling the comprehensive study of biological systems. Small molecules and metabolites are essential for numerous cellular functions [ 82 ], offering unique insights into the phenotypic characteristics associated with genome sequences [ 83 ].

Integration of spatial multi-omics

While single-cell multi-omics yields valuable insights into gene regulation across various omics layers [ 84 , 85 ], it lacks the spatial information necessary for understanding cellular functions within tissues. Recently, spatial transcriptomics, proteomics, genomics, epigenomics and metabolomics have emerged, with extensive application in various fields [ 86 , 87 ]. These techniques typically capture only one layer of omics information, and computational methods for integrating data from different omics layers cannot fully overcome the lack of mechanistic links between them. Spatial multi-omics enables the simultaneous analysis of multiple data modalities, such as transcriptomics, proteomics, genomics, epigenomics, and metabolomics, with the same tissue section (Table 2 ).

Integration of spatial transcriptomics and (epi)genomics

Spatial ATAC&RNA-seq and spatial CUT&Tag RNA-seq have revolutionized genome-wide co-mapping of the epigenome and transcriptome by simultaneously profiling chromatin accessibility and mRNA expression, or histone modifications and mRNA expression, respectively. These technologies integrate the chemistry of spatial ATAC-seq or CUT&Tag with spatial transcriptomics on the same tissue section at the cellular level via deterministic co-barcoding [ 88 ], combining microfluidic deterministic barcoding in tissue (DBiT) strategies for spatial ATAC-seq [ 89 ] and CUT&Tag [ 90 ] with DBiT-seq poly(A) transcript profiling [ 91 ]. Spatial-ATAC-seq enables high-spatial-resolution genome-wide mapping of chromatin accessibility in tissue at the cellular level by applying a spatial barcoding scheme to DNA oligomers inserted into accessible genomic loci by Tn5 transposition [ 89 ]. This technology advances our understanding of cell identity, cell state, and cell fate decisions related to epigenetic bases in development and disease. Spatial-CUT&Tag analyzes spatial histone modification profiling at the pixel level on frozen tissue sections without requiring dissociation. This method addresses spatially distinct and cell type-specific chromatin modifications during mouse embryonic organogenesis and postnatal brain development, adding a new dimension to spatial biology by mapping epigenetic regulation related to development and disease [ 90 ]. DBiT-seq creates a 2D grid of spatially barcoded tissue pixels, each defined by a unique combination of barcodes A and B [ 88 ]. After reverse crosslinking, barcoded complementary DNA and genomic DNA fragments are released, and NGS constructs separate libraries for gDNA and cDNA. Sequencing reads are then combined with microscopy images of the tissue section based on spatial barcodes, allowing multi-omics sequence information to be spatially mapped [ 88 ]. These techniques have been applied to co-map embryonic and juvenile mouse brains, as well as the adult human brain. Spatially resolved, genome-wide co-sequencing of the epigenome and transcriptome at the cellular level provides an informative tool for a wide range of biological and biomedical research. Transcriptomics focuses on gene expression from the perspective of mRNA, presenting a global perspective on molecular dynamic changes induced by environmental factors or pathogenic agents [ 92 ]. Benefiting from mature in situ RNA hybridization strategies, targeted capture of DNA sequences or chromosomal loci facilitates spatial genomics detection. DNA-seq FISH + can be applied for studying the spatial structure of the genome based on multi-round probe hybridization imaging. Takei et al. [ 93 ] reported the imaging of 3660 chromosomal sites in a single mouse embryonic stem cell (ES) using DNA-seq FISH + and the imaging of 17 chromatin markers and subnuclear structures by sequential immunofluorescence and expression profiles of 70 RNAs. Genomic regions and chromosomes associated with nuclear bodies and chromatin marks in different cells were revealed by genomic regions. Some of these regions appear to be related to cell types, whereas others (mostly spot-related regions) are more conserved among different cell types [ 46 ].

Integration of spatial proteomics and transcriptomics

Single-cell multi-omics has been highly successful in capturing diverse biological processes at the level of individual cells and nuclei but lacks spatial information [ 94 ]. Gene expression is regulated at multiple levels, from transcription to protein degradation, with RNA and protein levels conveying distinct information about gene function and cell state. These processes occur in various contexts, such as tumors and single-cell suspensions [ 95 ]. Recent progress in spatial in situ profiling has enabled the simultaneous profiling of location and expression. Spatial transcriptomics provides a global spatial tissue profile and has been applied to the study of diverse diseases. Spatial proteomics acquires large-scale spatial proteomic data through MS- and imaging-based experimental approaches. However, few platforms have successfully integrated spatial proteomics and transcriptomics data. Vickovic et al. [ 96 ] developed Spatial Multi-Omics (SM-Omics), an end-to-end framework that leverages a liquid handling platform for high-throughput transcriptome and antibody-based spatial tissue profiling. Using DNA-barcoded antibodies, this automated system enables the simultaneous profiling of the epitopes and transcriptomes within single cells, offering detailed molecular characterization of tissues in situ by quantifying both spatial transcriptomics and multiplex protein detection [ 96 ]. Compared to Visium by 10X Genomics, SM-Omics provides an automated workflow that extends combined spatial transcriptomics and antibody-based protein measurements into a scalable all-sequencing-based technology.

NanoString GeoMx Digital Spatial Profiler (DSP) facilitates high-plex profiling at both the protein and RNA level, permitting spatial and temporal assessment of tumors in frozen or formalin-fixed, paraffin-embedded limited tissue samples [ 97 ]. This platform quantifies protein or RNA abundance by counting unique indexing oligos assigned to each target of interest, using oligonucleotides to study a higher number of biomarkers. Additionally, DSP is a non-destructive technique, allowing the same slides to be used for subsequent studies after the assay is completed [ 97 ].

Spatial co-indexing of transcriptomes and epitopes (Spatial-CITE-seq) offers high-plex protein and whole-transcriptome co-mapping. This approach involves the staining of a tissue slide with a cocktail of approximately 200–300 antibody-derived tags (ADTs), followed by deterministic in-tissue barcoding of both DNA tags and mRNAs. Each tag contains a unique spatial address code AiBj (i = 1 − 50, j = 1 − 50), co-indexing all protein epitopes and the transcriptome. Barcoded cDNAs are subsequently retrieved, refined, and amplified via PCR to create two NGS libraries for paired-end sequencing of ADTs and mRNAs. This process enables computational reconstruction of spatial protein or gene-expression maps [ 98 ].

