how to summarize findings in research

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How To Write A Research Summary

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

• While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
• To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
• As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and  scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

• What is the research question and how is it addressed?
• Is there a hypothesis in the introductory part?
• What type of methods are being adopted?
• What is the sample size for data collection and how is it being analyzed?
• What are the most vital findings?
• Do the results support the hypothesis?

Discussion/Conclusion

• What is the final solution to the problem statement?
• What is the explanation for the obtained results?
• What is the drawn inference?
• What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

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Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD Cand). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter – exciting! But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step.  

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips and tricks for an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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Summarizing Research Findings: Systematic Review and Meta-Analysis

MSc, PhD, Lecturer, Health Research Development Unit (HeRDU), Faculty of Medicine, University of Malaya

The explosion of biomedical publishing makes keeping up with the primary studies an impossible task. The often disparate, confusing and contradicting findings of individual studies makes healthcare professionals turn to review articles where knowledge has been collated and published in summaries. Narrative reviews lack rigorous, systematic and reproducible synthesis. In contrast, systematic reviews are conducted using systematic and explicit methods to identify, select and critically appraise relevant research, and to collect and analyse data from the studies that are included in the review. The final pathway for systematic review is a statistical summary of the results of primary studies, or meta-analysis. This article provides some guidelines to health care providers in understanding the key aspects of systematic review and meta-analysis. Steps involved in systematic review are discussed. The potential pitfall of meta-analysis was also explored.

THE NEED FOR REVIEWS

The huge amount of medical information available and its exponential growth have become common problem in the literature of biomedical information. Health care practitioners face the explosion in biomedical knowledge which makes keeping up with the primary research an impossible feat. There are approximately 17,000 new biomedical books published every year along with 30,000 biomedical journals, with an annual increase of 7%. For instance, MEDLINE alone contains more than eleven million citations, and more than 400,000 articles are added to the file each year. 1 Majority health care providers noted that the current volume of scientific literature is unmanageable 2 and often do not have sufficient time for reading medical journals as the information explosion continues. 3 Further, some of these studies could be unclear, confusing or may also have contradicting results.

THE NARRATIVE REVIEWS

To make this task easier and manageable for health care providers as well as decision makers, reviews are often among their information resources. Reviews have always been a part of the medical literature. Traditionally, medical research has been integrated in the narrative or nonsystematic form. An expert in a particular field will review studies, decided on the relevance, and highlight the findings, both in terms of results and, to a lesser degree the methodology. 4 Such narrative reviews tend to be unsystematic and susceptible to many biases. Firstly, no systematic approach is prescribed to obtain the primary data and to integrate the data. Often, subjective judgment of the reviewer was used. There were often no explicit standards exist to assess the quality of review. Moreover, narrative reviewer also does not synthesize data quantitatively.

A CALL FOR SYSTEMATIC REVIEWS

A systematic review is defined by the Cochrane Handbook as ‘A review of a clearly formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant research, and to collect and analyse data from the studies that are included in the review’. In contrast to narrative review, systematic review allows readers to appraise how the review was conducted and synthesized. It is of particular value in bringing together a number of separately conducted studies, sometimes with conflicting findings, and synthesizing their results. Systematic reviews have been proven to be able to yield valid, precise, and widely applicable answers to clinical questions. 5 In short, systematic reviews summarise large amounts of information and are more likely than individual trials to describe the true clinical effect of an intervention. Thus, systematic reviews have come to play a central role in informing clinical decisions and guidelines. A systematic review is also often called an ‘overview’.

A meta-analysis takes a systematic review one step further by statistically pooling the results of combinable studies. Since its introduction, meta-analysis has established itself as an influential branch of clinical epidemiology and health services research, with hundreds of meta-analyses published in the medical literature each year. 6

THE PROCESS OF SYSTEMATIC REVIEW

Systematic review should be carefully planned with a detailed written protocol prepared in advance as any other search project. Systematic review involves several discrete steps and the steps are summarised below.

Step 1: Formulate review question . This requires the formulation of a clear statement of relevant patient groups, intervention of interest, as well as outcomes. The details are used to select studies for inclusion in the review.

Step 2: Locate studies . Systematic review must be undertaken in accordance with a predefined search strategy that would allow the completeness of the search to be assessed. Search strategies should consider the following sources: The Cochrane Controlled Trials Register (CCTR), other electronic databases and trials registered not covered by CCTR, checking reference lists, hand searching of key journals and personal communication with experts in the fields. The selection of primary studies is governed by inclusion and exclusion criteria that are initially specified when the protocol is defined.

Step 3: Appraising the quality of studies . After an exhaustive search, all possible primary studies that have been identified need to be assessed for eligibility for inclusion. Application of stringent inclusion/exclusion criteria should be addressed for example types of participants, interventions, outcomes, study designs and methodological quality. Independent assessment by more than one observer is desirable.

Step 4: Combining the results. The findings from combinable individual primary studies are then pooled to produce an ‘overall estimate’ on the clinical effectiveness of the intervention. The aggregation can be qualitative, or more appropriate, by statistically combining the data produced by individual studies into a single summary estimate. The statistical pooling of data is termed meta-analysis ( Figure 1 ). In meta-analysis, results from studies are combined using ‘inverse variance method’, whereby larger studies and studies with less random variation are given greater weight than smaller studies.

Meta-analysis: Data from several different studies are combined and produce a single estimate

Meta-analysis can only be undertaken when studies address the same question, administer the intervention in a similar manner or measure the same outcomes. When studies differ in one or more of these components, meta-analysis is not appropriate. Therefore, systematic review may or may not include meta-analysis.

In meta-analysis, for outcomes measured on a continuous scale, the weighted mean difference is commonly used. For outcomes measured on a dichotomous scale, common approaches include the use of odds ratio or relative risk. There are two approaches for combining the data: fixed-effects model assume that an intervention has a single true effect whereas random-effects models assume that an effect may vary across studies. 7 The results of meta-analysis can be displayed graphically (Forest plot) to allow a visual comparison of findings of individual studies ( Figure 2 ).

Forest plot of meta-analysis

Systematic review should continue with an investigation of the reasons for heterogeneity. Subgroup analysis, sensitivity analysis and meta-regression are frequently used to investigate heterogeneity of individual studies in meta-analysis. One of the major drawbacks to using meta-analysis is the possibility of publication bias. One way to investigate whether a review is subject to publication bias is to prepare a ‘funnel plot’ ( Figure 3 ) and examine this for signs of asymmetry. 8

Funnel plot showing evidence of publication bias

Step 5: Interpret results. The findings from systematic review and statistical pooling of the studies then need to be interpreted, discussed and set out the implications for practice or further research. Issues such as the quality and heterogeneity of the included studies plus the possible impact of bias need to be discussed.

PITFALLS AND PROBLEMS

Meta-analyses have received mixed receptions. Some see meta-analysis as an exercise of ‘mega-silliness’, 9 ‘a tool has become a weapon’ 10 and a number of statisticians think that meta-analysis represents the unacceptable face of statisticism. 11 There are also those that still prefer the conventional narrative review article. 12 The mixed receptions were due to opposite conclusions observed in some systematic reviews that address the same issue. 13 , 14 Also, meta-analyses of small trials were discovered to contradict by a single large randomized trial. 15

Publication bias could be a serious problem for meta-analyses, secondly, studies may be of varying quality. Clearly the quality of trials included in a systematic review and meta-analysis is of crucial importance and should be of high methodological quality as well as free from biases. Meta-analysis should therefore be considered only within the framework of systematic review that has been prepared using a systematic approach to mitigate all kinds of biases and explicitly address the issue of the completeness of the evidence identified, the quality of component studies and the combinability of studies. 15 The Cochrane Collaboration has been established to overcome this problem by providing high quality and authoritative systematic reviews and meta-analyses. 16 , 17 The collaboration not only ensures that high-quality reviews are conducted but also update reviews when new evidence becomes available.

Systematic review is an invaluable resource for both clinicians and researchers. However, not all reviews are systematic and even those that are described as systematic may be methodologically flawed. Nonetheless, a high quality systematic review provides the best available evidence. The usefulness of a systematic review can further be enhanced by statistical summary of the results by meta-analytical technique. Pooling individual studies may reduce the risk of random error, increase statistical power and allow for a more accurate estimate of effect size.

SOME TERMINOLOGIES

Bias (synonym: systematic error): the distortion of the outcome, as a result of a known or unknown variable other than intervention (i.e. the tendency to produce results that depart from the “true” result).

Cochrane Collaboration : The Cochrane Collaboration is an international organization that aims to help people make well-informed decisions about healthcare by preparing, maintaining & promoting the accessibility of systematic reviews of the effects of healthcare interventions.

Cochrane Controlled Trials Register (CCTR): CCTR is a database of references to controlled trials in health care.

Critical appraisal : systematically finding, appraising and interpreting evidence of effectiveness. It is aimed to examine research evidence to assess its validity, results and relevance before using it to inform a decision.

Cumulative meta-analysis : the repeated performance of meta-analysis whenever a new trial becomes available for inclusion. In cumulative meta-analysis studies are added one at a time in a specified order.

Effect size : refers to the size of a relationship between an expose and an outcome. The term is applied to measurement of the differences in the outcome between the study groups. Relative risk, odds ratio, and risk differences can be defined as effect sizes for dichotomous scale. Effect size of continuous variable is the standardized mean differences.

Fixed-effect model : a mathematical model that combines the results of studies that assume the effect of the intervention is constant in all subject population studied. Only within study variation is included when assessing the uncertainty of results.

Forest plot : a forest plot presents the means and variance for the difference for each pooled primary study. The line represents the standard error of the difference, the box represents the mean difference and its size proportional to the number of subjects in the study. The bottom entry in a forest plot is the summary estimate of the treatment difference and confidence interval for the summary difference ( Figure 2 ).

Funnel plot : a graphical method of assessing bias; the effect size of each study is plotted against some measure of study information. If the shape of the plot resembles an inverted funnel, it can be stated that there is no evidence of publication bias within the systematic review ( Figure 3 ).

Heterogeneity : the variability between studies in terms of key characteristics (i.e. ecological variables) quality (i.e. methodology) or effect (i.e. results). Statistical tests of heterogeneity may be used to assess whether the observed variability in effect size (i.e. study results) is greater than that expected to occur purely by chance.

Meta-regression : a multivariable model investigating effect size from individual studies, generally weighted by sample size, as a function of various study characteristics (i.e. to investigate whether study characteristics are influencing effect size).

Outlier : an outlier study in meta-analysis is study that results very different from the rest of the studies. Outlier could alter the conclusions of a meta-analysis.

Overall estimate : is the pooled estimate from a meta-analysis. The overall estimate from a meta-analysis is always displayed with its confidence interval.

Primary studies : Individual studies contributing to a systematic review are called primary studies whereas a systematic review is a form of a secondary study.

Publication bias : publication bias refers to the problem that positive results are more likely to be published than negative results and this may therefore give a misleading assessment of the impact of an intervention. Publication bias can be examined via a funnel plot.

Random-effects model : a mathematical model for combining the results of studies that allow for variation in the effect of the intervention amongst the subject populations studied. Both within-study variation and between-study variation is included when assessing the uncertainty of results.

Review : article that summarizes a number of primary studies and discusses the effectiveness of a particular intervention. It may not be a systematic review.

Search strategy : a description of the methodology used to locate and identify research articles pertinent to a systematic review, as specified within the relevant protocol. It includes a list of search terms, based on the subject, intervention and outcome of the review, to be used when searching electronic databases, websites, reference lists and when engaging with personal contacts. If required, the strategy may be modified once the search has commenced.

Sensitivity analysis : repetition of the analysis using different sets of assumptions in order to determine the impact of variation arising from these assumptions, or uncertain decisions, on the results of a systematic review.

Subgroup analysis : used to determine if the effects of an intervention vary between subgroups in the systematic review.

Weighted mean difference : a method used to combine measures on continuous scales (where the mean, standard deviation and sample size in each group are known) and the weight given to each study is determined by the precision of its estimate of effect.

Cochrane Systematic Review and SEA-ORCHID Project

Cochrane systematic reviews combine the results of the best medical research using rigorous methods, and are regarded as the gold standard of reference for health care professionals. Malaysia has relatively minor involvement in Cochrane Collaboration despite its economic growth and the fast improving standard of medical care. It is likely that clinical questions with high relevance to Malaysia are therefore not being addressed in Cochrane reviews.

The SEA-ORCHID project, which stands for South East Asia Optimising Reproductive and Child Health Outcomes in Developing Countries Project, is a five-year project (2003 to 2008) aiming to promote the synthesis and application of high level clinical evidence on issues relevant to this region, focusing on maternal and child health but also involving other related disciplines. Jointly funded by the Wellcome Trust and the Australian National Health and Medical Research Council and supported by the Cochrane Australasian Centre, the project activities include regular Cochrane Systematic Review Workshop and work-in sessions throughout the country. This is a good opportunity for the pool of clinical and research talents in our country to contribute in synthesizing the best clinical evidence and making a significant impact on evidence-based health care.

If you are interested in authoring or co-authoring a Cochrane review, you will be guided at every step by experienced reviewers leading to its publication in the Cochrane Library. In this workshop, you will also hear the experiences of people who are in the process of developing a protocol or review.

For further information please contact:

• Professor Jackie Ho [email protected]
• Dr Lai Nai Ming [email protected]

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• Research Papers

How to Write a Summary of a Research Paper

Last Updated: July 10, 2020 References

This article was co-authored by wikiHow staff writer, Hannah Madden . Hannah Madden is a writer, editor, and artist currently living in Portland, Oregon. In 2018, she graduated from Portland State University with a B.S. in Environmental Studies. Hannah enjoys writing articles about conservation, sustainability, and eco-friendly products. When she isn’t writing, you can find Hannah working on hand embroidery projects and listening to music. This article has been viewed 27,766 times. Learn more...

Writing a summary of an academic research paper is an important skill, and it shows that you understand all of the relevant information presented to you. However, writing a summary can be tough, since it requires you to be completely objective and keep any analysis or criticisms to yourself. By keeping your goal in mind as you read the paper and focusing on the key points, you can write a succinct, accurate summary of a research paper to prove that you understood the overall conclusion.

Reading the Research Paper

• For instance, if you’re supporting an argument in your own research paper, focus on the elements that are similar to yours.
• Or, if you’re comparing and contrasting methodology, focus on the methods and the significance of the results.

• You can also read the abstract of the paper as a good example of what the authors find to be important in their article.

• Depending on how long and dense the paper is, your initial reading could take you up to an hour or more.

• The important information will usually be toward the end of the paper as the authors explain their findings and conclusions.

• Writing a summary without plagiarizing, or copying the paper, is really important. Writing notes in your own words will help you get into the mindset of relaying information in your own way.

Including Relevant Information

• For example, “The methods used in this paper are not up to standards and require more testing to be conclusive.” is an analysis.
• ”The methods used in this paper include an in-depth survey and interview session with each candidate.” is a summary.

• If you’re writing a summary for class, your professor may specify how long your summary should be.
• Some summaries can even be as short as one sentence.

• ”Environmental conditions in North Carolina pose a threat to frogs and toads.”

• For example: “According to the climate model, frog and toad populations have been decreasing at a rapid rate over the past 10 years, and are on track to decrease even further in the coming years.”

• For example: “Smith and Herman (2008) argue that by decreasing greenhouse gases, frog and toad populations could reach historical levels within 20 years, and the climate model projections support that statement.”
• You can add in the authors and year of publication at any time during your summary.

• If you have time, try reading your summary to someone who hasn’t read the original paper and see if they understand the key points of the article.

Expert Q&A

• Make sure you fully understand the paper before you start writing the summary. Thanks Helpful 2 Not Helpful 0

• Plagiarism can have serious consequences in the academic world, so make sure you’re writing your summary in your own words. [12] X Research source Thanks Helpful 0 Not Helpful 0

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• ↑ https://writingcenter.uconn.edu/wp-content/uploads/sites/593/2014/06/How_to_Summarize_a_Research_Article1.pdf
• ↑ https://www.ufv.ca/media/assets/academic-success-centre/handouts/Summarizing-a-Scholarly-Journal-Article-rev2018.pdf
• ↑ https://integrity.mit.edu/handbook/academic-writing/summarizing
• ↑ https://writingcenter.unc.edu/tips-and-tools/summary-using-it-wisely/
• ↑ https://davidson.libguides.com/c.php?g=349327&p=2361763

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• Research Summary: What Is It & How To Write One

Introduction

A research summary is a requirement during academic research and sometimes you might need to prepare a research summary during a research project for an organization.

Most people find a research summary a daunting task as you are required to condense complex research material into an informative, easy-to-understand article most times with a minimum of 300-500 words.

In this post, we will guide you through all the steps required to make writing your research summary an easier task. 

What is a Research Summary?

A research summary is a piece of writing that summarizes the research of a specific topic into bite-size easy-to-read and comprehend articles. The primary goal is to give the reader a detailed outline of the key findings of a research.

It is an unavoidable requirement in colleges and universities. To write a good research summary, you must understand the goal of your research, as this would help make the process easier. 

A research summary preserves the structure and sections of the article it is derived from.

Research Summary or Abstract: What’s The Difference?

The Research Summary and Abstract are similar, especially as they are both brief, straight to the point, and provide an overview of the entire research paper. However, there are very clear differences.

To begin with, a Research summary is written at the end of a research activity, while the Abstract is written at the beginning of a research paper. 

A Research Summary captures the main points of a study, with an emphasis on the topic, method , and discoveries, an Abstract is a description of what your research paper would talk about and the reason for your research or the hypothesis you are trying to validate.

Let us take a deeper look at the difference between both terms.

What is an Abstract?

An abstract is a short version of a research paper. It is written to convey the findings of the research to the reader. It provides the reader with information that would help them understand the research, by giving them a clear idea about the subject matter of a research paper. It is usually submitted before the presentation of a research paper.

What is a Summary?

A summary is a short form of an essay, a research paper, or a chapter in a book. A research summary is a narration of a research study, condensing the focal points of research to a shorter form, usually aligned with the same structure of the research study, from which the summary is derived.

What Is The Difference Between an Abstract and a Summary?

An abstract communicates the main points of a research paper, it includes the questions, major findings, the importance of the findings, etc.

An abstract reflects the perceptions of the author about a topic, while a research summary reflects the ideology of the research study that is being summarized.

Getting Started with a Research Summary

Before commencing a research summary, there is a need to understand the style and organization of the content you plan to summarize. There are three fundamental areas of the research that should be the focal point:

• When deciding on the content include a section that speaks to the importance of the research, and the techniques and tools used to arrive at your conclusion.
• Keep the summary well organized, and use paragraphs to discuss the various sections of the research.
• Restrict your research to 300-400 words which is the standard practice for research summaries globally. However, if the research paper you want to summarize is a lengthy one, do not exceed 10% of the entire research material.

Once you have satisfied the requirements of the fundamentals for starting your research summary, you can now begin to write using the following format:

• Why was this research done?   – A clear description of the reason the research was embarked on and the hypothesis being tested.
• Who was surveyed? – Your research study should have details of the source of your information. If it was via a survey, you should document who the participants of the survey were and the reason that they were selected.
• What was the methodology? – Discuss the methodology, in terms of what kind of survey method did you adopt. Was it a face-to-face interview, a phone interview, or a focus group setting?
• What were the key findings? – This is perhaps the most vital part of the process. What discoveries did you make after the testing? This part should be based on raw facts free from any personal bias.
• Conclusion – What conclusions did you draw from the findings?
• Takeaways and action points – This is where your views and perception can be reflected. Here, you can now share your recommendations or action points.
• Identify the focal point of the article –  In other to get a grasp of the content covered in the research paper, you can skim the article first, in a bid to understand the most essential part of the research paper. 
• Analyze and understand the topic and article – Writing a summary of a research paper involves being familiar with the topic –  the current state of knowledge, key definitions, concepts, and models. This is often gleaned while reading the literature review. Please note that only a deep understanding ensures efficient and accurate summarization of the content.
• Make notes as you read – Highlight and summarize each paragraph as you read. Your notes are what you would further condense to create a draft that would form your research summary.

How to Structure Your Research Summary

• Title – This highlights the area of analysis, and can be formulated to briefly highlight key findings.
• Abstract – this is a very brief and comprehensive description of the study, required in every academic article, with a length of 100-500 words at most. 
• Introduction – this is a vital part of any research summary, it provides the context and the literature review that gently introduces readers to the subject matter. The introduction usually covers definitions, questions, and hypotheses of the research study. 
• Methodology –This section emphasizes the process and or data analysis methods used, in terms of experiments, surveys, sampling, or statistical analysis. 
• Results section – this section lists in detail the results derived from the research with evidence obtained from all the experiments conducted.
• Discussion – these parts discuss the results within the context of current knowledge among subject matter experts. Interpretation of results and theoretical models explaining the observed results, the strengths of the study, and the limitations experienced are going to be a part of the discussion. 
• Conclusion – In a conclusion, hypotheses are discussed and revalidated or denied, based on how convincing the evidence is.
• References – this section is for giving credit to those who work you studied to create your summary. You do this by providing appropriate citations as you write.

Research Summary Example 1

Below are some defining elements of a sample research summary.

Title – “The probability of an unexpected volcanic eruption in Greenwich”

Introduction – this section would list the catastrophic consequences that occurred in the country and the importance of analyzing this event. 

Hypothesis –  An eruption of the Greenwich supervolcano would be preceded by intense preliminary activity manifesting in advance, before the eruption.

Results – these could contain a report of statistical data from various volcanic eruptions happening globally while looking critically at the activity that occurred before these events. 

Discussion and conclusion – Given that Greenwich is now consistently monitored by scientists and that signs of an eruption are usually detected before the volcanic eruption, this confirms the hypothesis. Hence creating an emergency plan outlining other intervention measures and ultimately evacuation is essential. 

Research Summary Example 2

Below is another sample sketch.

Title – “The frequency of extreme weather events in the UK in 2000-2008 as compared to the ‘60s”

Introduction – Weather events bring intense material damage and cause pain to the victims affected.

Hypothesis – Extreme weather events are more frequent in recent times compared to the ‘50s

Results – The frequency of several categories of extreme events now and then are listed here, such as droughts, fires, massive rainfall/snowfalls, floods, hurricanes, tornadoes, etc.

Discussion and conclusion – Several types of extreme events have become more commonplace in recent times, confirming the hypothesis. This rise in extreme weather events can be traced to rising CO2 levels and increasing temperatures and global warming explain the rising frequency of these disasters. Addressing the rising CO2 levels and paying attention to climate change is the only to combat this phenomenon.

A research summary is the short form of a research paper, analyzing the important aspect of the study. Everyone who reads a research summary has a full grasp of the main idea being discussed in the original research paper. Conducting any research means you will write a summary, which is an important part of your project and would be the most read part of your project.

Having a guideline before you start helps, this would form your checklist which would guide your actions as you write your research summary. It is important to note that a Research Summary is different from an Abstract paper written at the beginning of a research paper, describing the idea behind a research paper.

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• Write a Literature Review
• Sheridan Libraries
• Find This link opens in a new window
• Evaluate This link opens in a new window

Not every source you found should be included in your annotated bibliography or lit review. Only include the most relevant and most important sources.

Get Organized

• Lit Review Prep Use this template to help you evaluate your sources, create article summaries for an annotated bibliography, and a synthesis matrix for your lit review outline.

Summarize your Sources

Summarize each source: Determine the most important and relevant information from each source, such as the findings, methodology, theories, etc.  Consider using an article summary, or study summary to help you organize and summarize your sources.