Integration of spatial transcriptomics and metabolomics

Gene expression and metabolite distribution in tissues are influenced by a variety of factors, including cell type, microenvironment, signaling pathways, and gene regulation. To elucidate the interplay among these factors, it is essential to employ methods that can simultaneously measure molecular evidence of different patterns in tissues while preserving spatial distribution information. Researchers have developed a spatial multimodal analysis (SMA) protocol that combines spatially resolved transcriptomics and mass spectrometry imaging (MSI) in a single tissue slice, while maintaining the specificity and sensitivity of both analytical methods [ 88 ]. This integrated approach reveals associations and heterogeneities between transcriptomes and metabolomes across different tissue regions. Combining spatial transcriptomic and metabolomic data, Vicari et al. identified a reduced proportion of midbrain dopaminergic neurons (MBDOP2) in the lesioned substantia nigra pars compacta and ventral tegmental area, and specified the localization of multiple neurotransmitters and metabolites, including taurine, 3-methoxytyramine, 3,4-dihydroxy-phenylacetaldehyde (DOPAL), 3,4-dihydroxy-phenylacetic acid, norepinephrine, serotonin, histidine, tocopherol, and gamma-aminobutyric acid [ 88 ]. Oral submucous fibrosis (OSF) is a well‐established precancerous lesion, but the molecular mechanisms underlying its malignant transformation into oral squamous cell carcinoma (OSCC) remain unclear [ 99 ]. Yuan et al. integrated spatial transcriptomics and metabolomics to obtain spatial location information on cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes of OSF-derived OSCC tissues. Moreover, they revealed the malignant progression from in situ carcinoma (ISC) to partial epithelial-mesenchymal transformation (pEMT), and identified significant metabolic reprogramming, including abnormal polyamine metabolism, which may play a key role in promoting tumorigenesis and immune escape [ 100 ]. Zheng et al. [ 101 ] combined spatial transcriptomic and metabolic analyses to reveal metabolic heterogeneity and complex transcriptome regulation in injured human brain tissue, facilitating the design of reagents for functional analysis of specific genes. The simultaneous application of these advanced technologies reveals the spatial composition of functional maps within tissues, heterogeneous distribution of cell populations, and differential gene expression in different locations. This comprehensive spatial expression mapping of genes holds significant research value and potential for advancing our understanding of complex biological systems.

Integration of spatial transcriptomics, genomics, and proteomics

The integration of spatial multi-omics aims to expand our understanding of mechanistic relationships across different omics layers and uncover molecular roles essential for cellular function by jointly profiling the transcriptome, genome, epigenome, proteome, and metabolome. Spatially resolved joint analysis of multi-omics can facilitate the identification of novel cell subtypes and measurement of intracellular and intercellular molecular interactions [ 102 ]. Therefore, the need for advanced spatial multi-omics methods has become increasingly important. Multi-omics in situ pairwise sequencing (MiP-seq) is a high-throughput targeted in situ sequencing technique that simultaneously detects multiplexed DNA, RNA, proteins, and biomolecules at subcellular resolution, providing comprehensive data for studying cellular functions and disease mechanisms [ 103 ]. The in situ detection of proteins and biomolecules is achieved using padlocking probes that target antibody-conjugated nucleic acids, while the detection of DNA and RNA is accomplished through direct padlock probes targeting nucleic acids [ 103 ]. Compared to current in situ sequencing methods, MiP-seq utilizes a pairwise-sequencing strategy and dual barcoded padlock probes, markedly increasing decoding capacity and requiring fewer sequencing rounds (10 N vs. 4 N ). Consequently, MiP-seq can reduce sequencing time by approximately 50%, lower sequencing and imaging costs, and minimize laser damage, thereby improving signal decoding accuracy, a key issue in in situ sequencing [ 104 ]. MiP-seq has been applied to mouse brain tissue, enabling the in situ detection of Rbfox3 and Nr4a1 gene loci, which are located on different chromosomes and spatially localized within the nucleus. MiP-seq has also been used to study PK-15 cells co-infected with porcine circovirus 2 (PCV2) and classical swine fever virus (CSFV), simultaneously detecting mRNA from eight cytokine or chemokine genes and two virus-specific proteins (CSFV E2 protein and PCV2 Cap protein) by binding antibodies to nucleic acids [ 103 ]. Thus, MiP-seq demonstrates versatility and high sensitivity in multi-omics in situ analysis, detecting specific DNA sequences, RNA transcripts, and proteins at single-cell resolution, and is a powerful tool for studying cell function, disease mechanisms, and cell–cell interactions in complex biological systems.

Applications of spatial multi-omics

Deciphering spatial-specific atlas production of molecular and cellular profiles.

A comprehensive spatial-specific atlas of molecular and cellular profiles in both healthy and diseased states is essential for developing new therapeutic targets and disease interventions (Fig.  3 A). Spatial transcriptomics combined with single-cell sequencing has been widely used to decipher molecular profiles. Fang et al. constructed a spatial atlas of the human middle and superior temporal gyrus using MERFISH, revealing differences in the cellular composition of these cortical regions between humans and mice [ 105 ]. Single-nucleus RNA-seq (snRNA-seq), single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) [ 106 ], and spatial transcriptomics have been applied to generate a spatially resolved multi-omics single-cell atlas of the entire human maternal–fetal interface, including the myometrium, enabling resolution of the full trajectory of trophoblast differentiation [ 107 ]. Kuppe et al. used snRNA-seq and spatial transcriptomics to create an integrative high-resolution map of cardiac remodeling, enhancing the spatial resolution of cell-type composition and providing spatially resolved insights into the cardiac transcriptome and epigenome with identification of distinct cellular zones of injury, repair, and remodeling [ 106 ]. Advanced spatial epigenome-transcriptome co-sequencing has revealed how epigenetic mechanisms control transcriptional phenotypes and cell dynamics at both spatial and genome-wide levels, providing new insights into spatial epigenetic initiation, differentiation, and gene regulation within tissue structures. Spatial ATAC-RNA-seq and spatial CUT&Tag-RNA-seq were first introduced in analyzing mouse embryos, successfully distinguishing each organ with epigenetic and transcriptome data [ 88 ]. In some mouse brain tissue regions, the epigenetic signature of certain genes persisted with development, but the gene expression was different. In addition, the results of the joint analysis also found that epigenetic regulation and gene expression in different regions of the brain of young mice have unexpected correlations, and that different epigenetic features can cooperate with each other to regulate gene expression. The integration of spatial multi-omics not only opens a new field of spatial omics but also provides novel research avenues for biological and biomedical research.

Applications of spatial-based technologies. Spatial multi-omics technology is employed to investigate various cell biology. This diagram provides an overview of the application of spatial multi-omics. A Spatial-based molecular and cellular atlas. B Spatial-based heterogeneity in human diseases. C Spatial-related crosstalk in tumor immunology. D Spatial trajectory and lineage tracking in human diseases. E Potential targets for therapeutic applications. F Reproduction and development research

Spatial multi-omics decodes spatial-based heterogeneity in human diseases

The complex interactions among tumor cells, surrounding tissues, infiltrating innate immune cells, and adaptive immune cells create a unique environment characterized by inter-related, coexisting, and competitive dynamics [ 108 ]. The characteristics of this tumor immune microenvironment vary significantly due to both intrinsic (e.g., tumor type) and extrinsic factors (e.g., environment). Tumor heterogeneity plays a crucial role in enabling tumor cells to adapt to changes in the microenvironment, thereby promoting tumor resistance and progression (Fig.  3 B).