Paraphrasing

• Use your own words, and do not copy and paste the abstract
• The library's tutorials about plagiarism are excellent, and will help you with paraphasing correctly

Annotated Bibliographies

     Annotated bibliographies can help you clearly see and understand the research before diving into organizing and writing your literature review.        Although typically part of the "summarize" step of the literature review, annotations should not merely be summaries of each article - instead, they should be critical evaluations of the source, and help determine a source's usefulness for your lit review.  

Definition:

A list of citations on a particular topic followed by an evaluation of the source’s argument and other relevant material including its intended audience, sources of evidence, and methodology

• Explore your topic.
• Appraise issues or factors associated with your professional practice and research topic.
• Help you get started with the literature review.
• Think critically about your topic, and the literature.

Steps to Creating an Annotated Bibliography:

• Find Your Sources
• Read Your Sources
• Identify the Most Relevant Sources
• Cite your Sources
• Write Annotations

Annotated Bibliography Resources

• Purdue Owl Guide
• Cornell Annotated Bibliography Guide
• << Previous: Evaluate
• Next: Synthesize >>
• Last Updated: Sep 26, 2023 10:25 AM
• URL: https://guides.library.jhu.edu/lit-review

How to Synthesize Written Information from Multiple Sources

Shona McCombes

Content Manager

B.A., English Literature, University of Glasgow

Shona McCombes is the content manager at Scribbr, Netherlands.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

When you write a literature review or essay, you have to go beyond just summarizing the articles you’ve read – you need to synthesize the literature to show how it all fits together (and how your own research fits in).

Synthesizing simply means combining. Instead of summarizing the main points of each source in turn, you put together the ideas and findings of multiple sources in order to make an overall point.

At the most basic level, this involves looking for similarities and differences between your sources. Your synthesis should show the reader where the sources overlap and where they diverge.

Unsynthesized Example

Franz (2008) studied undergraduate online students. He looked at 17 females and 18 males and found that none of them liked APA. According to Franz, the evidence suggested that all students are reluctant to learn citations style. Perez (2010) also studies undergraduate students. She looked at 42 females and 50 males and found that males were significantly more inclined to use citation software ( p < .05). Findings suggest that females might graduate sooner. Goldstein (2012) looked at British undergraduates. Among a sample of 50, all females, all confident in their abilities to cite and were eager to write their dissertations.

Synthesized Example

Studies of undergraduate students reveal conflicting conclusions regarding relationships between advanced scholarly study and citation efficacy. Although Franz (2008) found that no participants enjoyed learning citation style, Goldstein (2012) determined in a larger study that all participants watched felt comfortable citing sources, suggesting that variables among participant and control group populations must be examined more closely. Although Perez (2010) expanded on Franz’s original study with a larger, more diverse sample…

Step 1: Organize your sources

After collecting the relevant literature, you’ve got a lot of information to work through, and no clear idea of how it all fits together.

Before you can start writing, you need to organize your notes in a way that allows you to see the relationships between sources.

One way to begin synthesizing the literature is to put your notes into a table. Depending on your topic and the type of literature you’re dealing with, there are a couple of different ways you can organize this.

Summary table

A summary table collates the key points of each source under consistent headings. This is a good approach if your sources tend to have a similar structure – for instance, if they’re all empirical papers.

Each row in the table lists one source, and each column identifies a specific part of the source. You can decide which headings to include based on what’s most relevant to the literature you’re dealing with.

For example, you might include columns for things like aims, methods, variables, population, sample size, and conclusion.

For each study, you briefly summarize each of these aspects. You can also include columns for your own evaluation and analysis.

The summary table gives you a quick overview of the key points of each source. This allows you to group sources by relevant similarities, as well as noticing important differences or contradictions in their findings.

Synthesis matrix

A synthesis matrix is useful when your sources are more varied in their purpose and structure – for example, when you’re dealing with books and essays making various different arguments about a topic.

Each column in the table lists one source. Each row is labeled with a specific concept, topic or theme that recurs across all or most of the sources.

Then, for each source, you summarize the main points or arguments related to the theme.

The purposes of the table is to identify the common points that connect the sources, as well as identifying points where they diverge or disagree.

Step 2: Outline your structure

Now you should have a clear overview of the main connections and differences between the sources you’ve read. Next, you need to decide how you’ll group them together and the order in which you’ll discuss them.

For shorter papers, your outline can just identify the focus of each paragraph; for longer papers, you might want to divide it into sections with headings.

There are a few different approaches you can take to help you structure your synthesis.

If your sources cover a broad time period, and you found patterns in how researchers approached the topic over time, you can organize your discussion chronologically .

That doesn’t mean you just summarize each paper in chronological order; instead, you should group articles into time periods and identify what they have in common, as well as signalling important turning points or developments in the literature.

If the literature covers various different topics, you can organize it thematically .

That means that each paragraph or section focuses on a specific theme and explains how that theme is approached in the literature.

Source Used with Permission: The Chicago School

If you’re drawing on literature from various different fields or they use a wide variety of research methods, you can organize your sources methodologically .

That means grouping together studies based on the type of research they did and discussing the findings that emerged from each method.

If your topic involves a debate between different schools of thought, you can organize it theoretically .

That means comparing the different theories that have been developed and grouping together papers based on the position or perspective they take on the topic, as well as evaluating which arguments are most convincing.

Step 3: Write paragraphs with topic sentences

What sets a synthesis apart from a summary is that it combines various sources. The easiest way to think about this is that each paragraph should discuss a few different sources, and you should be able to condense the overall point of the paragraph into one sentence.

This is called a topic sentence , and it usually appears at the start of the paragraph. The topic sentence signals what the whole paragraph is about; every sentence in the paragraph should be clearly related to it.

A topic sentence can be a simple summary of the paragraph’s content:

“Early research on [x] focused heavily on [y].”

For an effective synthesis, you can use topic sentences to link back to the previous paragraph, highlighting a point of debate or critique:

“Several scholars have pointed out the flaws in this approach.” “While recent research has attempted to address the problem, many of these studies have methodological flaws that limit their validity.”

By using topic sentences, you can ensure that your paragraphs are coherent and clearly show the connections between the articles you are discussing.

As you write your paragraphs, avoid quoting directly from sources: use your own words to explain the commonalities and differences that you found in the literature.

Don’t try to cover every single point from every single source – the key to synthesizing is to extract the most important and relevant information and combine it to give your reader an overall picture of the state of knowledge on your topic.

Step 4: Revise, edit and proofread

Like any other piece of academic writing, synthesizing literature doesn’t happen all in one go – it involves redrafting, revising, editing and proofreading your work.

Checklist for Synthesis

•   Do I introduce the paragraph with a clear, focused topic sentence?
•   Do I discuss more than one source in the paragraph?
•   Do I mention only the most relevant findings, rather than describing every part of the studies?
•   Do I discuss the similarities or differences between the sources, rather than summarizing each source in turn?
•   Do I put the findings or arguments of the sources in my own words?
•   Is the paragraph organized around a single idea?
•   Is the paragraph directly relevant to my research question or topic?
•   Is there a logical transition from this paragraph to the next one?

Further Information

How to Synthesise: a Step-by-Step Approach

Help…I”ve Been Asked to Synthesize!

Learn how to Synthesise (combine information from sources)

How to write a Psychology Essay

How to Summarize Any Research Article Better: Proven Tips Outlined

Rev › Blog › Marketing › How to Summarize Any Research Article Better: Proven Tips Outlined

You’ve got content gold on your hands—  primary  and  secondary  research materials from some of the top market research companies. Now, it’s time to decide how it relates to your products, project, or consumers. What’s more, you need to distill each article’s essential parts into easy-to-read, accurate, informative, and, most importantly, concise summaries. Overwhelming? Maybe. Impossible? Heck no; you just need a good strategy. So, where to start?

You’ve landed on the right page! These tips and techniques provide a template to help guide you through the process. 

Know Your Focus

The streaming TV hit,  Cobra Kai,  brings to mind Mr. Miyagi’s age-old wisdom– ‘Focus, Daniel-San.’ Focus is vital, as some sections of a research article are more relevant to your strategy than others. 

For example, a summary crafted for a school project or a university may focus on the experiment itself. In contrast, the article’s results and discussion sections may be more relevant to consumer marketing or for a business model.

Once you establish your focus, you’re less likely to waste time.

Read The Research Article

But before you do, let’s look at the makeup of these articles. Market research, focus group data, and surveys usually consist of five or more sections.

• An abstract or hypothesis
• Explanation of the methods used
• Tests or experiments performed
• Summation and or discussion of the results
• A list of references or source materials

Read The Abstract

Since some of the research articles you find will not work for your purpose, you should always start with the abstract. It’s an overview of the data and explains the purpose of the study as well as the expected results. So you’ll know whether to include the article or move on to the next piece of research.

Take Good Notes

The next step– read the article from abstract to references. But be prepared! Your mind may wander when faced with numbers, statistics, and long-winded wording. So grab your highlighter and pen and start taking notes.

Depending on the space available, you can write your notes in the margin. If you’re in a time crunch, check out  Rev . We’ve designed a convenient application perfect for taking notes! Download our  Voice Recorder App  for free and read your notes out loud. You’ll get a 99% accurate transcription of your summary notes sent to your email or account with a simple tap. 

Research Hack:  As an overview, a research article may not include every insight from the participants, interviews, or market data. Take a look at the references. You may find some hidden gems that will help your strategy stand out.    

Outline Your Thoughts

You’ve made notes, sifted through the numbers and statistics; but, there’s still a ton of information. An outline will make your writing process much more efficient. Although each research article is relatively straight-forward, you want your summary to stay on strategy.

Write A Summary

Okay, you’re ready to condense someone else’s work. Rather than stress over grammar and length, take the pressure off by writing a rough draft. Use key points from your notes, REV transcriptions, your outline, and the research article’s sections as your guide. 

Identify The Goal And The Methods Used

Like the author’s abstract, the beginning of your summary should address the research article’s fundamental objective .  This section may also include critical details about demographics, customer behavior, or trends. When summarizing, consider three key questions. 

• What is the goal of the research?
• What methods did the author(s) use?
• Are potential obstacles to success listed?

Methods vary in market research. You may have focus groups ,  in-depth interviews , or online discussions. Depending on the reason for your summary, the raw audio or video clips used in the study may hold nuggets. If full transcripts aren’t available, save time by uploading the clips to Rev. Our human transcription service costs $1.50 per minute, and we offer a 99% accuracy guarantee. We also offer a more cost-effective A.I. speech-to-text solution for only $0.25 per minute .

Describe The Observations

The experiment is the “meat” of the research. In your own words, briefly explain what the author(s) observed as the testing played out in real-time. You can talk about the time it took participants to complete tasks or directives. Were they excited about the client’s brand or disinterested? Basically, you’re recapping the participant’s reactions. 

Discuss The Outcome

As with any study, the results make or break the goal of the research. Was the test successful? Was anyone surprised by the outcome, or were there any unexpected developments? Pay careful attention to detail as you layout all conclusions reached by the author(s).

Article Summary Quick Tips: Do This, Not That

Is your head spinning yet? You can simplify the editing process by following these technical takeaways.

• Be Careful Not To Draw Your Own Conclusions:  You are summarizing the results of the research. The last thing you want to do is editorialize your summary. To avoid this, use the third-person point of view and present tense.  
• Keep Your Copy Clean And Free Of Errors:  Reread your text. Eliminate words like “that,” “in fact,” “however,” and adverbs. Make sure your summary is accurate. Then, use free websites like  Hemingway App  or paid services such as  Grammarly  to check for grammar or spelling issues. 
• Watch For Plagiarism:  Unless you’re using a word coined by the researcher, paraphrase your text. If you notice similar wording in your summary, reread the article so you can explain the data in your own words.
• Cite Your Sources: Steer clear of directly quoting the research. It’s best to paraphrase the data and reference the source using: the name of the university, the name of the journal and year of publication, or the name of the researcher, team, or society and year of study.  

Finalize Your Article Summary

Remember, you want your summary to be clear, straight-forward, and compelling. The market research article or study you’ve chosen may prove vital to you or your client’s business strategy and brand analysis. Take your time. Read and reread your summary. Make sure it’s representative of the research. And always triple-check your text for technical and factual accuracy.

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Cochrane Training

Chapter 15: interpreting results and drawing conclusions.

Holger J Schünemann, Gunn E Vist, Julian PT Higgins, Nancy Santesso, Jonathan J Deeks, Paul Glasziou, Elie A Akl, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group

Key Points:

• This chapter provides guidance on interpreting the results of synthesis in order to communicate the conclusions of the review effectively.
• Methods are presented for computing, presenting and interpreting relative and absolute effects for dichotomous outcome data, including the number needed to treat (NNT).
• For continuous outcome measures, review authors can present summary results for studies using natural units of measurement or as minimal important differences when all studies use the same scale. When studies measure the same construct but with different scales, review authors will need to find a way to interpret the standardized mean difference, or to use an alternative effect measure for the meta-analysis such as the ratio of means.
• Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values, but report the confidence interval together with the exact P value.
• Review authors should not make recommendations about healthcare decisions, but they can – after describing the certainty of evidence and the balance of benefits and harms – highlight different actions that might be consistent with particular patterns of values and preferences and other factors that determine a decision such as cost.

Cite this chapter as: Schünemann HJ, Vist GE, Higgins JPT, Santesso N, Deeks JJ, Glasziou P, Akl EA, Guyatt GH. Chapter 15: Interpreting results and drawing conclusions. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

15.1 Introduction

The purpose of Cochrane Reviews is to facilitate healthcare decisions by patients and the general public, clinicians, guideline developers, administrators and policy makers. They also inform future research. A clear statement of findings, a considered discussion and a clear presentation of the authors’ conclusions are, therefore, important parts of the review. In particular, the following issues can help people make better informed decisions and increase the usability of Cochrane Reviews:

• information on all important outcomes, including adverse outcomes;
• the certainty of the evidence for each of these outcomes, as it applies to specific populations and specific interventions; and
• clarification of the manner in which particular values and preferences may bear on the desirable and undesirable consequences of the intervention.

A ‘Summary of findings’ table, described in Chapter 14 , Section 14.1 , provides key pieces of information about health benefits and harms in a quick and accessible format. It is highly desirable that review authors include a ‘Summary of findings’ table in Cochrane Reviews alongside a sufficient description of the studies and meta-analyses to support its contents. This description includes the rating of the certainty of evidence, also called the quality of the evidence or confidence in the estimates of the effects, which is expected in all Cochrane Reviews.

‘Summary of findings’ tables are usually supported by full evidence profiles which include the detailed ratings of the evidence (Guyatt et al 2011a, Guyatt et al 2013a, Guyatt et al 2013b, Santesso et al 2016). The Discussion section of the text of the review provides space to reflect and consider the implications of these aspects of the review’s findings. Cochrane Reviews include five standard subheadings to ensure the Discussion section places the review in an appropriate context: ‘Summary of main results (benefits and harms)’; ‘Potential biases in the review process’; ‘Overall completeness and applicability of evidence’; ‘Certainty of the evidence’; and ‘Agreements and disagreements with other studies or reviews’. Following the Discussion, the Authors’ conclusions section is divided into two standard subsections: ‘Implications for practice’ and ‘Implications for research’. The assessment of the certainty of evidence facilitates a structured description of the implications for practice and research.

Because Cochrane Reviews have an international audience, the Discussion and Authors’ conclusions should, so far as possible, assume a broad international perspective and provide guidance for how the results could be applied in different settings, rather than being restricted to specific national or local circumstances. Cultural differences and economic differences may both play an important role in determining the best course of action based on the results of a Cochrane Review. Furthermore, individuals within societies have widely varying values and preferences regarding health states, and use of societal resources to achieve particular health states. For all these reasons, and because information that goes beyond that included in a Cochrane Review is required to make fully informed decisions, different people will often make different decisions based on the same evidence presented in a review.

Thus, review authors should avoid specific recommendations that inevitably depend on assumptions about available resources, values and preferences, and other factors such as equity considerations, feasibility and acceptability of an intervention. The purpose of the review should be to present information and aid interpretation rather than to offer recommendations. The discussion and conclusions should help people understand the implications of the evidence in relation to practical decisions and apply the results to their specific situation. Review authors can aid this understanding of the implications by laying out different scenarios that describe certain value structures.

In this chapter, we address first one of the key aspects of interpreting findings that is also fundamental in completing a ‘Summary of findings’ table: the certainty of evidence related to each of the outcomes. We then provide a more detailed consideration of issues around applicability and around interpretation of numerical results, and provide suggestions for presenting authors’ conclusions.

15.2 Issues of indirectness and applicability

15.2.1 the role of the review author.

“A leap of faith is always required when applying any study findings to the population at large” or to a specific person. “In making that jump, one must always strike a balance between making justifiable broad generalizations and being too conservative in one’s conclusions” (Friedman et al 1985). In addition to issues about risk of bias and other domains determining the certainty of evidence, this leap of faith is related to how well the identified body of evidence matches the posed PICO ( Population, Intervention, Comparator(s) and Outcome ) question. As to the population, no individual can be entirely matched to the population included in research studies. At the time of decision, there will always be differences between the study population and the person or population to whom the evidence is applied; sometimes these differences are slight, sometimes large.

The terms applicability, generalizability, external validity and transferability are related, sometimes used interchangeably and have in common that they lack a clear and consistent definition in the classic epidemiological literature (Schünemann et al 2013). However, all of the terms describe one overarching theme: whether or not available research evidence can be directly used to answer the health and healthcare question at hand, ideally supported by a judgement about the degree of confidence in this use (Schünemann et al 2013). GRADE’s certainty domains include a judgement about ‘indirectness’ to describe all of these aspects including the concept of direct versus indirect comparisons of different interventions (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011b).

To address adequately the extent to which a review is relevant for the purpose to which it is being put, there are certain things the review author must do, and certain things the user of the review must do to assess the degree of indirectness. Cochrane and the GRADE Working Group suggest using a very structured framework to address indirectness. We discuss here and in Chapter 14 what the review author can do to help the user. Cochrane Review authors must be extremely clear on the population, intervention and outcomes that they intend to address. Chapter 14, Section 14.1.2 , also emphasizes a crucial step: the specification of all patient-important outcomes relevant to the intervention strategies under comparison.

In considering whether the effect of an intervention applies equally to all participants, and whether different variations on the intervention have similar effects, review authors need to make a priori hypotheses about possible effect modifiers, and then examine those hypotheses (see Chapter 10, Section 10.10 and Section 10.11 ). If they find apparent subgroup effects, they must ultimately decide whether or not these effects are credible (Sun et al 2012). Differences between subgroups, particularly those that correspond to differences between studies, should be interpreted cautiously. Some chance variation between subgroups is inevitable so, unless there is good reason to believe that there is an interaction, review authors should not assume that the subgroup effect exists. If, despite due caution, review authors judge subgroup effects in terms of relative effect estimates as credible (i.e. the effects differ credibly), they should conduct separate meta-analyses for the relevant subgroups, and produce separate ‘Summary of findings’ tables for those subgroups.

The user of the review will be challenged with ‘individualization’ of the findings, whether they seek to apply the findings to an individual patient or a policy decision in a specific context. For example, even if relative effects are similar across subgroups, absolute effects will differ according to baseline risk. Review authors can help provide this information by identifying identifiable groups of people with varying baseline risks in the ‘Summary of findings’ tables, as discussed in Chapter 14, Section 14.1.3 . Users can then identify their specific case or population as belonging to a particular risk group, if relevant, and assess their likely magnitude of benefit or harm accordingly. A description of the identifying prognostic or baseline risk factors in a brief scenario (e.g. age or gender) will help users of a review further.

Another decision users must make is whether their individual case or population of interest is so different from those included in the studies that they cannot use the results of the systematic review and meta-analysis at all. Rather than rigidly applying the inclusion and exclusion criteria of studies, it is better to ask whether or not there are compelling reasons why the evidence should not be applied to a particular patient. Review authors can sometimes help decision makers by identifying important variation where divergence might limit the applicability of results (Rothwell 2005, Schünemann et al 2006, Guyatt et al 2011b, Schünemann et al 2013), including biologic and cultural variation, and variation in adherence to an intervention.

In addressing these issues, review authors cannot be aware of, or address, the myriad of differences in circumstances around the world. They can, however, address differences of known importance to many people and, importantly, they should avoid assuming that other people’s circumstances are the same as their own in discussing the results and drawing conclusions.

15.2.2 Biological variation

Issues of biological variation that may affect the applicability of a result to a reader or population include divergence in pathophysiology (e.g. biological differences between women and men that may affect responsiveness to an intervention) and divergence in a causative agent (e.g. for infectious diseases such as malaria, which may be caused by several different parasites). The discussion of the results in the review should make clear whether the included studies addressed all or only some of these groups, and whether any important subgroup effects were found.

15.2.3 Variation in context

Some interventions, particularly non-pharmacological interventions, may work in some contexts but not in others; the situation has been described as program by context interaction (Hawe et al 2004). Contextual factors might pertain to the host organization in which an intervention is offered, such as the expertise, experience and morale of the staff expected to carry out the intervention, the competing priorities for the clinician’s or staff’s attention, the local resources such as service and facilities made available to the program and the status or importance given to the program by the host organization. Broader context issues might include aspects of the system within which the host organization operates, such as the fee or payment structure for healthcare providers and the local insurance system. Some interventions, in particular complex interventions (see Chapter 17 ), can be only partially implemented in some contexts, and this requires judgements about indirectness of the intervention and its components for readers in that context (Schünemann 2013).

Contextual factors may also pertain to the characteristics of the target group or population, such as cultural and linguistic diversity, socio-economic position, rural/urban setting. These factors may mean that a particular style of care or relationship evolves between service providers and consumers that may or may not match the values and technology of the program.

For many years these aspects have been acknowledged when decision makers have argued that results of evidence reviews from other countries do not apply in their own country or setting. Whilst some programmes/interventions have been successfully transferred from one context to another, others have not (Resnicow et al 1993, Lumley et al 2004, Coleman et al 2015). Review authors should be cautious when making generalizations from one context to another. They should report on the presence (or otherwise) of context-related information in intervention studies, where this information is available.

15.2.4 Variation in adherence

Variation in the adherence of the recipients and providers of care can limit the certainty in the applicability of results. Predictable differences in adherence can be due to divergence in how recipients of care perceive the intervention (e.g. the importance of side effects), economic conditions or attitudes that make some forms of care inaccessible in some settings, such as in low-income countries (Dans et al 2007). It should not be assumed that high levels of adherence in closely monitored randomized trials will translate into similar levels of adherence in normal practice.

15.2.5 Variation in values and preferences

Decisions about healthcare management strategies and options involve trading off health benefits and harms. The right choice may differ for people with different values and preferences (i.e. the importance people place on the outcomes and interventions), and it is important that decision makers ensure that decisions are consistent with a patient or population’s values and preferences. The importance placed on outcomes, together with other factors, will influence whether the recipients of care will or will not accept an option that is offered (Alonso-Coello et al 2016) and, thus, can be one factor influencing adherence. In Section 15.6 , we describe how the review author can help this process and the limits of supporting decision making based on intervention reviews.

15.3 Interpreting results of statistical analyses

15.3.1 confidence intervals.

Results for both individual studies and meta-analyses are reported with a point estimate together with an associated confidence interval. For example, ‘The odds ratio was 0.75 with a 95% confidence interval of 0.70 to 0.80’. The point estimate (0.75) is the best estimate of the magnitude and direction of the experimental intervention’s effect compared with the comparator intervention. The confidence interval describes the uncertainty inherent in any estimate, and describes a range of values within which we can be reasonably sure that the true effect actually lies. If the confidence interval is relatively narrow (e.g. 0.70 to 0.80), the effect size is known precisely. If the interval is wider (e.g. 0.60 to 0.93) the uncertainty is greater, although there may still be enough precision to make decisions about the utility of the intervention. Intervals that are very wide (e.g. 0.50 to 1.10) indicate that we have little knowledge about the effect and this imprecision affects our certainty in the evidence, and that further information would be needed before we could draw a more certain conclusion.

A 95% confidence interval is often interpreted as indicating a range within which we can be 95% certain that the true effect lies. This statement is a loose interpretation, but is useful as a rough guide. The strictly correct interpretation of a confidence interval is based on the hypothetical notion of considering the results that would be obtained if the study were repeated many times. If a study were repeated infinitely often, and on each occasion a 95% confidence interval calculated, then 95% of these intervals would contain the true effect (see Section 15.3.3 for further explanation).

The width of the confidence interval for an individual study depends to a large extent on the sample size. Larger studies tend to give more precise estimates of effects (and hence have narrower confidence intervals) than smaller studies. For continuous outcomes, precision depends also on the variability in the outcome measurements (i.e. how widely individual results vary between people in the study, measured as the standard deviation); for dichotomous outcomes it depends on the risk of the event (more frequent events allow more precision, and narrower confidence intervals), and for time-to-event outcomes it also depends on the number of events observed. All these quantities are used in computation of the standard errors of effect estimates from which the confidence interval is derived.

The width of a confidence interval for a meta-analysis depends on the precision of the individual study estimates and on the number of studies combined. In addition, for random-effects models, precision will decrease with increasing heterogeneity and confidence intervals will widen correspondingly (see Chapter 10, Section 10.10.4 ). As more studies are added to a meta-analysis the width of the confidence interval usually decreases. However, if the additional studies increase the heterogeneity in the meta-analysis and a random-effects model is used, it is possible that the confidence interval width will increase.

Confidence intervals and point estimates have different interpretations in fixed-effect and random-effects models. While the fixed-effect estimate and its confidence interval address the question ‘what is the best (single) estimate of the effect?’, the random-effects estimate assumes there to be a distribution of effects, and the estimate and its confidence interval address the question ‘what is the best estimate of the average effect?’ A confidence interval may be reported for any level of confidence (although they are most commonly reported for 95%, and sometimes 90% or 99%). For example, the odds ratio of 0.80 could be reported with an 80% confidence interval of 0.73 to 0.88; a 90% interval of 0.72 to 0.89; and a 95% interval of 0.70 to 0.92. As the confidence level increases, the confidence interval widens.

There is logical correspondence between the confidence interval and the P value (see Section 15.3.3 ). The 95% confidence interval for an effect will exclude the null value (such as an odds ratio of 1.0 or a risk difference of 0) if and only if the test of significance yields a P value of less than 0.05. If the P value is exactly 0.05, then either the upper or lower limit of the 95% confidence interval will be at the null value. Similarly, the 99% confidence interval will exclude the null if and only if the test of significance yields a P value of less than 0.01.

Together, the point estimate and confidence interval provide information to assess the effects of the intervention on the outcome. For example, suppose that we are evaluating an intervention that reduces the risk of an event and we decide that it would be useful only if it reduced the risk of an event from 30% by at least 5 percentage points to 25% (these values will depend on the specific clinical scenario and outcomes, including the anticipated harms). If the meta-analysis yielded an effect estimate of a reduction of 10 percentage points with a tight 95% confidence interval, say, from 7% to 13%, we would be able to conclude that the intervention was useful since both the point estimate and the entire range of the interval exceed our criterion of a reduction of 5% for net health benefit. However, if the meta-analysis reported the same risk reduction of 10% but with a wider interval, say, from 2% to 18%, although we would still conclude that our best estimate of the intervention effect is that it provides net benefit, we could not be so confident as we still entertain the possibility that the effect could be between 2% and 5%. If the confidence interval was wider still, and included the null value of a difference of 0%, we would still consider the possibility that the intervention has no effect on the outcome whatsoever, and would need to be even more sceptical in our conclusions.

Review authors may use the same general approach to conclude that an intervention is not useful. Continuing with the above example where the criterion for an important difference that should be achieved to provide more benefit than harm is a 5% risk difference, an effect estimate of 2% with a 95% confidence interval of 1% to 4% suggests that the intervention does not provide net health benefit.

15.3.2 P values and statistical significance

A P value is the standard result of a statistical test, and is the probability of obtaining the observed effect (or larger) under a ‘null hypothesis’. In the context of Cochrane Reviews there are two commonly used statistical tests. The first is a test of overall effect (a Z-test), and its null hypothesis is that there is no overall effect of the experimental intervention compared with the comparator on the outcome of interest. The second is the (Chi 2 ) test for heterogeneity, and its null hypothesis is that there are no differences in the intervention effects across studies.

A P value that is very small indicates that the observed effect is very unlikely to have arisen purely by chance, and therefore provides evidence against the null hypothesis. It has been common practice to interpret a P value by examining whether it is smaller than particular threshold values. In particular, P values less than 0.05 are often reported as ‘statistically significant’, and interpreted as being small enough to justify rejection of the null hypothesis. However, the 0.05 threshold is an arbitrary one that became commonly used in medical and psychological research largely because P values were determined by comparing the test statistic against tabulations of specific percentage points of statistical distributions. If review authors decide to present a P value with the results of a meta-analysis, they should report a precise P value (as calculated by most statistical software), together with the 95% confidence interval. Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values , but report the confidence interval together with the exact P value (see MECIR Box 15.3.a ).

We discuss interpretation of the test for heterogeneity in Chapter 10, Section 10.10.2 ; the remainder of this section refers mainly to tests for an overall effect. For tests of an overall effect, the computation of P involves both the effect estimate and precision of the effect estimate (driven largely by sample size). As precision increases, the range of plausible effects that could occur by chance is reduced. Correspondingly, the statistical significance of an effect of a particular magnitude will usually be greater (the P value will be smaller) in a larger study than in a smaller study.

P values are commonly misinterpreted in two ways. First, a moderate or large P value (e.g. greater than 0.05) may be misinterpreted as evidence that the intervention has no effect on the outcome. There is an important difference between this statement and the correct interpretation that there is a high probability that the observed effect on the outcome is due to chance alone. To avoid such a misinterpretation, review authors should always examine the effect estimate and its 95% confidence interval.

The second misinterpretation is to assume that a result with a small P value for the summary effect estimate implies that an experimental intervention has an important benefit. Such a misinterpretation is more likely to occur in large studies and meta-analyses that accumulate data over dozens of studies and thousands of participants. The P value addresses the question of whether the experimental intervention effect is precisely nil; it does not examine whether the effect is of a magnitude of importance to potential recipients of the intervention. In a large study, a small P value may represent the detection of a trivial effect that may not lead to net health benefit when compared with the potential harms (i.e. harmful effects on other important outcomes). Again, inspection of the point estimate and confidence interval helps correct interpretations (see Section 15.3.1 ).

MECIR Box 15.3.a Relevant expectations for conduct of intervention reviews

15.3.3 Relation between confidence intervals, statistical significance and certainty of evidence

The confidence interval (and imprecision) is only one domain that influences overall uncertainty about effect estimates. Uncertainty resulting from imprecision (i.e. statistical uncertainty) may be no less important than uncertainty from indirectness, or any other GRADE domain, in the context of decision making (Schünemann 2016). Thus, the extent to which interpretations of the confidence interval described in Sections 15.3.1 and 15.3.2 correspond to conclusions about overall certainty of the evidence for the outcome of interest depends on these other domains. If there are no concerns about other domains that determine the certainty of the evidence (i.e. risk of bias, inconsistency, indirectness or publication bias), then the interpretation in Sections 15.3.1 and 15.3.2 . about the relation of the confidence interval to the true effect may be carried forward to the overall certainty. However, if there are concerns about the other domains that affect the certainty of the evidence, the interpretation about the true effect needs to be seen in the context of further uncertainty resulting from those concerns.

For example, nine randomized controlled trials in almost 6000 cancer patients indicated that the administration of heparin reduces the risk of venous thromboembolism (VTE), with a risk ratio of 43% (95% CI 19% to 60%) (Akl et al 2011a). For patients with a plausible baseline risk of approximately 4.6% per year, this relative effect suggests that heparin leads to an absolute risk reduction of 20 fewer VTEs (95% CI 9 fewer to 27 fewer) per 1000 people per year (Akl et al 2011a). Now consider that the review authors or those applying the evidence in a guideline have lowered the certainty in the evidence as a result of indirectness. While the confidence intervals would remain unchanged, the certainty in that confidence interval and in the point estimate as reflecting the truth for the question of interest will be lowered. In fact, the certainty range will have unknown width so there will be unknown likelihood of a result within that range because of this indirectness. The lower the certainty in the evidence, the less we know about the width of the certainty range, although methods for quantifying risk of bias and understanding potential direction of bias may offer insight when lowered certainty is due to risk of bias. Nevertheless, decision makers must consider this uncertainty, and must do so in relation to the effect measure that is being evaluated (e.g. a relative or absolute measure). We will describe the impact on interpretations for dichotomous outcomes in Section 15.4 .

15.4 Interpreting results from dichotomous outcomes (including numbers needed to treat)

15.4.1 relative and absolute risk reductions.

Clinicians may be more inclined to prescribe an intervention that reduces the relative risk of death by 25% than one that reduces the risk of death by 1 percentage point, although both presentations of the evidence may relate to the same benefit (i.e. a reduction in risk from 4% to 3%). The former refers to the relative reduction in risk and the latter to the absolute reduction in risk. As described in Chapter 6, Section 6.4.1 , there are several measures for comparing dichotomous outcomes in two groups. Meta-analyses are usually undertaken using risk ratios (RR), odds ratios (OR) or risk differences (RD), but there are several alternative ways of expressing results.

Relative risk reduction (RRR) is a convenient way of re-expressing a risk ratio as a percentage reduction:

For example, a risk ratio of 0.75 translates to a relative risk reduction of 25%, as in the example above.

The risk difference is often referred to as the absolute risk reduction (ARR) or absolute risk increase (ARI), and may be presented as a percentage (e.g. 1%), as a decimal (e.g. 0.01), or as account (e.g. 10 out of 1000). We consider different choices for presenting absolute effects in Section 15.4.3 . We then describe computations for obtaining these numbers from the results of individual studies and of meta-analyses in Section 15.4.4 .

15.4.2 Number needed to treat (NNT)

The number needed to treat (NNT) is a common alternative way of presenting information on the effect of an intervention. The NNT is defined as the expected number of people who need to receive the experimental rather than the comparator intervention for one additional person to either incur or avoid an event (depending on the direction of the result) in a given time frame. Thus, for example, an NNT of 10 can be interpreted as ‘it is expected that one additional (or less) person will incur an event for every 10 participants receiving the experimental intervention rather than comparator over a given time frame’. It is important to be clear that:

• since the NNT is derived from the risk difference, it is still a comparative measure of effect (experimental versus a specific comparator) and not a general property of a single intervention; and
• the NNT gives an ‘expected value’. For example, NNT = 10 does not imply that one additional event will occur in each and every group of 10 people.

NNTs can be computed for both beneficial and detrimental events, and for interventions that cause both improvements and deteriorations in outcomes. In all instances NNTs are expressed as positive whole numbers. Some authors use the term ‘number needed to harm’ (NNH) when an intervention leads to an adverse outcome, or a decrease in a positive outcome, rather than improvement. However, this phrase can be misleading (most notably, it can easily be read to imply the number of people who will experience a harmful outcome if given the intervention), and it is strongly recommended that ‘number needed to harm’ and ‘NNH’ are avoided. The preferred alternative is to use phrases such as ‘number needed to treat for an additional beneficial outcome’ (NNTB) and ‘number needed to treat for an additional harmful outcome’ (NNTH) to indicate direction of effect.

As NNTs refer to events, their interpretation needs to be worded carefully when the binary outcome is a dichotomization of a scale-based outcome. For example, if the outcome is pain measured on a ‘none, mild, moderate or severe’ scale it may have been dichotomized as ‘none or mild’ versus ‘moderate or severe’. It would be inappropriate for an NNT from these data to be referred to as an ‘NNT for pain’. It is an ‘NNT for moderate or severe pain’.

We consider different choices for presenting absolute effects in Section 15.4.3 . We then describe computations for obtaining these numbers from the results of individual studies and of meta-analyses in Section 15.4.4 .

15.4.3 Expressing risk differences

Users of reviews are liable to be influenced by the choice of statistical presentations of the evidence. Hoffrage and colleagues suggest that physicians’ inferences about statistical outcomes are more appropriate when they deal with ‘natural frequencies’ – whole numbers of people, both treated and untreated (e.g. treatment results in a drop from 20 out of 1000 to 10 out of 1000 women having breast cancer) – than when effects are presented as percentages (e.g. 1% absolute reduction in breast cancer risk) (Hoffrage et al 2000). Probabilities may be more difficult to understand than frequencies, particularly when events are rare. While standardization may be important in improving the presentation of research evidence (and participation in healthcare decisions), current evidence suggests that the presentation of natural frequencies for expressing differences in absolute risk is best understood by consumers of healthcare information (Akl et al 2011b). This evidence provides the rationale for presenting absolute risks in ‘Summary of findings’ tables as numbers of people with events per 1000 people receiving the intervention (see Chapter 14 ).

RRs and RRRs remain crucial because relative effects tend to be substantially more stable across risk groups than absolute effects (see Chapter 10, Section 10.4.3 ). Review authors can use their own data to study this consistency (Cates 1999, Smeeth et al 1999). Risk differences from studies are least likely to be consistent across baseline event rates; thus, they are rarely appropriate for computing numbers needed to treat in systematic reviews. If a relative effect measure (OR or RR) is chosen for meta-analysis, then a comparator group risk needs to be specified as part of the calculation of an RD or NNT. In addition, if there are several different groups of participants with different levels of risk, it is crucial to express absolute benefit for each clinically identifiable risk group, clarifying the time period to which this applies. Studies in patients with differing severity of disease, or studies with different lengths of follow-up will almost certainly have different comparator group risks. In these cases, different comparator group risks lead to different RDs and NNTs (except when the intervention has no effect). A recommended approach is to re-express an odds ratio or a risk ratio as a variety of RD or NNTs across a range of assumed comparator risks (ACRs) (McQuay and Moore 1997, Smeeth et al 1999). Review authors should bear these considerations in mind not only when constructing their ‘Summary of findings’ table, but also in the text of their review.

For example, a review of oral anticoagulants to prevent stroke presented information to users by describing absolute benefits for various baseline risks (Aguilar and Hart 2005, Aguilar et al 2007). They presented their principal findings as “The inherent risk of stroke should be considered in the decision to use oral anticoagulants in atrial fibrillation patients, selecting those who stand to benefit most for this therapy” (Aguilar and Hart 2005). Among high-risk atrial fibrillation patients with prior stroke or transient ischaemic attack who have stroke rates of about 12% (120 per 1000) per year, warfarin prevents about 70 strokes yearly per 1000 patients, whereas for low-risk atrial fibrillation patients (with a stroke rate of about 2% per year or 20 per 1000), warfarin prevents only 12 strokes. This presentation helps users to understand the important impact that typical baseline risks have on the absolute benefit that they can expect.

15.4.4 Computations

Direct computation of risk difference (RD) or a number needed to treat (NNT) depends on the summary statistic (odds ratio, risk ratio or risk differences) available from the study or meta-analysis. When expressing results of meta-analyses, review authors should use, in the computations, whatever statistic they determined to be the most appropriate summary for meta-analysis (see Chapter 10, Section 10.4.3 ). Here we present calculations to obtain RD as a reduction in the number of participants per 1000. For example, a risk difference of –0.133 corresponds to 133 fewer participants with the event per 1000.

RDs and NNTs should not be computed from the aggregated total numbers of participants and events across the trials. This approach ignores the randomization within studies, and may produce seriously misleading results if there is unbalanced randomization in any of the studies. Using the pooled result of a meta-analysis is more appropriate. When computing NNTs, the values obtained are by convention always rounded up to the next whole number.

15.4.4.1 Computing NNT from a risk difference (RD)

A NNT may be computed from a risk difference as

where the vertical bars (‘absolute value of’) in the denominator indicate that any minus sign should be ignored. It is convention to round the NNT up to the nearest whole number. For example, if the risk difference is –0.12 the NNT is 9; if the risk difference is –0.22 the NNT is 5. Cochrane Review authors should qualify the NNT as referring to benefit (improvement) or harm by denoting the NNT as NNTB or NNTH. Note that this approach, although feasible, should be used only for the results of a meta-analysis of risk differences. In most cases meta-analyses will be undertaken using a relative measure of effect (RR or OR), and those statistics should be used to calculate the NNT (see Section 15.4.4.2 and 15.4.4.3 ).

15.4.4.2 Computing risk differences or NNT from a risk ratio

To aid interpretation of the results of a meta-analysis of risk ratios, review authors may compute an absolute risk reduction or NNT. In order to do this, an assumed comparator risk (ACR) (otherwise known as a baseline risk, or risk that the outcome of interest would occur with the comparator intervention) is required. It will usually be appropriate to do this for a range of different ACRs. The computation proceeds as follows:

As an example, suppose the risk ratio is RR = 0.92, and an ACR = 0.3 (300 per 1000) is assumed. Then the effect on risk is 24 fewer per 1000:

The NNT is 42:

15.4.4.3 Computing risk differences or NNT from an odds ratio

Review authors may wish to compute a risk difference or NNT from the results of a meta-analysis of odds ratios. In order to do this, an ACR is required. It will usually be appropriate to do this for a range of different ACRs. The computation proceeds as follows:

As an example, suppose the odds ratio is OR = 0.73, and a comparator risk of ACR = 0.3 is assumed. Then the effect on risk is 62 fewer per 1000:

The NNT is 17:

15.4.4.4 Computing risk ratio from an odds ratio

Because risk ratios are easier to interpret than odds ratios, but odds ratios have favourable mathematical properties, a review author may decide to undertake a meta-analysis based on odds ratios, but to express the result as a summary risk ratio (or relative risk reduction). This requires an ACR. Then

It will often be reasonable to perform this transformation using the median comparator group risk from the studies in the meta-analysis.

15.4.4.5 Computing confidence limits

Confidence limits for RDs and NNTs may be calculated by applying the above formulae to the upper and lower confidence limits for the summary statistic (RD, RR or OR) (Altman 1998). Note that this confidence interval does not incorporate uncertainty around the ACR.

If the 95% confidence interval of OR or RR includes the value 1, one of the confidence limits will indicate benefit and the other harm. Thus, appropriate use of the words ‘fewer’ and ‘more’ is required for each limit when presenting results in terms of events. For NNTs, the two confidence limits should be labelled as NNTB and NNTH to indicate the direction of effect in each case. The confidence interval for the NNT will include a ‘discontinuity’, because increasingly smaller risk differences that approach zero will lead to NNTs approaching infinity. Thus, the confidence interval will include both an infinitely large NNTB and an infinitely large NNTH.

15.5 Interpreting results from continuous outcomes (including standardized mean differences)

15.5.1 meta-analyses with continuous outcomes.

Review authors should describe in the study protocol how they plan to interpret results for continuous outcomes. When outcomes are continuous, review authors have a number of options to present summary results. These options differ if studies report the same measure that is familiar to the target audiences, studies report the same or very similar measures that are less familiar to the target audiences, or studies report different measures.

15.5.2 Meta-analyses with continuous outcomes using the same measure

If all studies have used the same familiar units, for instance, results are expressed as durations of events, such as symptoms for conditions including diarrhoea, sore throat, otitis media, influenza or duration of hospitalization, a meta-analysis may generate a summary estimate in those units, as a difference in mean response (see, for instance, the row summarizing results for duration of diarrhoea in Chapter 14, Figure 14.1.b and the row summarizing oedema in Chapter 14, Figure 14.1.a ). For such outcomes, the ‘Summary of findings’ table should include a difference of means between the two interventions. However, when units of such outcomes may be difficult to interpret, particularly when they relate to rating scales (again, see the oedema row of Chapter 14, Figure 14.1.a ). ‘Summary of findings’ tables should include the minimum and maximum of the scale of measurement, and the direction. Knowledge of the smallest change in instrument score that patients perceive is important – the minimal important difference (MID) – and can greatly facilitate the interpretation of results (Guyatt et al 1998, Schünemann and Guyatt 2005). Knowing the MID allows review authors and users to place results in context. Review authors should state the MID – if known – in the Comments column of their ‘Summary of findings’ table. For example, the chronic respiratory questionnaire has possible scores in health-related quality of life ranging from 1 to 7 and 0.5 represents a well-established MID (Jaeschke et al 1989, Schünemann et al 2005).

15.5.3 Meta-analyses with continuous outcomes using different measures

When studies have used different instruments to measure the same construct, a standardized mean difference (SMD) may be used in meta-analysis for combining continuous data. Without guidance, clinicians and patients may have little idea how to interpret results presented as SMDs. Review authors should therefore consider issues of interpretability when planning their analysis at the protocol stage and should consider whether there will be suitable ways to re-express the SMD or whether alternative effect measures, such as a ratio of means, or possibly as minimal important difference units (Guyatt et al 2013b) should be used. Table 15.5.a and the following sections describe these options.

Table 15.5.a Approaches and their implications to presenting results of continuous variables when primary studies have used different instruments to measure the same construct. Adapted from Guyatt et al (2013b)

15.5.3.1 Presenting and interpreting SMDs using generic effect size estimates

The SMD expresses the intervention effect in standard units rather than the original units of measurement. The SMD is the difference in mean effects between the experimental and comparator groups divided by the pooled standard deviation of participants’ outcomes, or external SDs when studies are very small (see Chapter 6, Section 6.5.1.2 ). The value of a SMD thus depends on both the size of the effect (the difference between means) and the standard deviation of the outcomes (the inherent variability among participants or based on an external SD).

If review authors use the SMD, they might choose to present the results directly as SMDs (row 1a, Table 15.5.a and Table 15.5.b ). However, absolute values of the intervention and comparison groups are typically not useful because studies have used different measurement instruments with different units. Guiding rules for interpreting SMDs (or ‘Cohen’s effect sizes’) exist, and have arisen mainly from researchers in the social sciences (Cohen 1988). One example is as follows: 0.2 represents a small effect, 0.5 a moderate effect and 0.8 a large effect (Cohen 1988). Variations exist (e.g. <0.40=small, 0.40 to 0.70=moderate, >0.70=large). Review authors might consider including such a guiding rule in interpreting the SMD in the text of the review, and in summary versions such as the Comments column of a ‘Summary of findings’ table. However, some methodologists believe that such interpretations are problematic because patient importance of a finding is context-dependent and not amenable to generic statements.

15.5.3.2 Re-expressing SMDs using a familiar instrument

The second possibility for interpreting the SMD is to express it in the units of one or more of the specific measurement instruments used by the included studies (row 1b, Table 15.5.a and Table 15.5.b ). The approach is to calculate an absolute difference in means by multiplying the SMD by an estimate of the SD associated with the most familiar instrument. To obtain this SD, a reasonable option is to calculate a weighted average across all intervention groups of all studies that used the selected instrument (preferably a pre-intervention or post-intervention SD as discussed in Chapter 10, Section 10.5.2 ). To better reflect among-person variation in practice, or to use an instrument not represented in the meta-analysis, it may be preferable to use a standard deviation from a representative observational study. The summary effect is thus re-expressed in the original units of that particular instrument and the clinical relevance and impact of the intervention effect can be interpreted using that familiar instrument.