Tumor heterogeneity includes both intratumor and intertumor heterogeneity [ 109 ]. Metastatic prostate cancer exhibits a wide spectrum of diverse phenotypes, but the extent of these heterogeneities has not yet been established [ 110 ]. Brady et al. integrated spatial transcriptomics and proteomics to analyze multiple discrete areas of metastases, discovering heterogeneity among tumors at different metastatic sites and within the same site. They also identified significant intra-patient heterogeneity in regions with varying androgen receptor (AR) and neuroendocrine activity. Most metastases lacked significant inflammatory infiltrates and PD1, PD-L1, and CTLA4 expression, while the B7-H3/CD276 immune checkpoint protein was highly expressed, particularly in metastatic prostate cancers with high AR activity [ 111 ]. These findings correlate with the clinical observation that metastatic prostate cancers often fail to respond to immune checkpoint blockade therapies such as anti-CTLA4, PD1, and PD-L1 antibodies, suggesting that B7-H3/CD276 could be a potential therapeutic target. Non-small cell lung cancer (NSCLC) is characterized by substantial heterogeneity among individual tumors and within regions of a single tumor [ 112 ]. Intratumor heterogeneity has been shown to contribute to treatment failure and drug resistance through the expansion of pre-existing resistant subclones [ 113 , 114 ]. Previous studies using multi-region profiling to decode the spatial patterns of heterogeneity were limited by the small number of regions analyzed per tumor [ 115 ]. Wu et al. employed multi-region matrix-assisted laser desorption ionization-time of flight (MALDI-TOF), cyclic immunofluorescence (CyCIF), and multi-region single-cell copy number sequencing to conduct spatial multi-omics analysis of tumors from 147 lung adenocarcinoma patients. They developed a novel analysis approach to quantify intratumor spatial heterogeneity: clustered geographic diversification (GD), where molecularly similar cells cluster together, and random GD, where molecularly similar cells are randomly distributed. Patients with random GD exhibited higher recurrence rates and risk of death, characterized by fewer tumor-interacting endothelial cells, higher infiltrating immune cells, and similar GD patterns observed in both proteomic and genomic data [ 116 ], providing insights into spatial heterogeneity and innovative ideas for cancer research. A non-targeted MALDI-MSI analysis [ 117 ] followed by spatial segmentation using different algorithms allowed to highlight molecular heterogeneity among glioblastomas. Three sub-regions were identified (A, B and C regions). Duhamel et al. performed a spatially resolved proteomic analysis to decode the biological pathways involved in these three regions: region A is enriched in genes related to neurotransmission and synaptogenesis; proteins overexpressed in region B are associated with immune infiltration; region C identified proteins involved in RNA processing and metabolism. Finally, they identified PPP1R12A and RPS14 are favorable prognostic markers while ALCAM, ANXA11, and AltProt IP_652563 are unfavorable prognostic markers [ 118 ]. These results highlight the potential of spatial proteomics and spatial metabolomics to decipher the molecular heterogeneity of glioblastoma and identify markers associated with survival.

Understanding how reprogrammed metabolic networks impact tumor growth is crucial for identifying metabolic vulnerabilities that improve cancer treatment. Sun et al. [ 119 ] combined mass spectrometry imaging-based spatial metabolomics and lipid-omics with microarray-based spatial transcriptomics [ 120 ] to visualize intratumor metabolic heterogeneity and cell metabolic interactions within the same gastric cancer sample. They imaged tumor-associated metabolic reprogramming at metabolic-transcriptional levels, linking marker metabolites, lipids, and genes within metabolic pathways and colocalizing them in heterogeneous cancer tissues. The integrated data revealed unique transcriptional features and significant immune-metabolic changes at the tumor invasion frontier. Furthermore, glutamine was overutilized in tumor tissue, genes related to lipids, fatty acid synthesis (FA), and fatty acid elongation were enriched in the tumor tissue region, and long chain polyunsaturated fatty acids were significantly up-regulated in borderline lymphoid tissue, even exceeding levels in tumor tissues [ 121 ]. These findings enhance our understanding of tumor molecular mechanisms and potential targets for cancer therapy. Spatial multi-omics technology accurately depicts gene expression in different tumor tissue locations, addressing the lack of spatial context in single-cell sequencing. Thus, the advancement of spatial omics provides essential support for exploring tumor immune microenvironment dynamics and identifying corresponding therapeutic targets.

Novel insights from spatial multi-omics analyze spatial-related crosstalk in tumor immunology

Spatial multi-omics has provided new perspectives on the complex interactions within the tumor microenvironment. Tumor tissue comprises various cell types, including epithelial, endothelial, fibroblast, vascular smooth muscle, resident immune, and infiltrating immune cells, all of which interact within a 3D environment to support cancer cell growth [ 122 ] (Fig.  3 C). By integrating mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics, researchers have identified a distinct interface at the junction of tumors and neighboring tissues, termed cluster9, within which peritumoral lymphoid tissue (PLT) and distal lymphoid tissue (DLT) are defined [ 121 ]. The PLT exhibits significantly increased uptake and metabolism of glutamine, as well as certain fatty acids, essential for tumor energy metabolism and signaling. Genes associated with fatty acid synthesis, such as FASN, SCD, and ELOVL, as well as ALOX5AP, which promotes arachidonic acid metabolism into leukotriene inflammatory mediators, are also up-regulated in PLT. These results suggest that PLT has a stronger inflammatory response than DLT and inhibits tumor cell proliferation [ 121 ]. Identification of this crosstalk between PLT and tumor cells has enhanced our understanding of tumor molecular mechanisms. ScRNA-seq studies on glioblastomas have highlighted the dynamic plasticity across cellular states [ 123 ], including mesenchymal-like (MES-like), neural progenitor cell-like (NPC-like), astrocyte-like (AC-like), and oligodendrocytic precursor cell-like (OPC-like) states, which are markers of malignant brain tumors [ 124 ]. However, single-cell analysis provides only indirect inferences about cell interactions, often neglecting the role of the local microenvironment in tumorigenesis. Ravi et al. [ 125 ] utilized spatial transcriptomics, metabolomics, and proteomics to quantify the relationship between tumor cells and myeloid and lymphoid cells, discovering increased interactions in inflammation-related gene-rich areas and confirming enhanced interactions between tumor cells and virus-free compartments within transcriptionally defined reactive immune regions. Annika et al. [ 126 ] combined spatial multi-omics and scRNA-seq data from epithelial and stromal compartments to examine immune cell composition during intestinal damage and regeneration, finding that activated B cells decreased and disrupted the essential crosstalk between stromal and epithelial cells during mucosal healing. Spatiotemporal multi-omics allows for consideration of the microenvironment in cell–cell crosstalk studies, enhancing the accuracy of research findings.