The same approach of re-expressing the results for a familiar instrument can also be used for other standardized effect measures such as when standardizing by MIDs (Guyatt et al 2013b): see Section 15.5.3.5 .

Table 15.5.b Application of approaches when studies have used different measures: effects of dexamethasone for pain after laparoscopic cholecystectomy (Karanicolas et al 2008). Reproduced with permission of Wolters Kluwer

1 Certainty rated according to GRADE from very low to high certainty. 2 Substantial unexplained heterogeneity in study results. 3 Imprecision due to wide confidence intervals. 4 The 20% comes from the proportion in the control group requiring rescue analgesia. 5 Crude (arithmetic) means of the post-operative pain mean responses across all five trials when transformed to a 100-point scale.

15.5.3.3 Re-expressing SMDs through dichotomization and transformation to relative and absolute measures

A third approach (row 1c, Table 15.5.a and Table 15.5.b ) relies on converting the continuous measure into a dichotomy and thus allows calculation of relative and absolute effects on a binary scale. A transformation of a SMD to a (log) odds ratio is available, based on the assumption that an underlying continuous variable has a logistic distribution with equal standard deviation in the two intervention groups, as discussed in Chapter 10, Section 10.6  (Furukawa 1999, Guyatt et al 2013b). The assumption is unlikely to hold exactly and the results must be regarded as an approximation. The log odds ratio is estimated as

(or approximately 1.81✕SMD). The resulting odds ratio can then be presented as normal, and in a ‘Summary of findings’ table, combined with an assumed comparator group risk to be expressed as an absolute risk difference. The comparator group risk in this case would refer to the proportion of people who have achieved a specific value of the continuous outcome. In randomized trials this can be interpreted as the proportion who have improved by some (specified) amount (responders), for instance by 5 points on a 0 to 100 scale. Table 15.5.c shows some illustrative results from this method. The risk differences can then be converted to NNTs or to people per thousand using methods described in Section 15.4.4 .

Table 15.5.c Risk difference derived for specific SMDs for various given ‘proportions improved’ in the comparator group (Furukawa 1999, Guyatt et al 2013b). Reproduced with permission of Elsevier 

15.5.3.4 Ratio of means

A more frequently used approach is based on calculation of a ratio of means between the intervention and comparator groups (Friedrich et al 2008) as discussed in Chapter 6, Section 6.5.1.3 . Interpretational advantages of this approach include the ability to pool studies with outcomes expressed in different units directly, to avoid the vulnerability of heterogeneous populations that limits approaches that rely on SD units, and for ease of clinical interpretation (row 2, Table 15.5.a and Table 15.5.b ). This method is currently designed for post-intervention scores only. However, it is possible to calculate a ratio of change scores if both intervention and comparator groups change in the same direction in each relevant study, and this ratio may sometimes be informative.

Limitations to this approach include its limited applicability to change scores (since it is unlikely that both intervention and comparator group changes are in the same direction in all studies) and the possibility of misleading results if the comparator group mean is very small, in which case even a modest difference from the intervention group will yield a large and therefore misleading ratio of means. It also requires that separate ratios of means be calculated for each included study, and then entered into a generic inverse variance meta-analysis (see Chapter 10, Section 10.3 ).

The ratio of means approach illustrated in Table 15.5.b suggests a relative reduction in pain of only 13%, meaning that those receiving steroids have a pain severity 87% of those in the comparator group, an effect that might be considered modest.

15.5.3.5 Presenting continuous results as minimally important difference units

To express results in MID units, review authors have two options. First, they can be combined across studies in the same way as the SMD, but instead of dividing the mean difference of each study by its SD, review authors divide by the MID associated with that outcome (Johnston et al 2010, Guyatt et al 2013b). Instead of SD units, the pooled results represent MID units (row 3, Table 15.5.a and Table 15.5.b ), and may be more easily interpretable. This approach avoids the problem of varying SDs across studies that may distort estimates of effect in approaches that rely on the SMD. The approach, however, relies on having well-established MIDs. The approach is also risky in that a difference less than the MID may be interpreted as trivial when a substantial proportion of patients may have achieved an important benefit.

The other approach makes a simple conversion (not shown in Table 15.5.b ), before undertaking the meta-analysis, of the means and SDs from each study to means and SDs on the scale of a particular familiar instrument whose MID is known. For example, one can rescale the mean and SD of other chronic respiratory disease instruments (e.g. rescaling a 0 to 100 score of an instrument) to a the 1 to 7 score in Chronic Respiratory Disease Questionnaire (CRQ) units (by assuming 0 equals 1 and 100 equals 7 on the CRQ). Given the MID of the CRQ of 0.5, a mean difference in change of 0.71 after rescaling of all studies suggests a substantial effect of the intervention (Guyatt et al 2013b). This approach, presenting in units of the most familiar instrument, may be the most desirable when the target audiences have extensive experience with that instrument, particularly if the MID is well established.

15.6 Drawing conclusions

15.6.1 conclusions sections of a cochrane review.

Authors’ conclusions in a Cochrane Review are divided into implications for practice and implications for research. While Cochrane Reviews about interventions can provide meaningful information and guidance for practice, decisions about the desirable and undesirable consequences of healthcare options require evidence and judgements for criteria that most Cochrane Reviews do not provide (Alonso-Coello et al 2016). In describing the implications for practice and the development of recommendations, however, review authors may consider the certainty of the evidence, the balance of benefits and harms, and assumed values and preferences.

15.6.2 Implications for practice

Drawing conclusions about the practical usefulness of an intervention entails making trade-offs, either implicitly or explicitly, between the estimated benefits, harms and the values and preferences. Making such trade-offs, and thus making specific recommendations for an action in a specific context, goes beyond a Cochrane Review and requires additional evidence and informed judgements that most Cochrane Reviews do not provide (Alonso-Coello et al 2016). Such judgements are typically the domain of clinical practice guideline developers for which Cochrane Reviews will provide crucial information (Graham et al 2011, Schünemann et al 2014, Zhang et al 2018a). Thus, authors of Cochrane Reviews should not make recommendations.

If review authors feel compelled to lay out actions that clinicians and patients could take, they should – after describing the certainty of evidence and the balance of benefits and harms – highlight different actions that might be consistent with particular patterns of values and preferences. Other factors that might influence a decision should also be highlighted, including any known factors that would be expected to modify the effects of the intervention, the baseline risk or status of the patient, costs and who bears those costs, and the availability of resources. Review authors should ensure they consider all patient-important outcomes, including those for which limited data may be available. In the context of public health reviews the focus may be on population-important outcomes as the target may be an entire (non-diseased) population and include outcomes that are not measured in the population receiving an intervention (e.g. a reduction of transmission of infections from those receiving an intervention). This process implies a high level of explicitness in judgements about values or preferences attached to different outcomes and the certainty of the related evidence (Zhang et al 2018b, Zhang et al 2018c); this and a full cost-effectiveness analysis is beyond the scope of most Cochrane Reviews (although they might well be used for such analyses; see Chapter 20 ).

A review on the use of anticoagulation in cancer patients to increase survival (Akl et al 2011a) provides an example for laying out clinical implications for situations where there are important trade-offs between desirable and undesirable effects of the intervention: “The decision for a patient with cancer to start heparin therapy for survival benefit should balance the benefits and downsides and integrate the patient’s values and preferences. Patients with a high preference for a potential survival prolongation, limited aversion to potential bleeding, and who do not consider heparin (both UFH or LMWH) therapy a burden may opt to use heparin, while those with aversion to bleeding may not.”

15.6.3 Implications for research

The second category for authors’ conclusions in a Cochrane Review is implications for research. To help people make well-informed decisions about future healthcare research, the ‘Implications for research’ section should comment on the need for further research, and the nature of the further research that would be most desirable. It is helpful to consider the population, intervention, comparison and outcomes that could be addressed, or addressed more effectively in the future, in the context of the certainty of the evidence in the current review (Brown et al 2006):

• P (Population): diagnosis, disease stage, comorbidity, risk factor, sex, age, ethnic group, specific inclusion or exclusion criteria, clinical setting;
• I (Intervention): type, frequency, dose, duration, prognostic factor;
• C (Comparison): placebo, routine care, alternative treatment/management;
• O (Outcome): which clinical or patient-related outcomes will the researcher need to measure, improve, influence or accomplish? Which methods of measurement should be used?

While Cochrane Review authors will find the PICO domains helpful, the domains of the GRADE certainty framework further support understanding and describing what additional research will improve the certainty in the available evidence. Note that as the certainty of the evidence is likely to vary by outcome, these implications will be specific to certain outcomes in the review. Table 15.6.a shows how review authors may be aided in their interpretation of the body of evidence and drawing conclusions about future research and practice.

Table 15.6.a Implications for research and practice suggested by individual GRADE domains

The review of compression stockings for prevention of deep vein thrombosis (DVT) in airline passengers described in Chapter 14 provides an example where there is some convincing evidence of a benefit of the intervention: “This review shows that the question of the effects on symptomless DVT of wearing versus not wearing compression stockings in the types of people studied in these trials should now be regarded as answered. Further research may be justified to investigate the relative effects of different strengths of stockings or of stockings compared to other preventative strategies. Further randomised trials to address the remaining uncertainty about the effects of wearing versus not wearing compression stockings on outcomes such as death, pulmonary embolism and symptomatic DVT would need to be large.” (Clarke et al 2016).

A review of therapeutic touch for anxiety disorder provides an example of the implications for research when no eligible studies had been found: “This review highlights the need for randomized controlled trials to evaluate the effectiveness of therapeutic touch in reducing anxiety symptoms in people diagnosed with anxiety disorders. Future trials need to be rigorous in design and delivery, with subsequent reporting to include high quality descriptions of all aspects of methodology to enable appraisal and interpretation of results.” (Robinson et al 2007).

15.6.4 Reaching conclusions

A common mistake is to confuse ‘no evidence of an effect’ with ‘evidence of no effect’. When the confidence intervals are too wide (e.g. including no effect), it is wrong to claim that the experimental intervention has ‘no effect’ or is ‘no different’ from the comparator intervention. Review authors may also incorrectly ‘positively’ frame results for some effects but not others. For example, when the effect estimate is positive for a beneficial outcome but confidence intervals are wide, review authors may describe the effect as promising. However, when the effect estimate is negative for an outcome that is considered harmful but the confidence intervals include no effect, review authors report no effect. Another mistake is to frame the conclusion in wishful terms. For example, review authors might write, “there were too few people in the analysis to detect a reduction in mortality” when the included studies showed a reduction or even increase in mortality that was not ‘statistically significant’. One way of avoiding errors such as these is to consider the results blinded; that is, consider how the results would be presented and framed in the conclusions if the direction of the results was reversed. If the confidence interval for the estimate of the difference in the effects of the interventions overlaps with no effect, the analysis is compatible with both a true beneficial effect and a true harmful effect. If one of the possibilities is mentioned in the conclusion, the other possibility should be mentioned as well. Table 15.6.b suggests narrative statements for drawing conclusions based on the effect estimate from the meta-analysis and the certainty of the evidence.

Table 15.6.b Suggested narrative statements for phrasing conclusions

Another common mistake is to reach conclusions that go beyond the evidence. Often this is done implicitly, without referring to the additional information or judgements that are used in reaching conclusions about the implications of a review for practice. Even when additional information and explicit judgements support conclusions about the implications of a review for practice, review authors rarely conduct systematic reviews of the additional information. Furthermore, implications for practice are often dependent on specific circumstances and values that must be taken into consideration. As we have noted, review authors should always be cautious when drawing conclusions about implications for practice and they should not make recommendations.

15.7 Chapter information

Authors: Holger J Schünemann, Gunn E Vist, Julian PT Higgins, Nancy Santesso, Jonathan J Deeks, Paul Glasziou, Elie Akl, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group

Acknowledgements: Andrew Oxman, Jonathan Sterne, Michael Borenstein and Rob Scholten contributed text to earlier versions of this chapter.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health. JJD receives support from the National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. JPTH receives support from the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Graham R, Mancher M, Wolman DM, Greenfield S, Steinberg E. Committee on Standards for Developing Trustworthy Clinical Practice Guidelines, Board on Health Care Services: Clinical Practice Guidelines We Can Trust. Washington, DC: National Academies Press; 2011.

Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y, Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schünemann HJ. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. Journal of Clinical Epidemiology 2011a; 64 : 383-394.

Guyatt GH, Juniper EF, Walter SD, Griffith LE, Goldstein RS. Interpreting treatment effects in randomised trials. BMJ 1998; 316 : 690-693.

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann HJ. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008; 336 : 924-926.

Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, Alonso-Coello P, Falck-Ytter Y, Jaeschke R, Vist G, Akl EA, Post PN, Norris S, Meerpohl J, Shukla VK, Nasser M, Schünemann HJ. GRADE guidelines: 8. Rating the quality of evidence--indirectness. Journal of Clinical Epidemiology 2011b; 64 : 1303-1310.

Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, Brozek J, Norris S, Meerpohl J, Djulbegovic B, Alonso-Coello P, Post PN, Busse JW, Glasziou P, Christensen R, Schünemann HJ. GRADE guidelines: 12. Preparing summary of findings tables-binary outcomes. Journal of Clinical Epidemiology 2013a; 66 : 158-172.

Guyatt GH, Thorlund K, Oxman AD, Walter SD, Patrick D, Furukawa TA, Johnston BC, Karanicolas P, Akl EA, Vist G, Kunz R, Brozek J, Kupper LL, Martin SL, Meerpohl JJ, Alonso-Coello P, Christensen R, Schünemann HJ. GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles-continuous outcomes. Journal of Clinical Epidemiology 2013b; 66 : 173-183.

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Santesso N, Carrasco-Labra A, Langendam M, Brignardello-Petersen R, Mustafa RA, Heus P, Lasserson T, Opiyo N, Kunnamo I, Sinclair D, Garner P, Treweek S, Tovey D, Akl EA, Tugwell P, Brozek JL, Guyatt G, Schünemann HJ. Improving GRADE evidence tables part 3: detailed guidance for explanatory footnotes supports creating and understanding GRADE certainty in the evidence judgments. Journal of Clinical Epidemiology 2016; 74 : 28-39.

Schünemann HJ, Puhan M, Goldstein R, Jaeschke R, Guyatt GH. Measurement properties and interpretability of the Chronic respiratory disease questionnaire (CRQ). COPD: Journal of Chronic Obstructive Pulmonary Disease 2005; 2 : 81-89.

Schünemann HJ, Guyatt GH. Commentary--goodbye M(C)ID! Hello MID, where do you come from? Health Services Research 2005; 40 : 593-597.

Schünemann HJ, Fretheim A, Oxman AD. Improving the use of research evidence in guideline development: 13. Applicability, transferability and adaptation. Health Research Policy and Systems 2006; 4 : 25.

Schünemann HJ. Methodological idiosyncracies, frameworks and challenges of non-pharmaceutical and non-technical treatment interventions. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 2013; 107 : 214-220.

Schünemann HJ, Tugwell P, Reeves BC, Akl EA, Santesso N, Spencer FA, Shea B, Wells G, Helfand M. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods 2013; 4 : 49-62.

Schünemann HJ, Wiercioch W, Etxeandia I, Falavigna M, Santesso N, Mustafa R, Ventresca M, Brignardello-Petersen R, Laisaar KT, Kowalski S, Baldeh T, Zhang Y, Raid U, Neumann I, Norris SL, Thornton J, Harbour R, Treweek S, Guyatt G, Alonso-Coello P, Reinap M, Brozek J, Oxman A, Akl EA. Guidelines 2.0: systematic development of a comprehensive checklist for a successful guideline enterprise. CMAJ: Canadian Medical Association Journal 2014; 186 : E123-142.

Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? Journal of Clinical Epidemiology 2016; 75 : 6-15.

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For permission to re-use material from the Handbook (either academic or commercial), please see here for full details.

How to Write a Research Paper Summary

One of the most important skills you can imbibe as an academician is to know how to summarize a research paper. During your academic journey, you may need to write a summary of findings in research quite often and for varied reasons – be it to write an introduction for a peer-reviewed publication , to submit a critical review, or to simply create a useful database for future referencing.

It can be quite challenging to effectively write a research paper summary for often complex work, which is where a pre-determined workflow can help you optimize the process. Investing time in developing this skill can also help you improve your scientific acumen, increasing your efficiency and productivity at work. This article illustrates some useful advice on how to write a research summary effectively. But, what is research summary in the first place?  

A research paper summary is a crisp, comprehensive overview of a research paper, which encapsulates the purpose, findings, methods, conclusions, and relevance of a study. A well-written research paper summary is an indicator of how well you have understood the author’s work. 

Table of Contents

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• 2. Invest enough time to understand the topic deeply 

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• Mistakes to avoid while writing your research paper summary 

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Frequently asked questions (faq), how to write a research paper summary.

Writing a good research paper summary comes with practice and skill. Here is some useful advice on how to write a research paper summary effectively.  

1. Determine the focus of your summary

Before you begin to write a summary of research papers, determine the aim of your research paper summary. This will give you more clarity on how to summarize a research paper, including what to highlight and where to find the information you need, which accelerates the entire process. If you are aiming for the summary to be a supporting document or a proof of principle for your current research findings, then you can look for elements that are relevant to your work.

On the other hand, if your research summary is intended to be a critical review of the research article, you may need to use a completely different lens while reading the paper and conduct your own research regarding the accuracy of the data presented. Then again, if the research summary is intended to be a source of information for future referencing, you will likely have a different approach. This makes determining the focus of your summary a key step in the process of writing an effective research paper summary. 

2. Invest enough time to understand the topic deeply

In order to author an effective research paper summary, you need to dive into the topic of the research article. Begin by doing a quick scan for relevant information under each section of the paper. The abstract is a great starting point as it helps you to quickly identify the top highlights of the research article, speeding up the process of understanding the key findings in the paper. Be sure to do a careful read of the research paper, preparing notes that describe each section in your own words to put together a summary of research example or a first draft. This will save your time and energy in revisiting the paper to confirm relevant details and ease the entire process of writing a research paper summary.

When reading papers, be sure to acknowledge and ignore any pre-conceived notions that you might have regarding the research topic. This will not only help you understand the topic better but will also help you develop a more balanced perspective, ensuring that your research paper summary is devoid of any personal opinions or biases. 

3. Keep the summary crisp, brief and engaging

A research paper summary is usually intended to highlight and explain the key points of any study, saving the time required to read through the entire article. Thus, your primary goal while compiling the summary should be to keep it as brief, crisp and readable as possible. Usually, a short introduction followed by 1-2 paragraphs is adequate for an effective research article summary. Avoid going into too much technical detail while describing the main results and conclusions of the study. Rather focus on connecting the main findings of the study to the hypothesis , which can make the summary more engaging. For example, instead of simply reporting an original finding – “the graph showed a decrease in the mortality rates…”, you can say, “there was a decline in the number of deaths, as predicted by the authors while beginning the study…” or “there was a decline in the number of deaths, which came as a surprise to the authors as this was completely unexpected…”.

Unless you are writing a critical review of the research article, the language used in your research paper summaries should revolve around reporting the findings, not assessing them. On the other hand, if you intend to submit your summary as a critical review, make sure to provide sufficient external evidence to support your final analysis. Invest sufficient time in editing and proofreading your research paper summary thoroughly to ensure you’ve captured the findings accurately. You can also get an external opinion on the preliminary draft of the research paper summary from colleagues or peers who have not worked on the research topic. 

Mistakes to avoid while writing your research paper summary

Now that you’ve understood how to summarize a research paper, watch out for these red flags while writing your summary. 

• Not paying attention to the word limit and recommended format, especially while submitting a critical review 
• Evaluating the findings instead of maintaining an objective , unbiased view while reading the research paper 
• Skipping the essential editing step , which can help eliminate avoidable errors and ensure that the language does not misrepresent the findings 
• Plagiarism, it is critical to write in your own words or paraphrase appropriately when reporting the findings in your scientific article summary 

We hope the recommendations listed above will help answer the question of how to summarize a research paper and enable you to tackle the process effectively. 

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How to summarize a research paper with Paperpal?

To generate your research paper summary, simply login to the platform and use the Paperpal Copilot Summary feature to create a flawless summary of your work. Here’s a step-by-step process to help you craft a summary in minutes:

• Paste relevant research articles to be summarized into Paperpal; the AI will scan each section and extract key information.
• In minutes, Paperpal will generate a comprehensive summary that showcases the main paper highlights while adhering to academic writing conventions.
• Check the content to polish and refine the language, ensure your own voice, and add citations or references as needed.

The abstract and research paper summary serve similar purposes but differ in scope, length, and placement. The abstract is a concise yet detailed overview of the research, placed at the beginning of a paper, with the aim of providing readers with a quick understanding of the paper’s content and to help them decide whether to read the full article. Usually limited to a few hundred words, it highlights the main objectives, methods, results, and conclusions of the study. On the other hand, a research paper summary provides a crisp account of the entire research paper. Its purpose is to provide a brief recap for readers who may want to quickly grasp the main points of the research without reading the entire paper in detail.

The structure of a research summary can vary depending on the specific requirements or guidelines provided by the target publication or institution. A typical research summary includes the following key sections: introduction (including the research question or objective), methodology (briefly describing the research design and methods), results (summarizing the key findings), discussion (highlighting the implications and significance of the findings), and conclusion (providing a summary of the main points and potential future directions).

The summary of a research paper is important because it provides a condensed overview of the study’s purpose, methods, results, and conclusions. It allows you to quickly grasp the main points and relevance of the research without having to read the entire paper. Research summaries can also be an invaluable way to communicate research findings to a broader audience, such as policymakers or the general public.

  When writing a research paper summary, it is crucial to avoid plagiarism by properly attributing the original authors’ work. To learn how to summarize a research paper while avoiding plagiarism, follow these critical guidelines: (1) Read the paper thoroughly to understand the main points and key findings. (2) Use your own words and sentence structures to restate the information, ensuring that the research paper summary reflects your understanding of the paper. (3) Clearly indicate when you are paraphrasing or quoting directly from the original paper by using appropriate citation styles. (4) Cite the original source for any specific ideas, concepts, or data that you include in your summary. (5) Review your summary to ensure it accurately represents the research paper while giving credit to the original authors.

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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5 reasons for rejection after peer review, ethical research practices for research with human subjects.

• How to Write a Conclusion for Research Papers (with Examples)
• Publish or Perish – Understanding the Importance of Scholarly Publications in Academia

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary : A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasize weaknesses or failures.