Spatial trajectory and lineage tracking in human diseases

Lineage tracking technology is crucial for studying the developmental trajectory and differentiation process of cells (Fig.  3 D). This technology can help determine how individual cells differentiate from a founder cell and how they evolve during development and disease [ 127 ]. Traditionally, lineage tracing involves labeling cells with heritable marks and tracking the trajectory of their offspring. The diversity of cell types produced from a founder cell reflects its differentiation potential. To predict the potential and evolutionary trajectory of founder cells, a wide array of markers is needed for accurate cell type classification. However, the limited availability of markers can mask the variability within cell subsets expressing the selected marker genes [ 128 ], potentially biasing the interpretation of organ complexity. Spatial transcriptomics not only enables comprehensive transcriptomic analysis of thousands of cells but also offers considerable insights into the spatiotemporal relationships among cells. This approach enhances cell-type identification, deepening our understanding of organizational complexity [ 129 ]. By constructing transcriptional atlases of adult tissues and developing embryos, spatial transcriptomics reveals the molecular mechanisms underlying differentiation from stem cells to mature cells. This detailed record elucidates the sequence of events and molecular mechanisms by which cells attain their final identity in embryogenesis or tissue regeneration. It also provides clues to the origins of developmental pathologies and cancer, allowing intervention in pathogenic pathways and replication of cell differentiation processes in vitro [ 130 ]. Densely sampling cells at various stages can describe state manifolds, which visualize the continuum of cell state changes in a multidimensional space and the trajectory of cell differentiation. To understand the instantaneous state of the cell, it is necessary to consider its molecular composition, inter-relationships, tissue position, and physical and regulatory interactions with surrounding cells. This comprehensive approach provides deeper insights into the state and function of cells [ 130 ]. Given the complexity of cells within different species, lineage tracing has expanded to include additional approaches, such as tracer dyes, cell transplantation, and in vivo genetic recombination. Advances in confocal and light-sheet microscopy have enabled the direct tracking of individual cell division patterns in complex vertebrates. However, these methods are limited to only a few measurements of cell state. Recent spatial transcriptomics approaches overcome spectral limitations by allowing genome-scale measurements in fixed in situ samples. High-throughput sequencing employs DNA sequence barcodes to encode clonal information, which can later be read and integrated with other sequence-based omics data. Zhang et al. applied single-cell and spatial transcriptomics to demonstrate extensive diversification of cells from a few multipotent progenitors to numerous differentiated cell states, including several novel cell populations. Furthermore, they identified lineage-specific clusters radiating from the center of six mesenchymal states and active transcription factor network modules associated with the progression of each lineage. They also observed that chondrocyte lineages increased over time, shifting from progenitor cells to more mature clusters [ 131 ]. Bao et al. [ 132 ] revealed that microglia and perivascular macrophages exhibit parallel differentiation processes, although the developmental origins of other tissue-resident macrophages require further exploration using single-cell and spatial transcriptomics. Spatial multi-omics have been applied in several fields, such as tumor progression, immune-associated diseases and metabolism-related disorders. Renal fibrosis, a critical pathological feature in chronic kidney disease progression, has significant global health implications. Spatial multi-omics techniques, such as Cut&Tag with DBiT-Seq [ 133 ], have been crucial in elucidating the complex epigenetic reprogramming during the transition from acute kidney injury to chronic kidney disease, underscoring the importance of multi-omics in understanding and addressing renal fibrosis pathogenesis [ 134 ]. The integration of imaging and sequencing-based omics has led to significant progress in spatial technologies, enabling spatially resolved single-cell detection [ 135 ]. These technologies preserve spatial resolution and large fields of view, allowing for detailed analysis of the microenvironment, spatial neighborhoods, and niche networks in kidney injury. Compatibility with formalin-fixed, paraffin-embedded tissue also facilitates the establishment of kidney injury cohorts, filling a critical gap in prognostic research [ 136 ].

Investigation of new therapies via spatial multi-omics

Targeting nucleotide metabolism is a well-established metabolic therapy in clinical oncology and practice [ 137 ]. However, efforts to target non-nucleotide metabolism in clinical trials have faced challenges due to drug toxicity, inconsistent dietary interventions, lack of biomarkers, and imprecise combination treatments, collectively leading to suboptimal trial outcomes. Additionally, cells within the TME can significantly influence treatment efficacy and undergo substantial changes during tumor progression and treatment response [ 138 ]. Therefore, developing biomarker-guided personalized precision metabolic therapies and targeted metabolic reprogramming is critical to improve the sensitivity of cancer therapy. Rational combinations of chemotherapy, radiation therapy, and other targeted therapies should also be considered. Integrating spatial multi-omics could enhance our understanding of tumor metabolic regulation, offering new therapeutic targets and identifying diagnostic and prognostic markers for various diseases.

Through multi-omics analysis of patients with triple-negative breast cancer (TNBC), researchers discovered that Clostridiales and the associated metabolite trimethylamine N-oxide (TMAO) induce pyroptosis in tumor cells by activating the endoplasmic reticulum stress kinase PERK, which amplifies CD8 + T cell-mediated antitumor immunity in vivo. These findings suggest that microbial metabolites, such as TMAO or its precursor choline, could serve as a new therapeutic strategy to enhance the efficacy of TNBC treatment [ 139 ], offering insights into the crosstalk between microbiota and metabolite immunology. Metastasis remains the leading cause of death in patients with breast cancer; however, the dynamic changes in dissemination evolution remain poorly understood. High-resolution technologies, such as spatial transcriptomics and metabolomics, have been used to map the metabolic landscape. Combined spatial transcriptomics and scRNA-seq have revealed metabolic changes in tumor cells during their transition from the primary site to the leading edge and metastatic lymph nodes, highlighting the potential of incorporating metabolic therapies in treating breast cancer with lymph node metastasis [ 140 ]. Eclipta prostrata L. [ 141 ] has long been used in traditional medicine for its liver-protective properties. Wedelolactone (WEL) and demethylwedelolactone (DWEL) are the primary coumarins found in E. prostrata L. Using a mature thioacetamide (TAA)-induced zebrafish model, Chen et al. integrated spatial metabolomics and transcriptomics and discovered that both WEL and DWEL can improve metabolic disorders induced by nonalcoholic fatty liver disease (NAFLD), primarily through the regulatory effects of WEL on steroid biosynthesis and fatty acid metabolism. Their study successfully mapped the biological distribution and metabolic characteristics of these compounds in zebrafish, revealing the unique mechanisms of WEL and DWEL in improving NAFLD and proposing a multi-omics platform to develop highly effective compounds that improve therapeutic outcomes [ 142 ]. Previous studies have highlighted the role of ferroptosis in a variety of neurological diseases [ 143 ], although its precise role in multiple sclerosis (MS) remained uncertain. Wu et al. integrated data from snRNA‐seq, spatial transcriptomics, and spatial proteomics to define a computational metric of ferroptosis levels and identify the ferroptosis landscape in neuroimmunity and neurodegeneration in MS patients [ 144 ]. Results showed that active lesion edges exhibited the highest ferroptosis scores, associated with phagocyte system activation, while remyelination lesions had the lowest scores. Elevated ferroptosis scores were also observed in cortical neurons, linked to multiple neurodegenerative disease-related pathways [ 144 ], while significant co-localization was detected between ferroptosis scores, neurodegeneration, and microglia. They also established a diagnostic model for MS based on 24 ferroptosis-related genes in peripheral blood. These findings suggest that ferroptosis may play a dual role in MS, associated with both neuroimmunological and neurodegenerative processes, making it a promising therapeutic target and diagnostic marker for MS. Vedolizumab (VDZ) is known to inhibit lymphocyte trafficking to the intestine and is effective in treating ulcerative colitis (UC). However, its broader effects on other cell subsets are less understood. Using comprehensive spatial transcriptomic and proteomic phenotyping, Mennillo et al. identified mononuclear phagocytes as an important cell type impacted by anti-integrin therapy in UC and revealed changes in the spatial distribution of cell subpopulations in tissues before and after VDZ treatment [ 145 ]. Notably, they highlighted the cellular and genetic factors of UC and VDZ therapy, potentially aiding in the development of more precise treatment strategies and the prediction of treatment responses (Fig.  3 E).

Multi-omics in reproduction and development research

Mammalian fertilization begins with the fusion of an oocyte and a sperm cell [ 146 ], with the reproductive system creating an environment for embryonic development (Fig.  3 F). In-depth exploration of the reproductive system requires an understanding of the function of each cell type and their interactions. Spatial multi-omics techniques have been used to examine interactions between adjacent cells and gametes or embryos within the natural tissue environment, preserving the spatial context of the analyzed cells. These technologies have the potential to transform our understanding of mammalian reproduction [ 147 ]. Winkler et al. used scRNA-seq and spatial transcriptomics to profile the remodeling of the female reproductive tract during the estrous cycle, decidualization, and aging and discovered that fibroblasts play a central and organ-specific role in female reproductive tract remodeling by coordinating extracellular matrix (ECM) recombination and inflammation. They also revealed the unexpected costs of repeated remodeling required during reproduction and illustrated how estrus, pregnancy, and aging collectively shape the female reproductive tract [ 148 ]. Yang et al. conducted scRNA-seq, scATAC-seq, and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13–18, generating a large-scale multi-omics atlas of the developing human fetal cerebellum. They found that PARM1 exhibits inconsistent distribution in human and mouse granulosa cells, and identified gene regulatory networks that control the diversity of Purkinje cells and unipolar brush cells [ 149 ]. These key regulatory factors can be harnessed in vitro to generate small brain cells for future clinical applications and enhance our understanding of the link between molecular variation and cell types in neurodevelopmental disorders. Li et al. employed scRNA-seq, spatial transcriptomics, and hybridization-based in situ sequencing to analyze 16 human embryonic and fetal spinal cord samples from post-conceptional weeks 5–12, providing a comprehensive atlas of developmental cells and identifying novel molecular targets and genetic regulation of childhood spinal cancer stem cells [ 150 ] (Table  3 ).

Perspectives

The rapidly evolving field of spatial omics technologies aims to achieve higher resolution, deeper coverage, greater multiplexity, and enhanced versatility in analyzing diverse samples, including formalin-fixed, paraffin-embedded, fresh-frozen, and living tissues. These advancements enable 3D reconstruction of larger tissue regions and comprehensive analysis of spatiotemporal multi-omics, enhancing our understanding of the complex molecular mechanisms underlying cellular interactions within tissues. Effective acquisition, manipulation, analysis, and visualization of spatial omics data are critical components for their successful application. Integrating datasets from different omics modalities is essential to unlock their synergistic potential, although this is challenging due to differing spatial features of the data. Consequently, there is an urgent need for specialized hardware and software to visualize these complex datasets effectively. Key steps include normalizing data matrices, removing low-quality data, improving signal-to-noise ratios, smoothing data to increase sensitivity, and eliminating unwanted technical and biological variations. Developing an independent benchmark of spatial omics integration algorithms should greatly assist researchers in selecting appropriate integration strategies and designing experiments. Without suitable analytical tools, even costly experiments can yield unusable data. To mitigate bias, the scientific community must provide open datasets for comparative analysis of tissues and develop novel methods for accurate detection or capture efficiency. The path to widespread adoption of these technologies remains long. A thorough understanding of the cellular and molecular mechanisms within specific normal or pathogenic microenvironments is crucial for advancing personalized precision medicine. This approach is anticipated to become the primary treatment option in the near future. Expected advancements include increased throughput, reduced costs, integration of more detection modes, and enhanced sensitivity and specificity. Ultimately, multi-omics techniques with spatial single-cell resolution will revolutionize our understanding of cell biology.

Conclusions

The integration of multi-omics with spatial analysis is a rapidly evolving field that holds great promise for a wide range of applications. Spatial multi-omics enables a deeper understanding of complex biological systems, providing novel insights into disease mechanisms, drug target identification, and biomarker discovery. However, integrating multi-omics data presents technical challenges, necessitating advanced computational and statistical methods. Moreover, the interpretation of spatial multi-omics data is further complicated by spatially varying environmental factors and technical noise. Thus, the development of sophisticated computational tools and analytical methods capable of managing large-scale spatial multi-omics datasets is essential for fully leveraging the potential of this approach.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Acknowledgements

Thank you for drawing materials from SMART—Servier Medical ART and SciDraw | Scientific Drawings.

The National Key R&D Program of China (2021YFC2701201 to P.W.), the Natural Science Foundation of China (82072895 and 82372929 to P.W., 82141106 to M.D., 82203453 to C. C.), and Foundation of Tongji Hospital (24-2KYC13057-08 to C. C.).

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Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

Xiaojie Liu, Ting Peng, Miaochun Xu, Shitong Lin, Binghan Liu, Yashi Xu & Peng Wu

Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

Bai Hu, Tian Chu, Wencheng Ding, Li Li & Canhui Cao

National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

Xiaojie Liu, Ting Peng, Miaochun Xu, Shitong Lin, Bai Hu, Tian Chu, Binghan Liu, Yashi Xu, Wencheng Ding, Li Li, Canhui Cao & Peng Wu

Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

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P. W. and X. L. conceived the whole article and completed the original draft preparation. P. W. and C. C. performed major reviews and edited the structure of the article. The primary drawing effort was done by T. P. and C. X. During subsequent revisions of the article, S. L., B. H., T. C., B. L., Y. X., W. D., and L. L. have touched up the language of the article and participated in the revision process of the later article. All authors reviewed the manuscript.