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Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that…
• The study demonstrates a correlation between…
• This analysis supports the theory that…
• The data suggest that…

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualizing your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis…
• Contrary to the hypothesized association…
• The results contradict the claims of Smith (2022) that…
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is y .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of…
• The results do not fit with the theory that…
• The experiment provides a new insight into the relationship between…
• These results should be taken into account when considering how to…
• The data contribute a clearer understanding of…
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalizability is limited.
• If you encountered problems when gathering or analyzing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalizability of the results is limited by…
• The reliability of these data is impacted by…
• Due to the lack of data on x , the results cannot confirm…
• The methodological choices were constrained by…
• It is beyond the scope of this study to…

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish…
• Future studies should take into account…
• Avenues for future research include…

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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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McCombes, S. (2023, July 18). How to Write a Discussion Section | Tips & Examples. Scribbr. Retrieved March 16, 2024, from https://www.scribbr.com/dissertation/discussion/

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• Review Article
• Published: 05 March 2024

Projections of an ice-free Arctic Ocean

• Alexandra Jahn   ORCID: orcid.org/0000-0002-6580-2579 1 , 2 ,
• Marika M. Holland   ORCID: orcid.org/0000-0001-5621-8939 3 &
• Jennifer E. Kay   ORCID: orcid.org/0000-0002-3625-5377 1 , 4  

Nature Reviews Earth & Environment volume  5 ,  pages 164–176 ( 2024 ) Cite this article

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• Climate and Earth system modelling
• Cryospheric science
• Projection and prediction

Observed Arctic sea ice losses are a sentinel of anthropogenic climate change. These reductions are projected to continue with ongoing warming, ultimately leading to an ice-free Arctic (sea ice area <1 million km 2 ). In this Review, we synthesize understanding of the timing and regional variability of such an ice-free Arctic. In the September monthly mean, the earliest ice-free conditions (the first single occurrence of an ice-free Arctic) could occur in 2020–2030s under all emission trajectories and are likely to occur by 2050. However, daily September ice-free conditions are expected approximately 4 years earlier on average, with the possibility of preceding monthly metrics by 10 years. Consistently ice-free September conditions (frequent occurrences of an ice-free Arctic) are anticipated by mid-century (by 2035–2067), with emission trajectories determining how often and for how long the Arctic could be ice free. Specifically, there is potential for ice-free conditions in May–January and August–October by 2100 under a high-emission and low-emission scenario, respectively. In all cases, sea ice losses begin in the European Arctic, proceed to the Pacific Arctic and end in the Central Arctic, if becoming ice free at all. Future research must assess the impact of model selection and recalibration on projections, and assess the drivers of internal variability that can cause early ice-free conditions.

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Introduction

Arctic sea ice cover — including  sea ice area (SIA) 1 , sea ice extent (SIE) 2 and sea ice thickness 3 , 4 — has declined conspicuously since the beginning of satellite observations in 1978. Although losses have occurred in all seasons 5 , reductions are greatest during summer, with SIA 6 declining by −0.078 million km 2  year −1 between 1979 and 2023. However, these reductions are not temporally consistent: summertime SIA losses between 1996–2012 are more than twice those over 1979–2023, reaching −0.17 million km 2  year −1 . Spatial variability also contributes to sea ice loss heterogeneity 7 , with the largest reductions seen in the shelf seas of the Arctic Ocean (the Barents, Kara, Laptev, East Siberian and Chukchi Seas).

Given observed and projected warming 8 , these sea ice reductions are set to continue such that the Arctic could become ice free. Indeed, climate models from the late 1970s already predicted the possibility of reaching summer ice-free conditions under sufficient warming 9 , with current climate models suggesting that September is likely to be ice free before mid-century 10 . However, internal variability 11 , 12 , physical differences between the models 13 and evolving definitions of ‘ice free’ 12 complicate accurate predictions, as demonstrated by the timing of ice-free conditions differing by more than 20 years owing to internal variability 12 , by more than 100 years across models 10 , 14 or by decades depending on the definition used 12 .

Regardless of prediction uncertainties, the transition to an ice-free Arctic signifies a regime shift from a perennial sea ice cover to a seasonal sea ice cover, or from a white summer Arctic to a blue Arctic 15 (Fig.  1 ). Such changes have probably not occurred for at least 80,000 years 16 (Box  1 ) and will have important impacts on the local and global climate and on ecological systems. For instance, replacing sea ice cover with open water modifies the radiation balance via reductions in  albedo 17 , in turn, accelerating and amplifying anthropogenic warming 18 , especially in the Arctic 19 , 20 , 21 , 22 . Moreover, open-water areas and ice-free conditions allow for a larger fetch 23 , increasing wave heights 24 , 25 and, thereby, coastal erosion around the Arctic Ocean 26 , 27 , 28 . From an ecosystem perspective, the transition towards a summer ice-free Arctic threatens the survival of sea ice-dependent mammals such as polar bears and seals 29 , 30 , 31 , leads to increasing ocean productivity 32 , and allows for the potential migration of some fish species from the sub-polar seas into the Arctic Ocean 33 , 34 . Economic activity in the Arctic could also increase owing to enhanced accessibility for shipping 35 and resource exploration 36 . Due to the multitude of impacts on an ice-free Arctic, it is important to understand the timing of when the Arctic could become ice free.

a , Pan-Arctic September sea ice concentration with a sea ice area (SIA) of 5.5 million km 2 , typical for the 1980s. b , The same as in part a , but for 3.3 million km 2 , typical for 2015–2023. c , The same as in part a , but for sea ice area of <1 million km 2 , referred to as an ice-free Arctic. d , The climatological sea ice area seasonal cycles for 1980–1999 from satellite-derived observations 121 using the bootstrap 122 (solid red line) and NASA team 123 (dashed red line) algorithms, for 1980–1999 from select CMIP6 models 10 (black), and for a predicted ice-free September in select CMIP6 ensemble mean (blue). Red shading indicates uncertainty in the observed sea ice area (bounded by the seasonal cycle from the two algorithms), grey shading the CMIP6 ensemble spread for 1980–1999, and light blue shading the CMIP6 ensemble spread during the decade when the ensemble mean first goes ice free. Although sea ice area is reduced in all months of the year in the future, the loss is predicted to be greatest in September, but winter sea ice returns even after ice-free conditions are reached.

In this Review, we summarize the current understanding of an ice-free Arctic. We begin by discussing the drivers of sea ice loss, followed by available methodological approaches and corresponding uncertainties. Next, we outline predictions of an ice-free Arctic, including for September, months beside September and regional variability. We end with an outlook of future research needs. To quantify ice-free projections, we analyse monthly sea ice from select 10 Climate Model Intercomparison Project 6 (CMIP6) 37 models, hereafter referred to as ‘select models’, chosen on the basis of observations falling within the ensemble spread of each model for two key metrics 10 : the 2005–2014 September mean sea ice area and the observed sensitivity of sea ice area to cumulative CO 2 emissions over 1979–2014 (Supplementary Table 1 ). These select models are supplemented by large ensemble simulations from CMIP5 (ref. 38 ) and CMIP6.

Box 1 The history of ice-free conditions in the Arctic

Although sea ice has been a defining feature of the Arctic Ocean since the Eocene (47 million years ago) 16 , with perennial sea ice first appearing during the Miocene (around 13–14 million years ago) 16 , 124 , 125 , 126 , ice-free conditions are not a first for the Arctic when assessed over the geological record. For example, before the Arctic became ice-covered, early ancestors of tropical plants and crocodiles thrived in the Arctic during the Cretaceous (over 70 million years ago) 127 , 128 , 129 . Moreover, proxy evidence suggest a return to ice-free summers in the Central Arctic Ocean during the late Miocene (approximately 5 million years ago) 130 .

There is also evidence for ice-free conditions in the more recent geological past. For example, the last ice-free conditions in the Arctic likely occurred during the Eemian — the warmest period of the warmest quaternary interglacials — including marine isotope stage 5e (MIS 5e) (between 130,000 and 115,000 years ago) and potentially MIS 5a (around 80,000 years ago). At these times, proxy records indicate open water north of Greenland 131 , 132 , 133 , 134 , 135 and a northward shift of the tree line by hundreds of kilometres in Alaska and Russia 126 , 136 ; note that paleo evidence for these changes is stronger for MIS 5e than MIS 5a.

By contrast, during the Holocene (the current interglacial that started 11,000 years ago), the Arctic Ocean likely retained its perennial sea ice cover 137 , 138 . However, there is evidence for regionally ice-free conditions in the Arctic during the mid-Holocene warm period that peaked around 6,000 years before present, particularly in the shelf seas of the eastern Arctic 16 , 138 , 139 . Thus, perennial sea ice was probably much reduced in the summer during the mid-Holocene and restricted to north of Greenland 138 where the oldest and thickest ice is found today 140 , 141 .

Thus, when pan-Arctic ice-free conditions occur again in the next few decades, it will probably be a first for at least 80,000 years 132 , 133 , if not for over 115,000 years 135 . The occurrence of pan-Arctic winter ice-free conditions, predicted to occur in the twenty-third century under extreme warming 115 , would be a first for 47 million years, since the Arctic became sea ice covered in the Eocene 16 .

Drivers of Arctic sea ice loss

Arctic sea ice changes are linked to a multitude of interconnected processes and feedbacks (Fig.  2 ). Atmospheric and oceanic heat transport into the Arctic are two processes that vary because of internal climate variability and externally forced changes 39 , 40 . Within the Arctic, various feedbacks are also at play 41 . In the case of forced anthropogenic changes, the majority of these local feedbacks are  positive feedbacks , amplifying Arctic sea ice loss and warming 42 , but their magnitude is uncertain and varies across models 43 , 44 . Dominant examples include the albedo feedback and lapse rate feedback.  Negative feedbacks , such as the influence of ice thickness on ice growth rates 45 , can somewhat mitigate ice loss, but not enough to counteract declining trends. The strength of these feedbacks can be climate state dependent 46 , 47 , which means their relative strength will vary as sea ice changes.

The highly coupled processes and feedbacks that affect Arctic sea ice in response to anthropogenic warming.

The observed September sea ice loss is attributable to forced change from anthropogenic emissions 48 , 49 , reinforced by internal variability 50 . Historical model simulations that apply subsets of external forcings (only natural forcings, only anthropogenic aerosol forcings, only greenhouse gas forcings) have enabled the attribution of forced changes in the climate, demonstrating that greenhouse gas emissions drove considerable ice loss, modestly offset by the cooling effects of anthropogenic aerosol emissions 51 . Thus, the magnitude of observed sea ice loss would not have been possible without anthropogenic greenhouse gas emissions 48 (Supplementary Fig. 1 ). Although CO 2 emissions were the most impactful drivers, the radiative effects of chlorofluorocarbons account for about 48% of forced September sea ice loss from 1979–2005 (ref. 52 ). Hence, the Montreal Protocol delayed the occurrence of the first ice-free Arctic by about 10 years (ref. 53 ). Although observed sea ice loss has a roughly linear relationship with global mean surface temperature 54 , 55 and with the cumulative carbon dioxide emissions 1 , these relationships might not hold for the future given changes in the mix of external forcings that contribute to forced changes in regional Arctic warming and Arctic sea ice loss.

Internal variability has enhanced this observed sea ice loss 50 . Specifically, internal variability in atmospheric circulation is estimated to have reinforced the observed September ice loss by up to 50% (refs.  50 , 56 , 57 ). Atmospheric variability, thereby, overall accounts for about 75% of Arctic sea ice internal variability 58 . Ocean heat fluxes into the Arctic, however, are also important and might have helped stabilize September SIA between 2007 and 2023 (ref. 59 ).

Internal variability combined with forced sea ice loss and local positive feedback can lead to large multi-year changes in the Arctic sea ice, referred to as rapid ice loss events (RILEs) 60 . As Arctic sea ice becomes thinner, large areas of the ice pack are susceptible to melt out, resulting in increased summer ice area variability 61 , 62 and a higher likelihood of RILEs. These RILEs are driven by ocean heat transport variations 60 , 63 , atmospheric circulation anomalies 64 or a combination of the two 65 . The surface albedo feedback and fall cloud feedbacks reinforce these events 66 . Notably, periods of limited ice loss or even increasing sea ice are also possible when internal variability counteracts anthropogenically forced change 50 . The evolution of these high-ice-loss and low-ice-loss events affects the trajectory by which Arctic summer ice-free conditions will be reached. The potential occurrence of high ice-loss events allows for the possibility of reaching ice-free conditions within a few years when starting from the average sea ice cover in the early 2020s.

Methods for predicting an ice-free Arctic

Predictions of an ice-free Arctic use different definitions or methods, each with their own inherent uncertainties. These approaches are now discussed.

Contrasting definitions

The definition of an ‘ice-free Arctic’ has varied over time. Early on, it referred to the nearly complete disappearance of all sea ice, or zero SIE (refs. 9 , 60 , 67 ). However, as thick sea ice remains north of Greenland and the Canadian Arctic Archipelago more than a decade after the rest of the Arctic Ocean becomes ice free in September 60 , 68 , a SIE threshold of 1 million km 2 became commonplace 48 .

This 1 million km 2 threshold, however, can also introduce differences depending on the sea ice metrics it is applied to. For instance, an ice-free Arctic occurs earlier when the threshold is used with SIA rather than SIE (ref. 69 ) (Fig.  3a ). Specifically, for the select CMIP6 models 10 , ice-free conditions occur 0–47 years earlier (mean, mode and standard deviation of 8, 3 and 10 years, respectively) when using SIA instead of SIE. Moreover, differences occur when SIA calculations use a minimum threshold of 15% sea ice concentration 70 , 71 , producing even earlier ice-free dates compared with using the standard SIA.

a , Year of the earliest ice-free September for ‘selected CMIP6 models’ 10 (Supplementary Table 1 ) based on different emission scenarios and definitions of ice free: ‘Earliest ice-free conditions’ use unsmoothed monthly sea ice area or sea ice extent (monthly SIA and SIE, respectively); ‘Consistently ice-free conditions’ use 5-year or 20-year smoothed sea ice area, or the first year after which the Arctic is ice free for 5 years for unsmoothed sea ice area (5-year mean, 20-year mean and 5 years in a row, respectively). Numbers on the right y -axis indicate the number of models that do not go ice free by 2100 for a given model, definition or scenario. b , The fraction of CMIP6 models that have reached ice-free conditions at least once in the monthly mean September sea ice area by a given year under a given forcing scenario — the cumulative probability of first ice-free conditions — and their likelihood according to the Intergovernmental Panel on Climate Change (IPCC) definitions. c , The same as in part b , but for the selected CMIP6 models in part a . d , The same as in part c , but the fraction of selected CMIP6 models that are ice free for a given temperature anomaly (using a 5-year smoothed mean to reflect the level of forced warming rather than individual year temperatures), with the anomaly calculated relative to each of the models 1850–1899 global temperature. e , The fraction of selected CMIP6 models that are ice free in a given year, smoothed by a 20-year running mean. Although definition differences and model selection influence the specifics of ice-free predictions, they all indicate that ice-free conditions tend to occur at least once by 2050 under all assessed scenarios, and become a frequent occurrence thereafter under all scenarios except SSP1-1.9.

The temporal aspect of the underlying sea ice data also impacts the definition of ice free. Collectively, two clear definition categories emerge: predictions of the earliest ice-free conditions and predictions of consistently ice-free conditions (Fig.  3a ), emphasizing internal variability and forced responses, respectively. Earliest ice-free conditions are obtained using unsmoothed monthly sea ice time series. This category focuses on the earliest possible occurrence of ice-free conditions, which could be a single event caused by internal variability once the mean sea ice state is low enough. By contrast, consistently ice-free conditions use smoothed data and, thus, focus on the likely occurrence of ice-free conditions based on the forced response. This category is heterogeneous, with examples using 5-year running means 54 , 67 , 72 , 73 , using ensemble means 74 , using five consecutive ice-free years 12 , 14 , 75 , 76 , or likely ice-free conditions based on cumulative probabilities 77 , 78 . With all these methods, the predicted occurrence of first ice-free conditions is delayed compared with the earliest ice-free conditions (Fig.  3a ). This diversity of definitions causes challenges in comparing existing ice-free predictions (Table  1 ), as definition differences affect the timing of ice-free conditions, ranging from a few years to well over a decade (Fig.  3a ).

Cumulative probabilities provide a useful way to provide insight into both first ice-free and consistently ice-free conditions in a comprehensive manner. Indeed, when predictions of an ice-free Arctic are given in terms of cumulative probabilities, both the occurrence of the first possible ice-free Arctic (any percentage above zero) and consistently ice-free conditions can be inferred 76 , 77 , 79 , 80 . For the latter, different thresholds can be used to define consistently ice free, for example, >66% corresponding to the start of the ‘likely’ cumulative probability (Fig.  3 , part a versus part c ).

For regional ice-free conditions, it is not the 1 million km 2 threshold that is used to determine ice-free conditions, but instead a regional average sea ice concentration threshold. However, again there are differences in the threshold chosen. Specifically, a region has been considered ice free when the area-averaged sea ice concentration in the region falls below 15% (ref. 81 ) or reaches 6% (ref. 75 ).

Different prediction methods

In addition to definition choices, predictions of an ice-free Arctic can also be made using different methodological approaches, namely, using climate models or statistical models. Most Arctic ice-free predictions are made using projections from climate models 9 , 10 , 48 , 67 , 76 , 80 , 82 , 83 . Climate models explicitly simulate the evolution of sea ice, including dynamical and thermodynamical processes, albeit in an incomplete way owing to limited scientific understanding and/or computational constraints. Their model output can provide predictions of both early and consistently ice-free conditions depending on how the model output is analysed.

Statistical methods have also been used to provide predictions of an ice-free Arctic. Most of these predictions are based on observed linear relationships between global or Arctic temperature and sea ice cover 54 , 77 , 82 , 84 , 85 , or CO 2 and sea ice cover 1 , 5 . Another approach is to use non-linear statistical relationships to make ice-free predictions 86 . Although useful, these statistical models possess several limitations. For example, the models typically assume that observed relationships will continue into the future, an assumption that might not be correct. Furthermore, they typically rely on linear relationships that represent the response of sea ice to forcing and, thereby, usually only provide predictions of consistently ice-free conditions and not early ice-free conditions. Inclusion of a statistical representation of internal variability can overcome this latter limitation 77 , 84 , 85 . In these cases, internal variability is usually based on standard deviations from observations or models, which means representation of rare sea ice loss events is dependent on how internal variability is estimated; using ±3  σ (ref. 77 ) accounts for 99.7% of internal variability and, hence, excludes only truly rare events (0.3%), whereas using ±1  σ (ref. 85 ) or ±2  σ (ref. 84 ) excludes 32% or 5% internal variability, respectively, delaying the predicted occurrence of the earliest ice-free conditions.

Inherent uncertainties of predictions

Predictions based on climate models and statistical models each have uncertainties that are important to recognize. For climate model predictions, internal variability uncertainty, scenario uncertainty and model uncertainty are key considerations 87 , whereas for statistical models, internal variability uncertainty (or neglecting internal variability uncertainty), scenario uncertainty, observational uncertainty and observed relationship uncertainty are important 85 .

Internal variability prediction uncertainty is caused by the chaotic nature of the climate system 88 . The magnitude of internal variability uncertainty is around 20 years for predictions of a first ice-free Arctic 11 , 12 (but can be even larger for some models 89 ) and slightly reduced by 8 years on average (Supplementary Fig. 2 ) for consistently ice-free conditions, as some internal variability is averaged out. This internal variability uncertainty cannot be eliminated, even with improvements in models and/or methodology, but could potentially be reduced by better understanding the underlying drivers of internal variability and refining predictions based on the potential predictability of those drivers 90 . Initial-value predictability (the predictability that arises from knowledge of an initial state) of sea ice might also allow for more precise predictions as the time of an ice-free Arctic comes closer, but this predictability is limited to seasonal–interannual timescales 91 .

Scenario uncertainty is related to the evolution of future net emissions of greenhouse gases from all sectors, including land use. Given that these scenarios depend on future societal and policy decisions, it is an uncertainty that is not reducible. However, predictions based on degrees of anthropogenic warming 10 , 54 , 55 , 80 , 84 (Fig.  3d ) or cumulative CO 2 emissions 1 , 10 , rather than time, remove dependency on the specific emission scenario used.

Model uncertainty arises from structural differences in climate models — the choices made when building individual climate model components. These model (or structural) uncertainties are the largest source of uncertainty when predicting an ice-free Arctic 10 , 14 , 92 . Indeed, the ice-free prediction range due to model uncertainty in non-refined projections is over 100 years 10 , 14 (Fig.  3b ). These model uncertainties are those that have the largest potential for reductions through model improvements. Yet, large multi-model spread has persisted for nearly two decades 10 despite improvements in sea ice model physics, highlighting that such improvements do not always yield immediate improvements in predictions.

Observational uncertainties in large-scale sea ice products refer to those associated with remote-sensing techniques. Depending on the methodology used, these uncertainties are linked to atmospheric interference, algorithmic uncertainties and the spatial resolution of sensors 93 . Comparing different products allows the magnitude of observational uncertainty to be estimated 10 , 85 , 93 , which for September SIA is about 0.9 million km 2 over 1980–1999 (Fig.  1d ).

Finally, uncertainties in observed relationships occur because of short time series 94 and/or uncertainty in whether historical relationships will continue in the future. For instance, extrapolating a short 12-year (1996–2007) observed linear relationship into the future led to prediction of the earliest possible ice-free Arctic in 2016 ± 3 years 95 . This prediction was not realized because the observed rate of sea ice decline is not constant in time, illustrating why linear extrapolation, especially of short time series, is not a reliable prediction method.

Refining model spread

Given the large uncertainties from structural model differences, there have been substantial efforts to reduce multi-model spread in ice-free projections. These approaches include using model selection 10 , 14 , 48 , 71 , 96 , 97 , 98 , model weighting 13 , 74 , 92 , emergent constraints 70 , 73 , and model recalibration or constrained estimation 55 , 78 , 99 , although no consensus on the best approach exists yet 14 , 55 .

Model selection describes the use of a subset of the best models, whereas model weighting includes all models but weights the best models more heavily. Various metrics have been used for model selection or weighting, largely the mean, seasonal cycle and trends of SIA or SIE, the rates of warming or cumulative CO 2 emissions 10 , 14 , 48 , 92 , and sea ice-based emergent constraints 70 , 73 . However, other metrics also show promise. For instance, model selection based on the relationship between summer SIA and April sea ice thickness narrowed CMIP6 projection uncertainty more than any previously used sea ice metrics 71 . Similarly, northward ocean heat flux as a selection parameter moved predictions 10 years earlier compared with sea ice-based parameters alone 98 . The importance of other oceanic variables in model weighting and selection also needs to be assessed, particularly Arctic Ocean stratification 100 , 101 and the properties of underlying warm Atlantic water 100 , 102 which have known biases in CMIP6 models.

Constrained model projections, also referred to as model recalibration, refer to the adjustment of climate model projections using observations. Thus, rather than selecting some models and using those as they are, model recalibrations modify the model-produced projections. Different recalibration methods influence the projected timing of ice-free conditions, as demonstrated by earlier ice-free dates when scaling the simulated SIA responses to greenhouse gas forcing 78 , whereas a recalibration of the SIE sensitivity to atmospheric circulation leads to later ice-free dates than unconstrained projections 99 (Table  1 ).

Owing to differences in the underlying data and the definition of used ice-free condition, it is not currently possible to directly compare the effect of different model selection or refinement methods on ice-free projections. Thus, there is a need for dedicated intercomparisons to assess such effects. These efforts would allow the creation of a common set of metrics to use to select and/or refine sea ice projections, as well as establish a common ice-free definition to use going forward. As part of that process, it is crucial to not confuse precision with accuracy, as more precise projections are not by default better and can indeed be worse if, for example, the influence of internal variability is neglected.

Predictions of an ice-free Arctic

Considering definition differences and corresponding uncertainties, pan-Arctic ice-free predictions for September, ice-free conditions for months outside of September, and regional ice-free conditions are now discussed.

Pan-Arctic predictions for September

Most predictions for an ice-free Arctic focus on September, the month of lowest seasonal SIA and, thus, the first to reach ice-free conditions. These predictions indicate that the earliest ice-free conditions could potentially occur in the 2020s to 2030s and are likely going to occur by 2050 (ref. 10 ) (Table  1 ; Fig.  3c ). However, there is large variability in these predictions, ranging from the 2010s to >2100 (refs. 10 , 14 ). Refined projections — through model selection, weighting and constraining — reduce this uncertainty to 2015–2050 (refs.  10 , 74 , 78 , 96 ). In terms of temperature, the earliest ice-free conditions could occur for warming >1.3 °C, are likely to occur for warming of 1.8 °C (Table  1 ; Fig. 3d ), and exhibit a range of 0.9–3.2 °C (ref. 10 ) that can be refined to 1.3–2.9 °C (refs. 10 , 74 , 78 , 96 ).

For these earliest September ice-free conditions, there is no influence of emission scenario 10 , 69 , 74 . Indeed, all scenarios exhibit the possibility of earliest ice-free conditions from the 2010s or from a warming of 1.3 °C (Fig.  3c,d ). This consistency arises owing to the short lead time and resulting small difference between emission trajectories 103 , 104 . Accordingly, the occurrence of the earliest ice-free conditions will be determined by internal climate variability 12 once the mean sea ice state is low enough. For example, conditions similar to those that caused the record 2007 (ref. 105 ) and 2012 (ref. 106 ) September minimums could lead to the drop of sea ice below the 1 million km 2 threshold once mean SIA is ≤2 million km 2 . Early ice-free conditions could also be the result of a multi-year RILE (refs. 60 , 63 ) that could lead to ice-free conditions from an even higher mean sea ice state. However, internal variability (and resulting large single-year or multi-year events) can either enhance or oppose the forced response 50 and, hence, could delay the occurrence of ice-free conditions past the predicted earliest ice-free conditions 12 .

Despite no impact of emission scenarios on the timing of an earliest ice-free Arctic, there remains a small chance that ice-free conditions can be avoided. In particular, if warming is limited to <1.5 °C or only exceeds 1.5 °C for a short time, there is a <10% chance that the Arctic does not become ice free 76 , 79 , 80 , 85 , 107 , 108 , as in Shared Socioeconomic Pathway (SSP) 1-1.9 (Fig.  3d ).

Warming levels also affect the frequency of ice-free conditions re-occurring after a first ice-free September 76 , 80 (Fig.  3e ). For instance, if ice-free conditions occur for warming of ≤1.5 °C, they would likely not re-occur for several decades 76 , 107 . Yet, for warming >2 °C and >3 °C, September ice-free conditions would likely re-occur every 2–3 years 76 , 80 , 107 or almost every year 76 , 80 , respectively; in the latter case, these changes are comparable to what is seen for the select CMIP6 models under SSP2-4.5 and SSP5-8.5 (Fig.  3e ). Notably, if temperatures decrease again, probabilities of ice-free conditions in a given year will also decrease, as evident for SSP1-1.9 (Fig.  3e ). Hence, no irreversible sea ice  tipping point exists for summer Arctic sea ice 109 , 110 , 111 .

Consistently ice-free conditions are expected by mid-century, potentially under all warming scenarios 78 . Predictions for consistently ice-free conditions range from 2023 to 2085, with refined projections of 2035–2067 (Table  1) ; the reduced uncertainty compared with the earliest ice-free conditions is linked to the averaging out of some internal variability (Supplementary Fig. 2 ). In terms of warming, these conditions begin to occur at a global temperature increase of ≥1.8 °C (Table  1) . Although consistently ice-free conditions potentially occur under all scenarios 78 (Fig.  3c ), the strength of the forcing has some effect on the timing of consistently ice-free conditions 78 and, hence, also the interval between the earliest and consistently ice-free conditions (Fig.  3a ). For example, although the difference between predictions of the earliest ice-free conditions and consistently ice-free conditions is around 10 years for SSP5-8.5, it is at least 15 years for SSP1-2.6 (Fig.  3a ).

All previous predictions of ice-free conditions used monthly means as their underlying base data. Yet, the first time SIA dips below the 1 million km 2 threshold will be detected in daily satellite observations. SIA-based calculations from the CESM2-LE (ref. 112 ) suggest that the first occurrence of daily ice-free conditions happens, on average, 4 years prior to the ice-free September monthly mean (Supplementary Fig. 3 ), with a range of 0–18 years. Of the CESM2-LE members, 56% exhibit daily ice-free conditions earlier than monthly ice-free conditions (Supplementary Fig. 3b ), whereas 44% of the CESM2-LE members experience daily and monthly ice-free conditions for the first time during the same year. Differences of 10 years or more, thereby, occur in 20% of the CESM2-LE members, with the largest differences occurring for ensemble members that have relatively late monthly mean ice-free conditions (Supplementary Fig. 3b ). The earliest ice-free conditions in daily observations could, thus, occur even earlier than predicted based on monthly analysis of CMIP6 models and, hence, potentially in the 2020s (Fig.  3c ).

Seasonality of reaching ice-free conditions

Although ice-free conditions are first expected in September, they could extend into other months 5 , 76 , 78 , 84 , 85 . The duration of this ice-free period has direct bearing on the resulting impacts: ice-free conditions that begin earlier in the summer strengthen the ice albedo feedback 47 , increase early open-water areas and, thereby, ocean heat uptake, subsequently delaying fall freeze-up 113 and extending the ice-free season into late fall 5 , 76 , 84 .

Generally, ice-free duration beyond September exhibits pronounced sensitivity to the warming level and, thus, emission scenario. For example, there is a possibility of occasional ice-free conditions in August and October with <2 °C warming (or SSP1-1.9) (refs. 76 , 85 ), extending into July with ≥2.5 °C warming 85 (or SSP1-2.6) and into November with ≥3.5 °C warming 76 (or SSP2-4.5) (Fig.  4 ). In some select CMIP6 models under SSP5-8.5, first ice-free conditions also occur in December, January, May and June during the second half of the twenty-first century (Fig.  4d ) when warming exceeds 3.5 °C (ref. 114 ).

a , The probability of ice-free conditions in a given year and month without any smoothing for selected CMIP6 models 10 forced with SSP1-1.9. The probability is given using the IPCC terms and percentage values. The earliest ice-free conditions can be inferred when any probability of ice-free conditions exists, whereas consistently ice-free conditions start to exist when the probability in a given year reaches the likely category. b , The same as in part a , but for SSP1-2.6. c , The same as in part a , but for SSP2-4.5. d , The same as in part a , but for SSP5-8.5. There are large differences in how likely an ice-free Arctic is to occur in the months of a given year depending on the forcing scenario, with the possibility of ice-free conditions limited to 3 months under SSP1-2.6 and SSP1-1.9, 5 months under SSP2-4.5 and 9 months under SSP5-8.5.

Intuitively, consistently ice-free conditions exhibit the same temperature sensitivity as first ice-free conditions but with delayed emergence. Consistently ice-free conditions likely emerge in August with ≥2.5 °C warming, October with ≥3.5 °C warming and November with ≥4 °C warming 76 , 85 . These sensitivities translate to differences across scenarios. For the selected CMIP6 models forced with SSP2-4.5, the ice-free season is expected to span 3 months per year by 2100 (as determined by continuous likely (>66%) ice-free conditions): ice-free conditions emerge in August by approximately 2055 and in October by approximately 2080 (Fig.  4c ). In contrast, the likely ice-free season is expected to span 6 months for SSP5-8.5: beyond September, continuous ice-free conditions emerge in August by approximately 2050, in October by approximately 2055, in November by approximately 2070, in July by approximately 2075 and in December by approximately 2090 (ice-free conditions in July to October become very likely or virtually certain by 2100) (Fig.  4d ). Consistently ice-free conditions are not expected beyond September for SSP1-1.9 (Fig.  4a ) or SSP1-2.6 (Fig.  4b ). In terms of CO 2 emissions, consistently ice-free conditions are predicted to begin to occur in July to October for an additional 1,400 Gt CO 2 relative to 2016 levels, and in November for around 3,000 Gt CO 2 (refs. 5 , 84 ).

With further warming, the Arctic could become ice free year-round. However, consistently ice-free conditions year-round are not anticipated until atmospheric CO 2 levels reach approximately 1,900 ppm (ref. 115 ), which are not expected until the twenty-third century under the strongest emission scenarios.

Regional variations of ice-free Arctic conditions

In addition to the seasonal sensitivity of ice-free projections, regional variability in ice-free timings are also expected. However, there are limited explicit predictions of these regional ice-free conditions, and those that do exist focus on consistently ice-free metrics 75 , 81 . In addition, regional assessments possess uncertainties greater than the pan-Arctic given larger internal variability (as averaging over smaller regions) and a reduced chance for compensating biases 75 , 81 . Accordingly, any projected dates of regional ice-free conditions are quite dependent on the underlying models and on whether model selection was performed, as well as on the exact definition of what constitutes ice-free conditions 75 , 81 . Thus, differences are apparent between CMIP5 and CMIP6, with earlier regional ice-free dates in CMIP6, potentially at least partially because of requiring only >85% open water 81 versus 94% open water 75 to consider a region ice free.

Despite uncertainty in the timing, CMIP5 and CMIP6 models generally exhibit the same progression of consistently ice-free conditions around the Arctic 75 , 81 . Across all scenarios, September ice-free conditions start in the European Arctic shelf seas, with the Barents Sea and Kara Sea followed by the Laptev Sea, proceed to the Chukchi Sea, East Siberian Sea and Beaufort Sea (the Pacific Arctic), and end in the central Arctic 75 , 81 . Specifically, September regional ice-free conditions in the Barents Sea and Kara Sea are simulated to exist from August to October prior to 2015, with the Laptev Sea, East Siberian Sea and Chukchi Sea following in the 2020s and 2030s, followed by the Beaufort Sea in the 2030s and 2040s, under both SSP1-2.6 and SSP5-8.5 (ref. 81 ). The central Arctic could become ice free in September between 2040 and 2060 under SSP5-8.5 (ref. 81 ) and in the 2060s to 2100 in SSP2-4.5, while avoiding consistently regional ice-free conditions in the central Arctic under SSP1-2.6 and SSP1-1.9 (Fig.  5 ).

a , Year sea ice is consistently ice free for July to November for SSP1-1.9, calculated as the first time sea ice concentration (SIC) in each grid is below 15% in a given month in the ensemble mean 81 of the selected CMIP6 models 10 . Bright white areas indicate regions that retain ice cover with more than 15% SIC in 2100, whereas dark blue areas indicate regions that became ice free before 2020 or that never had ice cover. b , The same as in part a , but for SSP1-2.6. c , The same as in part a , but for SSP2-4.5. d , The same as in part a , but for SSP5-8.5. Forcing scenarios have a big impact on the regional sea ice loss, with no ice-covered regions expected to remain between July and November by 2090 under SSP5-8.5, but some ice-covered regions remaining for every month under SSP1-1.9.

With only a small scenario impact on the timing of consistently ice-free conditions in the shelf seas, the main impacts of scenario differences are the duration of ice-free conditions in the shelf seas and whether and for how long the Central Arctic becomes ice free (Fig.  5) . Specifically, the ice-free season is limited to 3 months a year by 2100 under SSP1-2.6 in the Laptev, East Siberian, Chukchi and Beaufort Sea, but lasts 7–8 months under SSP5-8.5 (ref. 81 ). In the Kara Sea, the difference between these two scenarios is 5 versus 9 months, whereas in the Barents Sea, it is 9 months versus ice free year-round 81 . Regional ice-free conditions in the Central Arctic only occur for SSP5-8.5 and SSP2-4.5, with the consistently ice-free season limited to August and September in SSP2-4.5, but extending for over 5 months under SSP5-8.5 (Fig.  5 ).

Summary and future perspectives

Arctic sea ice has declined substantially since the beginning of the satellite observations in 1978, and is projected to continue to do so into the future. Indeed, earliest ice-free conditions, defined as a single occurrence of ice-free conditions in the monthly mean data, might occur in the 2020s or 2030s for September, and are likely to occur by mid-century 10 independent of emission scenario 10 , 76 , 78 , 80 . Consistently ice-free conditions, which refers to the transition to a frequently ice-free Arctic, are expected to occur between 2035–2067 under the high-emission scenarios, with a small delay possible for lower-emission scenarios. At the regional scale, these losses will begin in the shelf seas of the European Arctic, proceeding into the Pacific Arctic, and, under SSP2-4.5 and SSP5-8.5, end in the central Arctic. Ice loss is also expected beyond the months of September, particularly the shoulder months of August and October, but with marked temperature sensitivity. Thus, greenhouse gas mitigation strongly affects ice-free conditions, determining how often, for how long and where the Arctic will lose its sea ice cover. In particular, under the low-warming scenarios (SSP1-2.6), with warming remaining well below 2 °C, ice-free conditions could remain an exception rather than the new normal 76 . Furthermore, sea ice recovers quickly when temperatures drop 109 , 110 , 111 , so if the world reaches sufficient negative emissions to lead to a global warming of less than 1.5 °C, ice-free conditions could disappear again in the future.

As the earliest possible date of an ice-free Arctic approaches, clear communication is key. Predictions must differentiate between those of consistently ice-free conditions (or likely (>66%) ice-free conditions) because of the forced response, and predictions of the earliest possible ice-free conditions that could occur over a decade earlier because of internal variability 12 . Cumulative probabilities or the probability of ice-free conditions in a given year both provide opportunities to present both types of predictions (Fig.  3b–d ), with the additional benefit of highlighting that ice-free predictions are always probabilistic. In addition, any communications must make it clear what thresholds and approaches are used, as demonstrated by SIA-based assessments leading to earlier ice-free conditions in comparison to SIE (ref. 69 ) (Fig.  3a ). It also needs to be clearly communicated that currently published ice-free predictions focus on monthly averaged values, yet ice-free conditions will probably occur earlier when daily values are considered (in one model, 4 years earlier on average). Further projections using daily data are needed to assess whether such long daily–monthly offsets apply to other models.

Another important issue to consider is when the Arctic sea ice community will consider that an ice-free Arctic has been reached. Deciding on these criteria ahead of reaching ice free conditions is prudent given the various definitions as well as observational uncertainty in satellite-derived sea ice products (Fig.  1d ). As such, it is possible that the 1 million km 2 ice-free threshold will be crossed in some SIA or SIE products under some definitions but not in others. Clarity on how this issue will be handled will facilitate communication around the occurrence of the first ice-free Arctic when it occurs.

Most predictions have focused on pan-Arctic ice-free conditions, yet this transition will occur regionally. Regional ice-free predictions, however, have been rare 75 , 81 . Efforts are needed to develop methods that better constrain regional sea ice projections and reduce their uncertainties. For example, how well existing model selection, weighting and recalibration perform for sea ice projections in different regions of the Arctic should be assessed. New methods might need to be developed to better constrain regional sea ice projections from climate models, always accounting for the irreducible internal variability uncertainty.

Given that climate model and statistical ice-free predictions are always probabilistic, it is also important to assess whether seasonal sea ice predictions have the skill needed to predict the first ice-free conditions at shorter lead times. Given that seasonal sea ice predictions often perform least well when the decline in a given year is far from that expected from the long-term trend, predictions of earliest ice-free conditions are potentially going to be challenging for current seasonal prediction systems 116 . Seasonal prediction experiments initialized with climate model conditions several months prior to a simulated early ice-free state could provide useful insights. Of course, these prediction assessments have their limitations, particularly associated with resolution and absent processes in large-scale climate models, but they might, nonetheless, provide useful insights into the skills of seasonal ice-free predictions.

To better constrain predictions of an ice-free Arctic, and of Arctic sea ice loss in general, dedicated intercomparisons of different model selection, weighting and recalibration methods are required. Currently, too many parameters (models, ensemble members, emission scenarios and ice-free definitions) differ to be able to identify the impact of an individual approach. Furthermore, defining best practice for skilfully reducing sea ice projection uncertainty would be very valuable, including deciding on the best set of metrics to base such methods on to improve projection accuracy and reduce projection uncertainty. Considering sea ice thickness 71 and ocean heat fluxes 98 as selection criteria should be part of that discussion. Additionally, biases in models should be used as an opportunity to better understand the real world 117 . For example, by analysing what drives features not seen in models but present in observations, progress can be made on improving models.

Finally, there is an urgent need to gain a better understanding of the impacts of an ice-free Arctic and the processes that could lead to an early ice-free Arctic, especially drivers of internal variability that contribute to ensemble spread. Such research could provide answers as to what is or what is not predictable, regionally and in the pan-Arctic mean. In terms of impacts, priorities should be given to understand ice-free effects on marine ecosystems, the global energy budget, wave height and coastal erosion. In particular, understanding the nuances of the impacts of occasional daily ice-free conditions versus frequent monthly ice-free conditions versus ice-free conditions that occur for several months a year is needed to assess the true impact of what the transition of the Arctic sea ice cover into its new seasonal sea ice regime means in a warming world.

Data availability

The CMIP6 sea ice area data is the same as analysed in ref. 10 . The underlying SIC data, also used for the spatial plot (Fig.  5) , is available on the Earth System Grid Federation (ESGF, https://esgf-node.llnl.gov/search/cmip6/ ). The data for the CESM2-LE (ref. 112 ) is available at https://www.cesm.ucar.edu/projects/cvdp-le/data-repository . The data for the CLIVAR Large Ensemble Archive 38 is available at https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CLIVAR_LE.html .

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Acknowledgements

A.J. was supported by an Alexander von Humboldt Fellowship and NSF CAREER award 1847398. M.M.H. acknowledges support from NSF awards 2138788 and 2040538. J.E.K. was supported by NASA PREFIRE award 849K995 and NSF award 2233420. We thank J. Dörr for sharing the sea ice area data calculated for the SIMIP analysis 10 and C. Wyburn-Powell for the assistance with regridding of the CMIP6 models for the spatial analysis. We also thank the participants at the Interagency Arctic Research Policy Committee (IARPC) webinar on an ice-free Arctic for the helpful discussions. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making their model output available, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. We also acknowledge the US Climate and Ocean: Variability, Predictability and Change (CLIVAR) Working Group on Large Ensembles, the modelling centres that contributed to the CLIVAR Large Ensemble project, and the CESM2-LE project.

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A.J. decided on the overall scope of the article, wrote the majority of the article, and did all data analyses for the figures in the main article. M.M.H. and J.E.K. contributed to the writing of the manuscript, provided input on the article scope and figures, and edited the manuscript. M.M.H. also performed data analysis for supplementary figures and created one of the supplementary figures.

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The fraction of incoming shortwave solar radiation that is reflected by a surface, ranging between 0 and 1.

The variability in the climate system attributable to the chaotic nature of the climate system.

Dampening feedbacks in the climate system, reducing an initial perturbation.

Amplifying feedbacks in the climate system, enhancing an initial perturbation.

(SIA). The total area of sea ice present, without any threshold, calculated as sea ice concentration multiplied by grid area and summed over all Northern Hemisphere grid boxes. Note that sometimes, sea ice area is calculated only for grid cells with at least 15% sea ice cover.

(SIE). The area of all grid boxes that have at least 15% sea ice concentration, calculated as sea ice concentration multiplied by the area of all grid boxes with 15% or more sea ice concentration.

The change in sea ice area divided by the change in global or Arctic temperature or cumulative CO 2 emissions over the same time period.

(SSP). A forcing scenario that is part of the Scenario Model Intercomparison Project of CMIP6.

An irreversible change in an environmental condition.

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• Published: 19 December 2022

Centering Indigenous knowledge in suicide prevention: a critical scoping review

• Erynne Sjoblom 1 ,
• Winta Ghidei 1 ,
• Marya Leslie 2 ,
• Ashton James 2 ,
• Reagan Bartel 2 ,
• Sandra Campbell 3 &
• Stephanie Montesanti 1 , 4  

BMC Public Health volume  22 , Article number:  2377 ( 2022 ) Cite this article

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Indigenous peoples of Canada, United States, Australia, and New Zealand experience disproportionately high rates of suicide as a result of the collective and shared trauma experienced with colonization and ongoing marginalization. Dominant, Western approaches to suicide prevention—typically involving individual-level efforts for behavioural change via mental health professional intervention—by themselves have largely failed at addressing suicide in Indigenous populations, possibly due to cultural misalignment with Indigenous paradigms. Consequently, many Indigenous communities, organizations and governments have been undertaking more cultural and community-based approaches to suicide prevention. To provide a foundation for future research and inform prevention efforts in this context, this critical scoping review summarizes how Indigenous approaches have been integrated in suicide prevention initiatives targeting Indigenous populations.

A systematic search guided by a community-based participatory research (CBPR) approach was conducted in twelve electronic bibliographic databases for academic literature and six databases for grey literature to identify relevant articles. the reference lists of articles that were selected via the search strategy were hand-searched in order to include any further articles that may have been missed. Articles were screened and assessed for eligibility. From eligible articles, data including authors, year of publication, type of publication, objectives of the study, country, target population, type of suicide prevention strategy, description of suicide prevention strategy, and main outcomes of the study were extracted. A thematic analysis approach guided by Métis knowledge and practices was also applied to synthesize and summarize the findings.

Fifty-six academic articles and 16 articles from the grey literature were examined. Four overarching and intersecting thematic areas emerged out of analysis of the academic and grey literature: (1) engaging culture and strengthening connectedness; (2) integrating Indigenous knowledge; (3) Indigenous self-determination; and (4) employing decolonial approaches.

Conclusions

Findings demonstrate how centering Indigenous knowledge and approaches within suicide prevention positively contribute to suicide-related outcomes. Initiatives built upon comprehensive community engagement processes and which incorporate Indigenous culture, knowledge, and decolonizing methods have been shown to have substantial impact on suicide-related outcomes at the individual- and community-level. Indigenous approaches to suicide prevention are diverse, drawing on local culture, knowledge, need and priorities.

Peer Review reports

Suicide is a pressing health concern that continues to disproportionately impact Indigenous populations around the globe. Indigenous peoples of Canada, United States, Australia, and New Zealand experience rates of suicide approximately two to three times higher than the general population of their respective countries [ 1 , 2 , 3 , 4 , 5 ]. At the individual-level, the primary risk factors for suicide are mental health disorders, traumatic/stressful life events, and substance abuse [ 6 , 7 , 8 ]. All of these risk factors occur at disproportionately high rates in Indigenous populations as a result of the collective and shared trauma experienced with colonization and contemporary experiences of oppression and social exclusion including dispossession and disconnection from the land, loss of language and culture, grief and loss, and racism [ 9 , 10 , 11 , 12 , 13 , 14 ].

The rate of suicide among First Nations, Métis, and Inuit peoples in Canada is at least two times that of Canada’s general population [ 1 ]. In the United States, the rate of suicide among the American Indian/Alaskan Native population is approximately 3.5 times higher than those among racial/ethnic groups with the lowest rates and about 1.7 times higher than the overall US rate [ 2 , 3 ]. In Australia, suicide rates for Indigenous people ranged from 1.4 to 2.4 times that of non-Indigenous Australians in 2020 [ 4 ]. In New Zealand, suicide among Māori from 2010 to 2012 was 1.8 times greater than among non-Māori, with particularly high rates among Māori men [ 5 ].