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Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson’s disease over seven years in a cohort: a comparative analysis

• Anne-Marie Hanff 1 , 2 , 3 , 4 ,
• Rejko Krüger 1 , 2 , 5 ,
• Christopher McCrum 4 ,
• Christophe Ley 6 on behalf of

BMC Medical Research Methodology volume  24 , Article number:  183 ( 2024 ) Cite this article

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Introduction

While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort.

In this retrospective longitudinal analysis of 802 people with typical Parkinson’s disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models.

Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p -values (+ 1.016/ 7 years, p  = 0.107, -0.056/ 7 years, p  = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p -value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years ( p  < 0.001).

The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.

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In longitudinal studies: “an outcome is repeatedly measured, i.e., the outcome variable is measured in the same subject on several occasions.” [ 1 ]. When assessing the same individuals over time, the different data points are likely to be more similar to each other than measurements taken from other individuals. Consequently, the application of special statistical techniques is required, which take into account the fact that the repeated observations of each subject are correlated [ 1 ]. Parkinson’s disease (PD) is a heterogeneous neurodegenerative disorder resulting in a wide variety of motor and non-motor symptoms including apathy, defined as a disorder of motivation, characterised by reduced goal-directed behaviour and cognitive activity and blunted affect [ 2 ]. Apathy increases over time in people with PD [ 3 ]. Specifically, apathy has been associated with the progressive denervation of ascending dopaminergic pathways in PD [ 4 , 5 ] leading to dysfunctions of circuits implicated in reward-related learning [ 5 ].

T-tests are often misused to analyse changes over time [ 6 ]. Consequently, we aim to demonstrate how the choice of statistical method may influence research outcomes, specifically the size and interpretation of longitudinal effect estimates in a cohort. Thus, the findings are intended for illustrative and educational purposes related to the statistical methodology. In a retrospective analysis of data from the Luxembourg Parkinson's study, a nation-wide, monocentric, observational, longitudinal-prospective dynamic cohort [ 7 , 8 ], we assess change in apathy using three different statistical approaches (paired t-test, linear regression, mixed effects model). We defined the following target estimand: In people diagnosed with PD, what is the change in the apathy score from visit 1 to visit 8? To estimate this change, we formulated the statistical hypothesis as follows:

While apathy was the dependent variable, we included the visit number as an independent variable (linear regression, mixed effects model) and as a grouping variable (paired t-test). The outcome apathy was measured by the discrete score from the Starkstein apathy scale (0 – 42, higher = worse) [ 9 ], a scale recommended by the Movement Disorders Society [ 10 ]. This data was obtained from the National Centre of Excellence in Research on Parkinson's disease (NCER-PD). The establishment of data collection standards, completion of the questionnaires at home at the participants’ convenience, mobile recruitment team for follow-up visits or standardized telephone questionnaire with a reduced assessment were part of the efforts in the primary study to address potential sources of bias [ 7 , 8 ]. Ethical approval was provided by the National Ethics Board (CNER Ref: 201,407/13). We used data from up to eight visits, which were performed annually between 2015 and 2023. Among the participants are people with typical PD and PD dementia (PDD), living mostly at home in Luxembourg and the Greater Region (geographically close areas of the surrounding countries Belgium, France, and Germany). People with atypical PD were excluded. The sample at the date of data export (2023.06.22) consisted of 802 individuals of which 269 (33.5%) were female. The average number of observations was 3.0. Fig. S1 reports the numbers of individuals at each visit while the characteristics of the participants are described in Table  1 .

As illustrated in the flow diagram (Fig.  1 ), the sample analysed from the paired t-test is highly selective: from the 802 participants at visit 1, the t-test only included 63 participants with data from visit 8. This arises from the fact that, first, we analyse the dataset from a dynamic cohort, i.e., the data at visit 1 were not collected at the same time point. Thus, 568 of the 802 participants joined the study less than eight years before, leading to only 234 participants eligible for the eighth yearly visit. Second, after excluding non-participants at visit 8 due to death ( n  = 41) and other reasons ( n  = 130), only 63 participants at visit 8 were left. To discuss the selective study population of a paired t-test, we compared the characteristics (age, education, age at diagnosis, apathy at visit 1) of the remaining 63 participants at visit 8 (included in the paired t-test) and the 127 non-participants at visit 8 (excluded from the paired t-test) [ 12 ].

Flow diagram of patient recruitment

The paired two-sided t-test compared the mean apathy score at visit 1 with the mean apathy score at the visit 8. We attract the reader’s attention to the fact that this implies a rather small sample size as it includes only those people with data from the first and 8th visit. The linear regression analysed the relationship between the visit number and the apathy score (using the “stats” package [ 13 ]), while we performed longitudinal two-level mixed effects models analysis with a random intercept on subject level, a random slope for visit number and the visit number as fixed effect (using the “lmer”-function of the “lme4”-package [ 14 ]). The latter two approaches use all available data from all visits while the paired t-test does not. We illustrated the analyses in plots with the function “plot_model” of the R package sjPlot [ 15 ]. We conducted data analysis using R version 3.6.3 [ 13 ] and the R syntax for all analyses is provided on the OSF project page ( https://doi.org/ https://doi.org/10.17605/OSF.IO/NF4YB ).

Panel A in Fig.  2 illustrates the means and standard deviations of apathy for all participants at each visit, while the flow-chart (Fig. S1 ) illustrates the number of participants at each stage. On average, we see lower apathy scores at visit 8 compared to visit 1 (higher score = worse). By definition, the paired t-test analyses pairs, and in this case, only participants with complete apathy scores at visit 1 and visit 8 are included, reducing the total analysed sample to 63 pairs of observations. Consequently, the t-test compares mean apathy scores in a subgroup of participants with data at both visits leading to different observations from Panel A, as illustrated and described in Panel B: the apathy score has increased at visit 8, hence symptoms of apathy have worsened. The outcome of the t-test along with the code is given in Table  2 . Interestingly, the effect estimates for the increase in apathy were not statistically significant (+ 1.016 points, 95%CI: -0.225, 2.257, p  = 0.107). A possible reason for this non-significance is a loss of statistical power due to a small sample size included in the paired t-test. To visualise the loss of information between visit 1 and visit 8, we illustrated the complex individual trajectories of the participants in Fig.  3 . Moreover, as described in Table S1 in the supplement, the participants at visit 8 (63/190) analysed in the t-test were inherently significantly different compared to the non-participants at visit 8 (127/190): they were younger, had better education, and most importantly their apathy scores at visit 1 were lower. Consequently, those with the better overall situation kept coming back while this was not the case for those with a worse outcome at visit 1, which explains the observed (non-significant) increase. This may result in a biased estimation of change in apathy when analysed by the compared statistical methods.