Nevertheless, national-level data can obscure considerable variability in suicide rates and patterns between communities and wider regions [ 1 , 4 , 15 , 16 , 17 ]. While there is evidence demonstrating the connection to broader protective factors like employment status, educational attainment, and social support networks at the individual-level [ 6 ] this paper focuses on the community-level risk and protective factors for Indigenous populations. Research has identified a number of community-level factors that have been demonstrated to create resiliency to suicide in Indigenous populations, and can explain much of the observed variability in suicide rates across different communities. These protective factors include advances towards self-determination, efforts to secure Indigenous title to traditional lands, and activities that promote and protect Indigenous culture and language [ 15 , 16 , 18 ]. Research has also demonstrated that integrating Indigenous knowledge into mental wellness promotion, prevention and intervention initiatives has been associated with positive outcomes, including in suicide rates [ 19 , 20 , 21 ]. Calls continue to grow for upholding Indigenous peoples’ right to self-determination in defining effective and culturally-grounded means to address health and wellness needs in their respective communities [ 22 , 23 ]. Moreover, a growing body of research contends that standard suicide prevention programs—primarily rooted in Western individual-level efforts for behavioural change via mental health professional intervention—are culturally misaligned with Indigenous paradigms of health, mental wellbeing, and relationality [ 24 , 25 ]. For these reasons, many Indigenous communities, organizations and governments have been moving away from initiatives designed for the general population and moving towards more cultural and community-based approaches for mental health promotion [ 26 ].

Despite these advancements in knowledge and understanding of the unique factors impacting Indigenous peoples’ risk and resilience for suicide, suicide prevention initiatives continue to fall short of meeting the needs of Indigenous peoples who are at a higher risk of adverse mental health outcomes and experience limited access to appropriate care and resources. Overall, there is a notable gap in comprehensive community-based, culturally safe suicide prevention resources for Indigenous communities. In recent years, many suicide prevention programs targeting Indigenous populations and incorporating Indigenous approaches have been developed; however, a thorough review of these initiatives has yet to be conducted. While a number of reviews of Indigenous suicide prevention initiatives have been conducted, they have focused on specific Indigenous groups (e.g., Indigenous youth, Inuit, or American Indian/ Alaska Native populations), particular programs employed in Indigenous populations (e.g. Adolescent Suicide Prevention Project), or focused on specific types of evidence (e.g. case files or evaluated programs only). To the authors’ knowledge, no reviews to-date have broadly examined suicide prevention efforts employed in Indigenous populations nor explicitly the contribution of Indigenous knowledge. A comprehensive exploration of how Indigenous approaches have been incorporated into suicide prevention efforts to-date could be instrumental in informing and supporting further development of Indigenous-driven suicide prevention. Consequently, this review explores how Indigenous knowledge and approaches have been incorporated in suicide prevention for Indigenous populations.

A critical scoping review was conducted to conceptualize, map and identify gaps in the literature and to assess if Indigenous knowledge was the guiding principle in developing these programs and interventions. Scoping reviews aim to map ‘the key concepts underpinning a research area and the main sources and types of evidence available [ 27 , 28 ]. As such they differ from systematic reviews in focusing on broader topics and a range of study designs with little emphasis on quality; nor are they designed to perform detailed assessments or synthesis of findings [ 29 ]. Our critical scoping review aligned with the processes and objectives of a scoping review as recommended by Arksey and O’Malley [ 29 ] and Levac, Colquhoun, and O’Brien [ 30 ]. Additionally, we applied a two-step process to better align with ethical standards of research involving Indigenous peoples, and to enable Indigenous knowledge to inform the evidence appraisal and interpretation: 1) Indigenous and non-Indigenous co-authors synthesized the evidence; and 2) input was sought from a reference group of Indigenous community leaders with expertise in Indigenous knowledge systems.

Community engagement

This review utilized a community-based participatory research (CBPR) approach. The need for a critical scoping review arose out of an existing project to develop a Métis suicide knowledge awareness training program through extensive community engagement sessions led by the Métis Nation of Alberta (MNA). At the start, the MNA approached the academically-situated members of the research team (SM, WG) to work together to pursue research funding and work in partnership to develop a community-driven, culturally-grounded suicide prevention program. It was determined that this scoping review would provide a thorough knowledge base for how Indigenous approaches have been integrated into suicide prevention targeting Indigenous populations and inform the development of a Métis suicide knowledge awareness intervention. Consequently, the methods, emergent themes and subthemes, analysis of outcomes, and final manuscript were all co-developed between the MNA team members and University-situated research team members.

Information sources and search strategy

A scoping review of both academic and grey literature was employed to examine relevant evidence on how Indigenous knowledge has been incorporated in suicide prevention initiatives. We felt it key to include an online grey literature search in recognition that many Indigenous communities may have implemented suicide prevention efforts that might not always be formalized in the academic literature. We first developed a list of search terms in consultation with a research librarian and used combinations of the following search terms and their synonyms: (suicid* or "self harm"); (communit* or family or families or caregiver* or gatekeeper*); (awareness or prevent* or know* or educat* or train or trained or training); (indigenous people/ or alaska native/ or american indian/ or canadian aboriginal/ or first nation/ or indigenous australian). Search strategies were designed to be suitable to the specific features of each database (Additional file  1 : Appendix A). The following databases were searched: Prospero, Wiley Cochrane Library, Ovid Embase, Ovid Medline, Ovid Global Health, Ovid PsycInfo, EBSCO CINAHL, EBSCO Socindex, ProQuest Dissertations and Theses Global, and SCOPUS. All of the databases were searched from inception to November 2021. The search strategy included both text words and controlled vocabulary (eg: MeSH, EMTREE, etc.) for the concepts “Indigenous people” and “suicide prevention” and “community/caregiver awareness.” In addition, the reference lists of articles that were selected via the search strategy were hand-searched in order to include any further articles that may have been missed.

We also systematically searched for grey literature (Indigenous texts, songs, videos, artform, reports, etc.) in the following online databases and resource hubs: University of Alberta-Native Studies Databases, National Collaborating Center for Indigenous Health, International Journal for Indigenous Health, Health Canada’s National Aboriginal Youth Suicide Prevention Strategy (NAYSPS), Center for Suicide Prevention, and the Thunderbird Partnership Foundation. Additionally, we ran a customized Google Scholar search on the terms “Suicide Prevention” AND (Indigenous OR Aborigin OR First Nation OR Inuit OR Métis OR Native) and examined the first 20 pages of the returned results.

Selection process

The articles resulting from the search were screened for relevance and subjected to a critical appraisal process by two reviewers (WG, ML). Only English language publications were considered. Relevance was established by the research team first by reviewing the title and abstracts of the identified literature against the review objectives. Specifically, articles were included if (1) the papers discussed programs and/or initiatives that aimed to prevent suicide in Indigenous populations and (2) the target population were Indigenous populations of Canada, United States, Australia and New Zealand. Canada, the United States, Australia, and New Zealand are commonly seen as natural comparators in terms of Indigenous well-being. These jurisdictions consistently rank highly on the United Nations Development Programme’s Human Development Index (HDI), yet all have minority Indigenous populations with much poorer health and social conditions than their non-Indigenous population [ 31 ], including a disproportionate burden of suicide. Moreover, Indigenous peoples in these countries share similar experiences as subjects of British colonialism, including comparable colonial histories, laws, policies, and political structures. Such similarities include processes of treaty making (except Australia), policies aimed at assimilation, paternal protectionism, dislocation from the land to make way for settlers, and loss of culture [ 32 , 33 ]. These countries have also undertaken comparable efforts towards reconciliation with Indigenous peoples in recent years [ 34 ]. Where relevance could not be determined from the article title or abstract alone, a review of the full text was conducted. There was no limitation based on study design and source type (academic and grey literature were included). Any literature that did not fit into the above criteria, or that addressed other mental health prevention programs (other than suicide) was excluded. Grey literature articles were screened first on the basis of relevancy of their title to the research objectives of this scoping review, with further review of the full document led to the exclusion of any additional irrelevant grey literature articles.

Article and data management

The following information was extracted from academic literature into a standard extraction form: authors, year of publication, type of publication, objectives of the study, country, target population, type of suicide prevention strategy, description of suicide prevention strategy, and main outcomes of the study. We also took detailed notes on whether suicide knowledge was defined from the perspective of the local Indigenous communities and the level of involvement of local Indigenous communities, including in project development and paper co-authorship. Findings from grey literature were summarized by the research team with consideration of the following questions: What does suicide and suicide prevention mean from an Indigenous perspective? How has Indigenous knowledge been incorporated in suicide prevention?

Thematic analysis

We adopted a thematic analysis approach guided by Métis knowledge and practices to synthesizing and summarizing the findings. Our research team is composed of Indigenous (Métis) and non-Indigenous members. First, two researchers (WG and ML) worked together to read all the articles, annotate them, and identify broad thematic categories. Next, additional researchers (SM, AJ, and RB) discussed each theme and subtheme until they reached consensus in a team meeting. After this team meeting, WG and ML compared and contrasted the various findings to identify recurrent and unique themes. Findings from both academic and grey sources were merged and appropriate themes were applied. All team members reviewed themes, discussed disagreements between them and reached a consensus. Each term, phrase and/or meaning used to contract the categories and themes were confirmed with Métis knowledge holders. The academic literature and grey literature articles were then thematically analyzed by WG and ML simultaneously who used NVivo™ to apply codes to the articles on a consensus basis. This also involved examination of articles to document similarities for the purpose of identifying common themes across suicide prevention, while also detailing their distinctions and differences.

The review process resulted in the collection of 1,352 academic papers and grey literature documents, with 391 articles ultimately considered after duplicates were removed. Of these, 961 were excluded as, despite appearing in the search, upon interrogation of the article title and abstract, it was determined that the content of the article fell outside of the scope of the review objectives. 197 articles necessitated full text review as inclusion could not be determined from the title and abstract alone. Of these, 149 further papers were excluded because they did not align with our review objectives nor did not meet inclusion criteria. 8 articles were identified via a hand-search of the reference lists of relevant reviews. Sixteen articles from the grey literature were ultimately included as well. Thus, our final search process resulted in 72 articles—56 of which were academic papers and 16 grey literature documents—that were included for extraction (Fig.  1 ). The characteristics of the articles along with the overarching themes identified through thematic analysis are summarized in the following section.

Flow chart describing included and excluded articles

Article characteristics

This section presents an overview of the suicide prevention strategies targeting Indigenous populations examined in this review, namely describing the Indigenous communities involved, target demographics, and the types of strategies employed. Articles from the academic literature were primarily peer-reviewed outcomes of primary research activities involving the above types of suicide prevention strategies along with several different types of academic reviews. Articles obtained from the grey literature were diverse and included for suicide prevention guides, strategies, toolkits, outcomes of community engagement, and more typically authored by Indigenous communities and organizations.

Indigenous population and sample

Thirteen articles focused on Indigenous Australians; thirty-two on Indigenous peoples living in the United States (US) (specifically fourteen with Alaska Natives, four Alaska Natives and/or American Indians; eleven American Indians/Native Americans, and three Native Hawaiians); nine on Indigenous peoples in Canada; one on Indigenous peoples in North America; and one on Indigenous peoples in Canada, the US, Australia and New Zealand. No articles involved the Māori of New Zealand. The sample populations reported by studies included Indigenous youth, general community members, or specific subpopulations (such as Indigenous prisoners, students, or males). Notably, no papers incorporated considerations for lesbian, gay, bisexual, transgender, queer, and two-spirited (LGBTQ2 +) or gender diverse Indigenous persons.

Types of articles

The main prevention strategies employed in articles examined in this scoping review comprised: culture as treatment; community prevention activities; gatekeeper training; and education/awareness initiatives. These articles presented peer-reviewed outcomes of primary research activities involving the above types of suicide prevention strategies (39 articles). Methods employed in the primary research articles included pre/post studies (8); randomized (4) and non-randomized (4) control trials; retrospective study design (1); qualitative methods including focus groups, workshops, interviews, and more (15); and mixed-methods approaches (7). The remaining articles were papers describing intervention development and implementation (11 articles) and different types of academic reviews (6 articles). See Additional file 2 : Appendix B for a table detailing key information extracted from academic literature. Articles obtained from the grey literature concerned: guides, strategies, toolkits for suicide prevention in Indigenous populations, outcomes of community engagement on suicide prevention; curriculum documents; brochures, magazine or news articles describing suicide prevention projects; and literature reviews (see Additional file 3 : Appendix C).

Culture as treatment specifically involved engaging Indigenous culture to mitigate suicide risk or “treating” suicidality among individuals, usually on a one-on-one basis. Community prevention initiatives typically involved empowerment programs, multi-level approaches, broader resiliency strategies targeting Indigenous groups and communities at high risk of suicide, or community-based participatory research to inform program development. Gatekeeper training strategies featured prominently. Gatekeeper training involved teaching specific groups of people in the community how to identify and support individuals at high risk of suicide. Education/awareness initiatives involved activities that explicitly aimed to improve suicide knowledge, attitudes, and/or awareness to develop knowledge/skills that are known to be protective against suicide via, for example, school-based programs for youth, multi-media education sessions to interested community members, or culturally-tailored life skills training for youth. 25 articles involved community prevention, 13 concerned educational/awareness initiatives, 7 featured gatekeeper trainings, 3 involved culture as treatment, 1 featured both community prevention and gatekeeper training components, and 1 involved community prevention, gatekeeper, and education/awareness approaches. The remaining 6 were scoping or systematic reviews of Indigenous suicide prevention projects/programs. Community prevention and education/awareness initiatives involved primary prevention that address upstream root causes and aim to prevent suicide ideation or attempts from even happening by reducing risk and promoting protective factors. Gatekeeper training and culture as treatment involved secondary prevention which endeavour to provide support to persons at immediate risk for suicide/self-harm. No articles involved tertiary prevention efforts which might involve postvention to reduce the risk of further suicides or clusters. Levels of suicide prevention and corresponding articles are detailed in Fig.  2 .

Articles by level of suicide prevention [ 35 ]

The findings are presented through four overarching and intertwining thematic areas that emerged out of analysis of the academic and grey literature. These thematic areas focus on (1) engaging culture and strengthening connectedness; (2) integrating Indigenous knowledge; (3) Indigenous self-determination; and (4) employing decolonial approaches. We also highlight components of strategies that exemplify each theme. Table 1 summarizes themes, subthemes and corresponding articles.

Engaging culture and strengthening connectedness

Engaging culture and strengthening connectedness to prevent suicide emerged as an important theme across the articles examined in this critical scoping review. All articles highlighted connection to culture as a crucial component to meaningful and effective suicide prevention in Indigenous populations. Within this theme, we focused on analyzing the ways in which culture and efforts to strengthen connectedness are integrated into suicide prevention content and the resulting impacts.

Generally, engaging culture took on several different forms. First, some initiatives were built around the notion of “culture as intervention” or “culture as treatment,” where engaging Indigenous culture was seen as an important means for mitigating suicide risk or “treating” suicidality among individuals. Culture as intervention or treatment could be the main strategy, or a component of a broader strategy, and took on several forms including resilience retreats/culture camps, cultural teachings/values, ceremony, sharing circles, storytelling, creative arts, narrative approaches to psychotherapy, art therapy, other locally-relevant healing/coping strategies and more [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].

In these initiatives, individual-level effects on suicide risk via cultural connections included increases in the number of protective behaviours which authors argue were fostered by culture-specific beliefs and experiences that make life enjoyable, worthwhile, and meaningful [ 38 ]. Individual-level impacts among participants included reductions in distress, bolstering of protective factors, and reduction in suicide/self-harm behaviours [ 38 , 40 , 44 ]. Activities that involved local Indigenous culture as suicide prevention were observed to have a measurable impact on suicide-related outcomes such as increased positive mood, feelings of belongingness, and perceived coping of participants, even in programs where the specific topic of suicide was not breached [ 36 , 37 ].

Second, engaging cultural connections arose as an important means to create new or adapt existing suicide prevention strategies to increase effectiveness and appropriateness in Indigenous contexts. Initiatives often took the form of gatekeeper trainings or educational/awareness initiatives and were typically created or adapted from the ground up either in partnership with or, less frequently, under the leadership of the respective Indigenous community [ 48 , 49 , 50 , 51 ]. Cultural inclusion in the design of programs or adaptation involved a multitude of factors including: acknowledgement of the impacts of colonization and ongoing marginalization on suicide in Indigenous contexts, integration of Indigenous pedagogies (i.e. team-teaching, land-based learning, experiential/hands-on activities, etc.), emphasis on the holistic aspects of wellbeing, focus on strengths-based approaches, incorporating art, and inclusion of Indigenous languages and cultural values [ 36 , 45 , 46 , 47 , 48 , 51 , 52 , 53 , 54 , 55 ].

Authors underlined the positive impacts of integrating local Indigenous culture at both the individual- and community-level. For instance, individual-level impacts for gatekeeper trainings included improved attitudes toward suicide, increases in participants’ knowledge and confidence in how to identify individuals at-risk of suicide, increases in intended and actual assisting behaviours, and significant improvements in understanding the links between cultural strengths, social and emotional wellbeing and suicide prevention [ 47 , 48 , 51 , 52 ]. Moreover, participants in culturally-grounded suicide education and awareness initiatives were shown to have less suicidal ideation and “negative thinking”, expressed fewer feelings of hopelessness, could come to terms with the ‘cycle of grief,’ demonstrated reduced stigma towards suicide and increased willingness to seek help, and had an increase in psychological service utilization [ 49 , 56 , 57 , 58 ]. Participants in community suicide prevention programs which integrated culture had significant increases in positive mood, feelings of belongingness, perceived coping, reasons for living, and overall resiliency [ 36 , 38 , 41 ].

Strengthening connectedness was consistently identified by articles as an important element for effective suicide prevention in Indigenous populations. We included it along with the theme of engaging culture as it was typically discussed as a key Indigenous cultural value which contrasted conventional Western approaches to suicide prevention. While there is much diversity in Indigenous ways of being and knowing, the ontologies of interconnectedness and relationality are shared across many of the Indigenous populations involved in articles reviewed. Strengthening connectedness comprised emphasis on encouraging intergenerational relationships, particularly between youth and Elders, strengthening connectedness within families and whole communities, and bolstering cultural continuity.

Fostering relationships between youth and Elders was a frequent community-identified means of prevention to support protective factors and promote healing among youth via opportunities to learn cultural teachings, language, and connect with the land and spirit with Elders who are the holders of a community’s traditional knowledge. Three strategies featured approaches that brought together youth and Elders as part of suicide prevention, which were noted to have implications for protective factors against suicide among youth such as strengthening youth reasons for living and combating “discontinuity” [ 38 , 60 , 61 ]. Furthermore, a participatory action research project that sought to explore community-identified risk factors as well as strategies to strengthen protective factors found connection between youth and Elders to be an important community-level strategy to suicide prevention [ 62 ].

This importance of bringing together youth and Elders was also echoed across the grey literature, typically as outcomes of community engagement on suicide prevention. Reports emphasized how Indigenous culture, knowledge, and language—which impart protection against suicide—are transferred from Elders to youth and suicide prevention thus needs to foster these relationships [ 63 , 64 , 65 , 66 ]. This sentiment is embodied in a quote from an Elder from an Australian Indigenous community experiencing high rates of youth suicide and self-harm: “The only way to stop suicide is to fulfill our cultural obligation to teach our young ….strength of character through strength of culture” [ 64 ]. Other suicide prevention initiatives did not necessarily bring together youth and Elders as an intervention component, but still created opportunities for connecting them as part of community engagement processes [ 44 , 59 , 66 , 67 , 68 , 69 , 70 , 79 ].

Notably, academic and grey literature articles also spoke to the importance of strengthening connectedness within families and across community as part of suicide prevention [ 62 , 69 , 71 , 72 , 73 ]. For families, this could include restoring and strengthening connections within and between families through shared activities (especially cultural or spiritual activities); offering life skills programs; and providing access to education and/or training [ 62 , 71 ]. For communities, fostering connections might involve a focus on youth (i.e., drop-in centres, camps, connect to Elders, health promotion and education sessions, parenting programs, restore sporting competitions); restoring and strengthening a sense of community through shared activities (i.e., community events, fun days, competitions, projects); upholding self-determination; men’s and women’s groups; and providing access to employment, education, housing and transport [ 62 , 72 ].

Several articles noted the importance of efforts to bolster cultural continuity as part of suicide prevention. Authors stressed how strengthening “cultural continuity,” or the degree to which a community participates in actions symbolic of their sense of community as a cultural group, has positive implications for mental wellness, resilience, and thus suicide in Indigenous contexts [ 65 , 71 , 74 , 75 ]. Many cite the research of Chandler and Lalonde [ 15 , 16 ] to underline that a community’s effort to preserve the continuity of their collective culture can impact continuity at the individual-level and act as a hedge against suicide by facilitating individuals’ endurance through life’s routine hardships and build a connection to a sense of self and identity.

Despite the expressed importance of including cultural continuity in suicide prevention, no initiatives involved explicit efforts to support continuity of collective culture at the community-level to impact suicide. When culture was integrated into suicide prevention, it was primarily done so to impact the wellbeing, knowledge and/or behaviours of individuals, not the community as a whole. This was also reflected in the outcome measures captured in program evaluations.

A final subtheme around the definition of cultural intervention emerged from the literature reviewed in this scoping study. Many articles made the distinction between cultural intervention and culturally appropriate or culturally safe intervention. In the former, Indigenous culture is both a central focus of the intervention activities and underlies the theory guiding the intervention. In this sense, a cultural intervention is more likely to be transformative; underpinned by Indigenous ontologies, epistemologies, and/or worldviews; incorporate Indigenous notions of suicide; and be rooted in community defined and prioritized health issues [ 38 , 39 , 68 , 69 , 73 ].

Culturally-appropriate, -sensitive, -tailored, or -safe interventions, on the other hand, may incorporate Indigenous cultural activities, teachings, language and more but can still be dominated by and reproduce conventional Western/colonial understandings of mental wellness and perpetuate colonial power dynamics [ 38 , 39 , 55 , 69 , 73 , 76 , 77 , 78 ]. As one author notes, the focus on culture by outsiders in health intervention has “too often been a shallow or surface translation describing more macro-level, formulaic, and ahistorical aspects of [Indigenous] life.” [ 53 ]. Cultural adaptations of conventional suicide prevention strategies may be more susceptible to reliance on the underlying Western/colonial assumptions of the original intervention and typically involve modifying “non-active” treatment components of the intervention for cultural acceptability such as language or style of the intervention, who delivers it, or the treatment setting [ 50 ]. Many adaptations also place importance on finding a balance between meeting community/cultural needs and preserving fidelity/standardization [ 54 , 56 , 71 , 80 ].

Integrating Indigenous knowledge

Integrating Indigenous knowledge into suicide prevention arose as a prominent theme across articles included in this scoping review. In this section, we specifically focus on how Indigenous knowledge impacts the conceptualizing of the issue of suicide and subsequently shapes how programs are designed and implemented. The majority of articles attempted to define the issue of suicide from an Indigenous perspective as a foundational step to developing an appropriately community-driven initiative. This was achieved through two methods: via author-driven definitions or via definitions acquired through community engagement processes. In cases where definitions were proposed by authors, suicide was commonly defined in connection to assumed Indigenous notions of wellness in general. For example, one study’s authors characterized suicide in alignment with Indigenous perspectives which “[focus] more on understanding and addressing what is going on around the individual than addressing what is going on inside” [ 76 ]. Focusing on what is going on around the individual meant consideration of the “complex socio-cultural, political, biological and psychological phenomenon that needs to be understood in the context of colonization, loss of land and culture, transgenerational trauma, grief and loss, and racism and discrimination” [ 46 ].

Some strategies informed by this notion employed multi-level approaches (community-wide events, policy efforts, educational programs for youth, and traditional ceremonies) that involved multiple sectors of the community simultaneously (individuals, families, wider community) [ 38 , 69 , 73 , 76 , 90 ]. Others took aim at intervening on one or more of these broader determinants like, for example, seeking to support knowledge transfer via intergenerational relationships or just generally integrating Indigenous culture into curriculum content [ 60 ]. Other initiatives incorporated locally-relevant content, information about Indigenous culture or colonization and ongoing marginalization and how they contribute to the issue of suicide in Indigenous contexts [ 46 , 49 , 81 ].

On the other hand, articles that sought community-based understandings of suicide via engagement processes tended to emphasize a focus on strengths and resilience in opposition to the typical focus on deficits and problems [ 45 , 53 , 54 , 55 , 60 , 67 , 69 , 73 , 76 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]. This is illustrated in a statement from an Elder who co-led the development of a youth resiliency project and co-authored the resulting paper: “Why do we talk about suicide all the time!? Let’s talk about love!” [ 45 ]. Thus, strategies informed from Indigenous notions of suicide intentionally shifted from a focus on deficit—which is more typical of conventional Western suicide prevention—to a focus on life promotion with attention to existing community strengths and assets, while upholding community control and sovereignty and supporting local empowerment [ 44 , 68 , 76 , 90 , 91 , 93 ]. This connects with the subsequent section in which we discuss the theme of Indigenous self-determination in suicide prevention.

Indigenous self-determination

Self-determination in suicide prevention was frequently identified as an essential requirement for success. The majority of articles employed some means to ensure some level of self-determination was achieved via community engagement in the design and/or implementation of suicide prevention. This was commonly achieved by employing community-based participatory research (CBPR) or participatory action research approaches. CBPR was utilized “to address power differentials through shared learning ….[it] is a move toward reconciliation, reciprocity, and production of culturally relevant prevention measures” [ 68 ]. Community engagement was seen by authors as a key requirement for success, as it increases community relevance, appropriateness, and in particular, ownership of suicide prevention [ 38 , 39 , 45 , 57 , 68 , 76 , 82 , 93 , 97 , 99 , 100 ].

The emergent subtheme of ownership referred not to the legal sense of the word (i.e., the right to possess and control the initiative) [ 100 ] but more to the community stepping up to take on responsibility in executing an initiative while being invested in seeing out its success [ 45 , 57 , 65 , 83 , 97 , 99 ]. In this sense, community ownership in a strategy has implications for acceptability, uptake, participation, and dedication to investing necessary resources. Another reason why community ownership was expressed as crucial to suicide prevention success is because it is correlated with longevity and sustainability [ 45 , 57 , 83 ].

Indigenous authorship also emerged as a notable subtheme of Indigenous self-determination in suicide prevention. Many of the articles reviewed here highlighted that they were co-authored by members of the specific target Indigenous communities or members of broader Indigenous communities [ 36 , 39 , 41 , 43 , 44 , 45 , 46 , 47 , 50 , 51 , 53 , 56 , 60 , 67 , 68 , 69 , 80 , 84 , 94 , 97 ]. Indigenous authorship was important to ensure materials represent content as intended by community stakeholders, particularly when it comes to native language expertise [ 60 ].

Employing decolonial approaches

Lastly, employing decolonial approaches in suicide prevention creation and implementation was a prominent theme in the literature. Decoloniality—or the creation of “locally-governed, community-based, and culturally-responsive systems of care” [ 55 ]—has been touched upon in many of the themes already discussed above (i.e., incorporating Indigenous knowledge and culture, upholding self-determination, community-engaged approaches). In this section, we hence focus on other attributes of decolonial approaches highlighted in the literature, namely avoidance of pan-Indigenous approaches, integrating contextual considerations, and more generally, approaches that might diverge from features of conventional Western suicide prevention.

Use of pan-Indigenous approaches was cautioned in the literature and authors advised against application of Indigenous-driven suicide prevention in contexts that they were not designed for [ 56 , 67 , 71 , 90 ]. This is because pan-Indigenous strategies may not be reflective of diverse cultural practices, values, sociohistorical context, and geographic considerations unique to each group. Authors stress that programs need to be adaptable to the local community context. This is especially pertinent in the issue of suicide, which can vary significantly from community to community [ 56 , 68 ].

In line with this, integrating contextual considerations into suicide prevention was also expressed as an important decolonial component. This subtheme was most prominent in recommendations for suicide prevention in Indigenous populations outlined in the grey literature. Contextual considerations included gaining the knowledge of a community’s unique risk and protective factors, incorporating local examples where possible, respect for and adherence to local protocol, involvement of local experts/Elders, developing culture-centered understanding of suicide, assessing community readiness, building and maintaining relationships, contemplating the impact of recent incidents of suicide, considering existing level of trauma and unresolved grief, and incorporating healing components [ 48 , 55 , 68 , 76 , 93 , 98 ].

Other decolonial approaches detailed in the literature specifically took aim at breaking down some of the features common in conventional, Western programs that tend to persist in Indigenous programs (especially cultural adaptions) despite communities finding them unhelpful. This includes allowing for flexibility, especially in contrast to rigid, standardized procedures employed by some interventions like, for example, in Applied Suicide Intervention Skills Training (ASIST). Flexibility could include encouraging use of Indigenous language, carrying out sessions in outdoor settings, allowing for adjusting of required time commitment, creating space for ceremony/spirituality, and more [ 54 , 59 , 68 , 79 , 82 , 92 ].

Articles also discussed avoidance of clinical language or jargon which can create barriers to understanding content. Finally, articles discussed efforts to break down power imbalances between Western and Indigenous approaches by, for example, focusing on local empowerment and capacity by training Indigenous facilitators, involving locally-recognized experts/leaders/healers, and employing Indigenous ways of learning versus a focus on employing clinical experts or utilizing the typical didactic educational models [ 44 , 48 , 54 , 79 , 91 , 98 ]. According to authors, breaking down power imbalances could also involve creating a practice of reflexivity as part of the strategy, where researchers and developers actively reflect on their relationships, position, and privilege and how they are fulfilling their obligations to community [ 45 , 54 , 82 , 91 , 97 ].

This scoping review set out to identify and describe what is known about the types of Indigenous approaches to suicide prevention that are employed with Indigenous populations and their implications for program outcomes. All articles, from both the academic and grey literature, were from Canada, the United States, or Australia. Notably, no suicide prevention initiatives from New Zealand were identified in our search strategy. We hypothesize that this may be because a higher rate of suicide among Māori in comparison to non-Māori is a relatively newer phenomenon and no public-facing reports on prevention strategies have been published yet [ 101 ]. There was also a notable absence of inclusion of considerations for Indigenous sexual minorities and gender diverse persons in suicide prevention. While the suicide rates among these groups is not well-known, two-spirited, queer, gay, lesbian, bisexual, or transgendered Indigenous persons experience suicide-related risk factors at a much higher rate than cis-gendered, heterosexual Indigenous persons [ 102 ]. Indigenous sexual minorities and gender diverse persons’ experiences around suicide, risk and protective factors may also be unique—including compounded effects of discrimination, arguably necessitating special consideration in suicide prevention [ 103 ].

Another noteworthy gap was the absence of articles concerning Indigenous approaches to tertiary suicide prevention, namely suicide and crisis response and postvention. Conventional crisis response and acquiring care via the medical system for a suicide attempt or self-harm may be present problems for Indigenous peoples as these systems may be culturally unsafe and possibly less helpful than they could be. In order to respond to a lack of accessible and culturally safe crisis and suicide response services, many First Nations in Canada are establishing their own mobile crisis response teams (e.g., Manitoba Keewatinowi Okimakanak, n.d.; Southern Chiefs Organization, 2022; Thunderbird Partnership Foundation, 2018 [ 104 , 105 , 106 ]). The activities of these teams commonly have culturally inclusive elements, but still follow mostly Western crisis response models. We are unaware of any Indigenous approaches to suicide postvention; however, given the issues many Indigenous communities face following the suicide of a fellow community member including trauma, grief and loss, and possibilities of suicide clusters [ 107 ], further exploration is warranted.

The grey literature articles included toolkits, guides, information resources, and strategies to support Indigenous communities and/or organizations in developing and implementing suicide prevention programs and increasing suicide knowledge and awareness. Both academic and grey literature articles emphasized the role of culture, community connectedness and Indigenous knowledge in the prevention of suicide. Whereas the grey literature articles provided practical “how to” resources, information and guidance for delivering suicide prevention programs, the academic literature provided knowledge of “what works” by reviewing the outcomes of different suicide prevention strategies and interventions in Indigenous communities, including effective approaches to community engagement, and identifying key components of culturally-appropriate, -sensitive, -tailored, or -safe interventions.

Outcomes of this critical scoping review align with much of the work being done within the context of the broader field of Indigenous mental wellness promotion and Indigenous health research in general, which is increasingly looking to approaches that are decolonizing; founded on community engagement and self-determination; and inclusive of Indigenous culture, language and knowledge [ 26 , 108 , 109 ]. Moreover, in Indigenous mental wellness promotion, there is also an increasing focus on intervening beyond the individual to the family- and community-level, with an emphasis on fostering strength and resiliency [ 110 , 111 ]. Family and community are important resources for developing a sense of belonging, connectedness, meaning, and identity, all of which are well-established protective factors for overall mental wellness and suicide in Indigenous populations [ 112 ]. It is within the family and the community where Indigenous culture, knowledge and language is transmitted, positive cultural identity forms, and where one can seek out support in a crisis [ 112 ]. However, in many Indigenous contexts, family and community environments have been severely damaged with colonization [ 13 , 113 ]. Hence why community engagement outcomes consistently point to a need to move beyond the individual to support family and community strengths [ 53 ]. This need was echoed in articles examined in this review; however, we found that few suicide prevention initiatives employed broader family and community approaches.

Many of the suicide prevention programs identified as “community prevention” still primarily focused on protective and risk factors among individual participants and not on broader familial and community elements. As noted by Cox et al., [ 62 ] family-level intervention activities could involve a wide variety of initiatives—but especially cultural or spiritual activities—to restore and strengthen connections within and between families. Community-level suicide prevention activities might focus on bolstering the fabric of community by creating places and reasons to gather, unite, and build connection. This could be achieved through a variety of community-driven activities, for example via sports, competitions, ceremony, celebrations, gardening, learning programs, traditional land use, medicine picking, community groups, advocacy, and self-determination efforts [ 62 ].

This gap was also observed within program evaluations examined for this review, the majority of which did not incorporate any type of community-level outcome measure. The rare few that did attempt to measure, for example, community connectedness via social network characteristics [ 93 ], changes in community readiness for suicide prevention [ 39 ], or community-level changes in suicide and self-harm behaviours [ 59 ]. No evaluations included family-level outcome measures. Overall, we found that efforts to include family- and community-level initiatives within suicide prevention and evaluation did not adequately meet the need or sufficiently answer the consistent calls for such considerations from Indigenous communities [ 53 , 83 , 85 ]. Future suicide prevention development and implementation must do more to address this need.

In this review, we examined the ways in which Indigenous approaches have been incorporated into suicide prevention targeting Indigenous populations and the resulting impacts. Incorporation of Indigenous culture and knowledge as well as decolonizing efforts into suicide prevention was consistently shown to have positive implications for suicide-related outcomes. The meaningful inclusion of these components into suicide prevention, however, is dependent on the extent to which community self-determination is respected and upheld. From the articles reviewed here, which primarily involved partnerships between Western institutions and Indigenous communities, self-determination was best upheld where community engagement efforts were employed from before the development of a prevention program began all the way through implementation and evaluation to the finish. These engagement processes—especially those utilizing community-based participatory research (CBPR) approaches—also had a particular focus on restructuring the relationships between power, knowledge production, and public health policy and practice [ 24 , 55 ]. Suicide prevention strategies that employed such community-engaged approaches were transformative and privileged Indigenous knowledge, culture, language, and locally-driven strategies that were decolonizing in nature. Decades have come to pass with no alleviation of suicide rates within Indigenous populations in Canada, the United States, Australia, and New Zealand. Dominant Western approaches to suicide prevention by themselves have largely failed at addressing suicide in Indigenous populations [ 114 ]. Consequently, future suicide prevention development and implementation must endeavor to privilege self-determined Indigenous approaches that are developed via comprehensive community engagement processes. While our review examined protective factors for suicide prevention within Indigenous populations in Canada, the United States, Australia, and New Zealand, higher suicide rates among Indigenous peoples compared to non-Indigenous populations have also been reported in Latin American countries including Brazil, Peru, Colombia, and Chile [ 115 ]. However, there is limited research evidence on Indigenous suicide in Latin American countries. A possible explanation for this lack of research could be that suicide among Indigenous peoples in those countries is significantly underreported.

Many of the suicide prevention efforts incorporated an evaluation component to measure impacts on suicide-related outcomes and were able to demonstrate some level of evidence for effectiveness. However, few evaluations were scientifically and methodologically rigorous [ 83 , 116 , 117 ]. In a systematic review of evaluations of suicide prevention targeting Indigenous populations and critique of their methodological quality, authors point out that evaluations were vulnerable to bias; heavily focused on proximal outcomes versus intermediate or distal outcomes; measured proxy outcomes like hopelessness and depression while ignoring suicide and self-harm behaviours entirely; and failed to consider cost [ 117 ]. In addition, it has been noted that there are considerable challenges related to suicide surveillance, including that national health data systems often lack Indigenous identifiers, do not capture data from some regions, and do not routinely engage Indigenous communities in data governance [ 118 ]. While we recognize that evaluation methodologies are heavily grounded in Western ways of knowing and constructs of “value” [ 119 ], evaluation outcomes are often crucial for justifying to funders (most often Western/colonial institutions) for continued resources for programming [ 117 ]. Moreover, evaluation can provide valuable insight into program goals, activities, strengths, areas for improvement, and cost-effectiveness [ 120 , 121 ] and do not have to rely purely on Western epistemologies.

Practices to uphold Indigenous self-determination in suicide prevention (i.e. community engaged, participatory approaches) should extend to the evaluation phase in order to ensure outcomes measured are also meaningful to community and reflect Indigenous worldviews and ways of knowing [ 119 , 121 ]. Evaluation processes that are culturally inclusive and community-driven have a number of crucial benefits. For instance, community-engaged suicide prevention evaluation ensures the needs and knowledge of the community remain central and community ownership, control, access and possession of evaluation outcomes and surveillance data are safeguarded. This includes how outcomes and surveillance data are shared, mitigating potential for harm that Indigenous communities have faced with non-inclusive research and surveillance [ 119 , 122 ]. Unfortunately, most of the suicide prevention initiatives examined for this scoping review did not extend community engagement processes to the evaluation phase and thus failed to develop measures and evaluation tools based in Indigenous knowledge, focused on community-level outcome measures, or inclusive of Indigenous culture. Future work involving suicide prevention evaluation should ensure processes of community engagement are incorporated.

This critical scoping review is limited in two ways. Firstly, scoping studies provide a narrative or descriptive account of research and do not seek to appraise their quality or effectiveness [ 29 ]. Thus, in the context of this paper, which seeks to describe how Indigenous approaches have been integrated into suicide prevention, we do not evaluate individual program content, approaches to establishing programs, their methods, or their impacts. Instead, in employing a critical lens to this review, we sought to examine underlying structures of power and meaning that organize relationships, institutions, and knowledge production in suicide prevention, how Indigenous peoples are challenging these structures, and the resultant impacts on suicide outcomes [ 86 ] Secondly, this study is limited to the review of public-facing, written Indigenous suicide prevention only. Further exploration into informal, oral, and/or non-publicly facing initiatives would be required to create a more comprehensive picture of how Indigenous peoples are intervening on suicide in their communities.

The urgent need to reduce the disproportionately high rates of suicide in Indigenous populations of Australia, New Zealand, Canada and the United States has been widely acknowledged. Conventional Western approaches to suicide prevention by themselves have largely failed at addressing this disproportionate burden. On the other hand, initiatives that are built upon comprehensive community engagement processes and which incorporate Indigenous culture, knowledge, and decolonizing methods have been shown to have substantial impact on suicide-related outcomes in the communities in which they are employed. Indigenous approaches to suicide prevention are diverse, drawing on local culture, knowledge, need and priorities. Nevertheless, substantial barriers persist to implementing such strategies. Existing funding and health service provision systems operate from a Western, biomedical model and may be unable or unwilling to provide the resources required to invest in Indigenous approaches, highlighting the importance of evaluation to establish efficacy. On a practical level, the findings from this critical scoping review may be useful to Indigenous communities and their partners to inform their own endeavours to develop and implement suicide prevention. We hope that the outcomes presented here encourage funders, health promotion experts, and government decision-makers to support community-driven, Indigenous-approaches to suicide prevention.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors wish to thank the Métis Nation of Alberta (MNA) for their guidance in determining the objectives and questions for the review, interpretation and feedback on the themes, and supporting with identifying a reference group of Indigenous community leaders with expertise in Indigenous knowledge systems.

This work was funded by the Alberta Addiction and Mental Health Strategic Clinical Network (SCN)’s Valuing Mental Health Innovation and Integration Research grant.

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Contributions

Erynne Sjoblom refined and organized the emergent themes and was the lead in writing the manuscript. Winta Ghidei conducted the literature search, paper selection and annotation, and data extraction. Ghidei also assisted in identifying broad thematic categories and refining subthemes. Marya Leslie assisted with paper annotation and in refining thematic categories as well as offering a perspective from her experience in Indigenous community wellness program management. Ashton James led in identifying and refining thematic categories via a consensus-building process and also contributed to the review and editing of the manuscript. Reagan Bartel aided in identifying and refining thematic categories and reviewed final themes. She also provided leadership and support to the MNA team involved in the project. Stephanie Montesanti is the senior researcher and academic lead of the project. She led the funding acquisition for the study, oversaw the whole scoping review process, contributed to identifying and refining themes, and review and editing of the manuscript. Sandra Campbell designed and conducted the search strategy for the academic literature. The author(s) read and approved the final manuscript.

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Supplementary Information

Additional file 1: appendix a..

Academic Literature Search Strategy and Terms by Database.

Additional file 2: Appendix B.

Academic literature data extraction table [ 123 , 124 ].

Additional file 3:

Appendix C. Summary of grey literature articles [ 125 , 126 , 127 , 128 , 129 , 130 ].

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Sjoblom, E., Ghidei, W., Leslie, M. et al. Centering Indigenous knowledge in suicide prevention: a critical scoping review. BMC Public Health 22 , 2377 (2022). https://doi.org/10.1186/s12889-022-14580-0

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Review completed & altman, brockman to continue to lead openai.

New board members named and enhancements to the governance structure introduced 

The Special Committee of the OpenAI Board today announced the completion of the review by WilmerHale. The firm conducted dozens of interviews with members of OpenAI’s prior Board, OpenAI executives, advisors to the prior Board, and other pertinent witnesses; reviewed more than 30,000 documents; and evaluated various corporate actions. Based on the record developed by WilmerHale and following the recommendation of the Special Committee, the Board expressed its full confidence in Mr. Sam Altman and Mr. Greg Brockman’s ongoing leadership of OpenAI.

“We have unanimously concluded that Sam and Greg are the right leaders for OpenAI,” stated Bret Taylor, Chair of the OpenAI Board.

Sam Altman, as CEO, will rejoin the OpenAI Board of Directors. 

The OpenAI Board also announced today the election of three new Board members as one part of its commitment to expansion, including: 

• Dr. Sue Desmond-Hellmann , former CEO of the Bill and Melinda Gates Foundation and on the Board of Directors at Pfizer and on the President’s Council of Advisors on Science and Technology.
• Nicole Seligman , former EVP and Global General Counsel of Sony and President of Sony Entertainment and on the Board of Directors at Paramount Global, Meira GTx, and Intuitive Machines, Inc.
• Fidji Simo , CEO and Chair of Instacart and on the Board of Directors at Shopify

The new members have experience in leading global organizations and navigating complex regulatory environments, including backgrounds in technology, nonprofit and board governance. They will work closely with current board members Adam D’Angelo, Larry Summers and Bret Taylor as well as Greg, Sam, and OpenAI’s senior management. 

Taylor further stated, “As Chair of the Board, I am excited to welcome Sue, Nicole, and Fidji to the OpenAI Board of Directors. Their experience and leadership will enable the Board to oversee OpenAI’s growth and to ensure that we pursue OpenAI’s mission of ensuring artificial general intelligence benefits all of humanity.”

The Board also announced the adoption of important improvements to OpenAI’s governance structure. Key enhancements include:

• Adopting a new set of corporate governance guidelines;
• Strengthening OpenAI’s Conflict of Interest Policy;
• Creating a whistleblower hotline to serve as an anonymous reporting resource for all OpenAI employees and contractors; and
• Creating additional Board committees, including a Mission & Strategy committee focused on implementation and advancement of the core mission of OpenAI. 

The expanded board will prioritize its crucial work to enhance the governance procedures to best achieve OpenAI’s mission. “We recognize the magnitude of our role in stewarding transformative technologies for the global good,” added Taylor.

The Special Committee acknowledged the important work done by WilmerHale in conducting this extensive review and thanked OpenAI current and former Board members, advisors and employees for their cooperation. The Special Committee of OpenAI’s Board of Directors released a summary of findings.

Summary of WilmerHale review & findings

On December 8, 2023, the Special Committee retained WilmerHale to conduct a review of the events concerning the November 17, 2023 removal of Sam Altman and Greg Brockman from the OpenAI Board of Directors and Mr. Altman’s termination as CEO. WilmerHale reviewed more than 30,000 documents; conducted dozens of interviews, including of members of OpenAI’s prior Board, OpenAI executives, advisors to the prior Board, and other pertinent witnesses; and evaluated various corporate actions.  

The Special Committee provided WilmerHale with the resources and authority necessary to conduct a comprehensive review. Many OpenAI employees, as well as current and former Board members, cooperated with the review process. WilmerHale briefed the Special Committee several times on the progress and conclusions of the review.  

WilmerHale evaluated management and governance issues that had been brought to the prior Board’s attention, as well as additional issues that WilmerHale identified in the course of its review. WilmerHale found there was a breakdown in trust between the prior Board and Mr. Altman that precipitated the events of November 17.  

WilmerHale reviewed the public post issued by the prior Board on November 17 and concluded that the statement accurately recounted the prior Board’s decision and rationales. WilmerHale found that the prior Board believed at the time that its actions would mitigate internal management challenges and did not anticipate that its actions would destabilize the Company. WilmerHale also found that the prior Board’s decision did not arise out of concerns regarding product safety or security, the pace of development, OpenAI’s finances, or its statements to investors, customers, or business partners. Instead, it was a consequence of a breakdown in the relationship and loss of trust between the prior Board and Mr. Altman. WilmerHale found the prior Board implemented its decision on an abridged timeframe, without advance notice to key stakeholders, and without a full inquiry or an opportunity for Mr. Altman to address the prior Board’s concerns. WilmerHale found that the prior Board acted within its broad discretion to terminate Mr. Altman, but also found that his conduct did not mandate removal.  

After reviewing the WilmerHale findings, the Special Committee recommended to the full Board that it endorse the November 21 decision to rehire Mr. Altman and Mr. Brockman. With knowledge of the review’s findings, the Special Committee expressed its full confidence in Mr. Altman and Mr. Brockman’s ongoing leadership of OpenAI.   

The Special Committee is pleased to conclude this review and looks forward to continuing with the important work of OpenAI.