Bar charts illustrating apathy scores (means and standard deviations) per visit (Panel A: all participants, Panel B: subgroup analysed in the t-test). The red line indicates the mean apathy at visit 1

Scatterplot illustrating the individual trajectories. The red line indicates the regression line

From the results in Table  2 , we see that the linear regression coefficient, representing change in apathy symptoms per year, is not significantly different from zero, indicating no change over time. One possible explanation is the violation of the assumption of independent observations for linear regressions. On the contrary, the effect estimates for the linear mixed effects models indicated a significant increase in apathy symptoms from visit 1 to visit 8 by + 2.680 points (95%CI: 1.880, 3.472, p  < 0.001). Consequently, mixed effects models were the only method able to detect an increase in apathy symptoms over time and choosing mixed effect models for the analysis of longitudinal data reduces the risk of false negative results. The differences in the effect sizes are also reflected in the regression lines in Panel A and B of Fig.  4 .

Scatterplot illustrating the relationship between visit number and apathy. Apathy measured by a whole number interval scale, jitter applied on x- and y-axis to illustrate the data points (Panel A: Linear regression, Panel B: Linear mixed effects model). The red line indicates the regression line

The effect sizes differed depending on the choice of the statistical method. Thus, the paired t-test and the linear regression resulted in an output that would lead to different interpretations than the mixed effects models. More specifically, compared to the t-test and linear regression (which indicated non-significant changes in apathy of only + 1.016, -0.064 points from visit 1 to visit 8, respectively), the linear mixed effects models found an increase of + 2.680 points from visit 1 to visit 8 on the apathy scale. This increase is more than twice as high as indicated by the t-test and suggests linear mixed models is a more sensitive approach to detect meaningful changes perceived by people with PD over time.

Mixed effects models are a valuable tool in longitudinal data analysis as these models expand upon linear regression models by considering the correlation among repeated measurements within the same individuals through the estimation of a random intercept [ 1 , 16 , 17 ]. Specifically, to account for correlation between observations, linear mixed effects models use random effects to explicitly model the correlation structure, thus removing correlation from the error term. A random slope in addition to a random intercept allows both the rate of change and the mean value to vary by participant, capturing individual differences. This distinguishes them from group comparisons or standard linear regressions, in which such explicit modelling of correlation is not possible. Thus, the linear regression not considering correlation among the repeated observations leads to an underestimation of longitudinal change, explaining the smaller effect sizes and insignificant results of the regression. By including random effects, linear mixed effects models can better capture the variability within the data.

Another common challenge in longitudinal studies is missing data. Compared to the paired t-test and regression, the mixed effects models can also include participants with missing data at single visits and account for the individual trajectories of each participant as illustrated in Fig.  2 [ 18 ]. Although multiple imputation could increase the sample size, those results need to be interpreted with caution in case the data is not missing at random [ 18 , 19 ]. Note that we do not further elaborate here on this topic since this is a separate issue to statistical method comparison. Finally, assumptions of the different statistical methods need to be respected. The paired t-test assumes a normal distribution, homogeneity of variance and pairs of the same individuals in both groups [ 20 , 21 ]. While mixed effects models don’t rely on independent observations as it is the case for linear regression, all other assumptions for standard linear regression analysis (e.g., linearity, homoscedasticity, no multicollinearity) also hold for mixed effects model analyses. Thus, additional steps, e.g., check for linearity of the relationships or data transformations are required before the analysis of clinical research questions [ 17 ].

While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome [ 1 ], they are worth considering for longitudinal data analyses. Thus, assuming an increase of apathy over time [ 3 ], mixed effects models were the only method able to detect statistically significant changes in the defined estimand, i.e., the change in apathy from visit 1 to visit 8. Possible reasons are a loss of statistical power due to a small sample size included in the paired t-test and the violence of the assumption of independent observations for linear regressions. Specifically, the effects estimated for the group comparison and the linear regression were smaller with high p -values, indicating a statistically insignificant change in apathy over time. The effect estimates for the mixed effects models were positive with a very small p -value, indicating a statistically significant increase in apathy symptoms from visit 1 to visit 8 in line with clinical expectations. Mixed effects models can be used to estimate different types of longitudinal effects while an inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change and thus clinical significance. Therefore, researchers should more often consider mixed effects models for longitudinal analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.

Availability of data and materials

The LUXPARK database used in this study was obtained from the National Centre of Excellence in Research on Parkinson’s disease (NCER-PD). NCER-PD database are not publicly available as they are linked to the Luxembourg Parkinson’s study and its internal regulations. The NCER-PD Consortium is willing to share its available data. Its access policy was devised based on the study ethics documents, including the informed consent form approved by the national ethics committee. Requests for access to datasets should be directed to the Data and Sample Access Committee by email at [email protected].

The code is available on OSF ( https://doi.org/10.17605/OSF.IO/NF4YB )

Abbreviations

Parkinson's disease

Null hypothesis

Alternative hypothesis

Parkinson's disease dementia

National Centre of Excellence in Research on Parkinson's disease

Open Science Framework

Confidence Interval

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Acknowledgements

We would like to thank all participants of the Luxembourg Parkinson’s Study for their important support of our research. Furthermore, we acknowledge the joint effort of the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) Consortium members from the partner institutions Luxembourg Centre for Systems Biomedicine, Luxembourg Institute of Health, Centre Hospitalier de Luxembourg, and Laboratoire National de Santé generally contributing to the Luxembourg Parkinson’s Study as listed below:

Geeta ACHARYA 2, Gloria AGUAYO 2, Myriam ALEXANDRE 2, Muhammad ALI 1, Wim AMMERLANN 2, Giuseppe ARENA 1, Michele BASSIS 1, Roxane BATUTU 3, Katy BEAUMONT 2, Sibylle BÉCHET 3, Guy BERCHEM 3, Alexandre BISDORFF 5, Ibrahim BOUSSAAD 1, David BOUVIER 4, Lorieza CASTILLO 2, Gessica CONTESOTTO 2, Nancy DE BREMAEKER 3, Brian DEWITT 2, Nico DIEDERICH 3, Rene DONDELINGER 5, Nancy E. RAMIA 1, Angelo Ferrari 2, Katrin FRAUENKNECHT 4, Joëlle FRITZ 2, Carlos GAMIO 2, Manon GANTENBEIN 2, Piotr GAWRON 1, Laura Georges 2, Soumyabrata GHOSH 1, Marijus GIRAITIS 2,3, Enrico GLAAB 1, Martine GOERGEN 3, Elisa GÓMEZ DE LOPE 1, Jérôme GRAAS 2, Mariella GRAZIANO 7, Valentin GROUES 1, Anne GRÜNEWALD 1, Gaël HAMMOT 2, Anne-Marie HANFF 2, 10, 11, Linda HANSEN 3, Michael HENEKA 1, Estelle HENRY 2, Margaux Henry 2, Sylvia HERBRINK 3, Sascha HERZINGER 1, Alexander HUNDT 2, Nadine JACOBY 8, Sonja JÓNSDÓTTIR 2,3, Jochen KLUCKEN 1,2,3, Olga KOFANOVA 2, Rejko KRÜGER 1,2,3, Pauline LAMBERT 2, Zied LANDOULSI 1, Roseline LENTZ 6, Laura LONGHINO 3, Ana Festas Lopes 2, Victoria LORENTZ 2, Tainá M. MARQUES 2, Guilherme MARQUES 2, Patricia MARTINS CONDE 1, Patrick MAY 1, Deborah MCINTYRE 2, Chouaib MEDIOUNI 2, Francoise MEISCH 1, Alexia MENDIBIDE 2, Myriam MENSTER 2, Maura MINELLI 2, Michel MITTELBRONN 1, 2, 4, 10, 12, 13, Saïda MTIMET 2, Maeva Munsch 2, Romain NATI 3, Ulf NEHRBASS 2, Sarah NICKELS 1, Beatrice NICOLAI 3, Jean-Paul NICOLAY 9, Fozia NOOR 2, Clarissa P. C. GOMES 1, Sinthuja PACHCHEK 1, Claire PAULY 2,3, Laure PAULY 2, 10, Lukas PAVELKA 2,3, Magali PERQUIN 2, Achilleas PEXARAS 2, Armin RAUSCHENBERGER 1, Rajesh RAWAL 1, Dheeraj REDDY BOBBILI 1, Lucie REMARK 2, Ilsé Richard 2, Olivia ROLAND 2, Kirsten ROOMP 1, Eduardo ROSALES 2, Stefano SAPIENZA 1, Venkata SATAGOPAM 1, Sabine SCHMITZ 1, Reinhard SCHNEIDER 1, Jens SCHWAMBORN 1, Raquel SEVERINO 2, Amir SHARIFY 2, Ruxandra SOARE 1, Ekaterina SOBOLEVA 1,3, Kate SOKOLOWSKA 2, Maud Theresine 2, Hermann THIEN 2, Elodie THIRY 3, Rebecca TING JIIN LOO 1, Johanna TROUET 2, Olena TSURKALENKO 2, Michel VAILLANT 2, Carlos VEGA 2, Liliana VILAS BOAS 3, Paul WILMES 1, Evi WOLLSCHEID-LENGELING 1, Gelani ZELIMKHANOV 2,3

1 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

2 Luxembourg Institute of Health, Strassen, Luxembourg

3 Centre Hospitalier de Luxembourg, Strassen, Luxembourg

4 Laboratoire National de Santé, Dudelange, Luxembourg

5 Centre Hospitalier Emile Mayrisch, Esch-sur-Alzette, Luxembourg

6 Parkinson Luxembourg Association, Leudelange, Luxembourg

7 Association of Physiotherapists in Parkinson's Disease Europe, Esch-sur-Alzette, Luxembourg

8 Private practice, Ettelbruck, Luxembourg

9 Private practice, Luxembourg, Luxembourg

10 Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

11 Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University Medical Centre+, Maastricht, the Netherlands

12 Luxembourg Center of Neuropathology, Dudelange, Luxembourg

13 Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

This work was supported by grants from the Luxembourg National Research Fund (FNR) within the National Centre of Excellence in Research on Parkinson's disease [NCERPD(FNR/NCER13/BM/11264123)]. The funding body played no role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.

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Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg

Anne-Marie Hanff & Rejko Krüger

Translational Neurosciences, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg

Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University Medical Centre+, Maastricht, The Netherlands

Anne-Marie Hanff

Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands

Anne-Marie Hanff & Christopher McCrum

Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg

Rejko Krüger

Department of Mathematics, University of Luxembourg, Esch-Sur-Alzette, Luxembourg

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Contributions

A-MH: Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Project administration, Writing – original draft, Writing – review & editing. RK: Conceptualization, Methodology, Funding, Resources, Supervision, Project administration, Writing – review & editing. CMC: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing. CL: Conceptualization, Methodology, Writing – original draft, Writing – review & editing.

Corresponding author

Correspondence to Anne-Marie Hanff .

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The study involved human participants, was reviewed and obtained approval from the National Ethics Board Comité National d’Ethique de Recherche (CNER Ref: 201407/13). The study was performed in accordance with the Declaration of Helsinki and patients/participants provided their written informed consent to participate in this study. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.

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Hanff, AM., Krüger, R., McCrum, C. et al. Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson’s disease over seven years in a cohort: a comparative analysis. BMC Med Res Methodol 24 , 183 (2024). https://doi.org/10.1186/s12874-024-02301-7

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Received : 21 March 2024

Accepted : 01 August 2024

Published : 24 August 2024

DOI : https://doi.org/10.1186/s12874-024-02301-7

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Energy & Environmental Science

Thermodynamically stable low-na o3 cathode materials driven by intrinsically high ionic potential discrepancy †.

* Corresponding authors

a GRINM (Guangdong) Research Institute for Advanced Materials and Technology, Foshan, Guangdong, P.R. China E-mail: [email protected]

b University of Science and Technology Beijing, Beijing, P.R. China

c China Automotive Battery Research Institute Co., Ltd, Beijing, P.R. China

d School of Materials Science and Engineering, Hubei University, Wuhan, P.R. China

e Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, Zhejiang, P.R. China E-mail: [email protected]

f Powder Metallurgy Research Institute, Central South University, Changsha, Hunan, P.R. China

g Nanjing University of Information Science & Technology, Nanjing, Jiangsu, P.R. China E-mail: [email protected]

h Graphene Composite Research Center, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, P.R. China E-mail: [email protected]

i General Research Institute for Nonferrous Metals, Beijing, P.R. China

The thermodynamically stable window for an O3-type layered sodium cathode material is largely determined by its Na stoichiometry; a spontaneous transition to the P-type structure occurs when it is relatively low. With such limitation, the capacity and stability of O3-structured materials become restricted and a potentially promising class of O3-type materials that garner the structural stability of P2-type materials is underexplored. This work discovers that a large ionic potential discrepancy within the transition metal layer acts as a driving force that pushes the Na-ions from prismatic coordination to octahedral coordination. Utilizing this strategy, we have explored a class of off-stoichiometric O3-type materials with exceptionally low Na-stoichiometry (generally forming P2-type structures with higher thermodynamic stability) yet having the structural parameter features of P-type materials. These materials demonstrate rapid O3–P3 phase transition while maintaining a stable solid solution reaction at high voltages, resulting in an impressive P-phase range of 81.4%, thus showing superior performance compared with conventional O3-type materials. This principle provides a great extension to the existing family of layered cathode materials for sodium-ion batteries.

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Thermodynamically stable low-Na O3 cathode materials driven by intrinsically high ionic potential discrepancy

M. Li, H. Zhuo, Y. Xu, Q. Jing, Y. Wu, Y. Gu, Z. Liao, K. Wang, M. Song, X. Li, J. Liang, C. Zhao, Y. Jiang, T. Wu, D. Geng, J. Hu, X. Sun and B. Xiao, Energy Environ. Sci. , 2024, Advance Article , DOI: 10.1039/D4EE02359E

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