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Conceptual Framework – Types, Methodology and Examples

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Conceptual Framework

Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

  • Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
  • Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
  • Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
  • Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
  • Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
  • Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
  • Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
  • Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
  • Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
  • Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
  • Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
  • Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

  • In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
  • In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
  • In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
  • In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
  • In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

  • Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
  • Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
  • Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
  • Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
  • Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

  • Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
  • Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
  • Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
  • Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

  • To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
  • To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
  • To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
  • To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
  • To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

  • Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
  • Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
  • Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
  • Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
  • Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
  • Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

  • Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
  • Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
  • Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
  • Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
  • Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
  • Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

  • Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
  • Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
  • Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
  • Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
  • Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
  • Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

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  • What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on 4 May 2022 by Bas Swaen and Tegan George. Revised on 18 March 2024.

Conceptual-Framework-example

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualise your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

  • The expected cause, ‘hours of study’, is the independent variable (the predictor, or explanatory variable)
  • The expected effect, ‘exam score’, is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (‘hours of study’).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualising your expected cause-and-effect relationship.

Sample-conceptual-framework-using-an-independent-variable-and-a-dependent-variable

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the ‘effect’ component of the cause-and-effect relationship.

Let’s add the moderator ‘IQ’. Here, a student’s IQ level can change the effect that the variable ‘hours of study’ has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Sample-conceptual-framework-with-a-moderator-variable

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

Figure-effect-without-moderator

But the graph looks different when we add our ‘IQ’ moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Figure-effect-with-moderator-iq-120

Below, the value of the ‘IQ’ moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Figure-effect-with-moderator-iq-150

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

Conceptual-framework-mediator-variable

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

  • A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
  • A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

Conceptual-framework-control-variable

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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How to Use a Conceptual Framework for Better Research

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A conceptual framework in research is not just a tool but a vital roadmap that guides the entire research process. It integrates various theories, assumptions, and beliefs to provide a structured approach to research. By defining a conceptual framework, researchers can focus their inquiries and clarify their hypotheses, leading to more effective and meaningful research outcomes.

What is a Conceptual Framework?

A conceptual framework is essentially an analytical tool that combines concepts and sets them within an appropriate theoretical structure. It serves as a lens through which researchers view the complexities of the real world. The importance of a conceptual framework lies in its ability to serve as a guide, helping researchers to not only visualize but also systematically approach their study.

Key Components and to be Analyzed During Research

  • Theories: These are the underlying principles that guide the hypotheses and assumptions of the research.
  • Assumptions: These are the accepted truths that are not tested within the scope of the research but are essential for framing the study.
  • Beliefs: These often reflect the subjective viewpoints that may influence the interpretation of data.
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Together, these components help to define the conceptual framework that directs the research towards its ultimate goal. This structured approach not only improves clarity but also enhances the validity and reliability of the research outcomes. By using a conceptual framework, researchers can avoid common pitfalls and focus on essential variables and relationships.

For practical examples and to see how different frameworks can be applied in various research scenarios, you can Explore Conceptual Framework Examples .

Different Types of Conceptual Frameworks Used in Research

Understanding the various types of conceptual frameworks is crucial for researchers aiming to align their studies with the most effective structure. Conceptual frameworks in research vary primarily between theoretical and operational frameworks, each serving distinct purposes and suiting different research methodologies.

Theoretical vs Operational Frameworks

Theoretical frameworks are built upon existing theories and literature, providing a broad and abstract understanding of the research topic. They help in forming the basis of the study by linking the research to already established scholarly works. On the other hand, operational frameworks are more practical, focusing on how the study’s theories will be tested through specific procedures and variables.

  • Theoretical frameworks are ideal for exploratory studies and can help in understanding complex phenomena.
  • Operational frameworks suit studies requiring precise measurement and data analysis.

Choosing the Right Framework

Selecting the appropriate conceptual framework is pivotal for the success of a research project. It involves matching the research questions with the framework that best addresses the methodological needs of the study. For instance, a theoretical framework might be chosen for studies that aim to generate new theories, while an operational framework would be better suited for testing specific hypotheses.

Benefits of choosing the right framework include enhanced clarity, better alignment with research goals, and improved validity of research outcomes. Tools like Table Chart Maker can be instrumental in visually comparing the strengths and weaknesses of different frameworks, aiding in this crucial decision-making process.

Real-World Examples of Conceptual Frameworks in Research

Understanding the practical application of conceptual frameworks in research can significantly enhance the clarity and effectiveness of your studies. Here, we explore several real-world case studies that demonstrate the pivotal role of conceptual frameworks in achieving robust research conclusions.

  • Healthcare Research: In a study examining the impact of lifestyle choices on chronic diseases, researchers used a conceptual framework to link dietary habits, exercise, and genetic predispositions. This framework helped in identifying key variables and their interrelations, leading to more targeted interventions.
  • Educational Development: Educational theorists often employ conceptual frameworks to explore the dynamics between teaching methods and student learning outcomes. One notable study mapped out the influences of digital tools on learning engagement, providing insights that shaped educational policies.
  • Environmental Policy: Conceptual frameworks have been crucial in environmental research, particularly in studies on climate change adaptation. By framing the relationships between human activity, ecological changes, and policy responses, researchers have been able to propose more effective sustainability strategies.

Adapting conceptual frameworks based on evolving research data is also critical. As new information becomes available, it’s essential to revisit and adjust the framework to maintain its relevance and accuracy, ensuring that the research remains aligned with real-world conditions.

For those looking to visualize and better comprehend their research frameworks, Graphic Organizers for Conceptual Frameworks can be an invaluable tool. These organizers help in structuring and presenting research findings clearly, enhancing both the process and the presentation of your research.

Step-by-Step Guide to Creating Your Own Conceptual Framework

Creating a conceptual framework is a critical step in structuring your research to ensure clarity and focus. This guide will walk you through the process of building a robust framework, from identifying key concepts to refining your approach as your research evolves.

Building Blocks of a Conceptual Framework

  • Identify and Define Main Concepts and Variables: Start by clearly identifying the main concepts, variables, and their relationships that will form the basis of your research. This could include defining key terms and establishing the scope of your study.
  • Develop a Hypothesis or Primary Research Question: Formulate a central hypothesis or question that guides the direction of your research. This will serve as the foundation upon which your conceptual framework is built.
  • Link Theories and Concepts Logically: Connect your identified concepts and variables with existing theories to create a coherent structure. This logical linking helps in forming a strong theoretical base for your research.

Visualizing and Refining Your Framework

Using visual tools can significantly enhance the clarity and effectiveness of your conceptual framework. Decision Tree Templates for Conceptual Frameworks can be particularly useful in mapping out the relationships between variables and hypotheses.

Map Your Framework: Utilize tools like Creately’s visual canvas to diagram your framework. This visual representation helps in identifying gaps or overlaps in your framework and provides a clear overview of your research structure.

A mind map is a useful graphic organizer for writing - Graphic Organizers for Writing

Analyze and Refine: As your research progresses, continuously evaluate and refine your framework. Adjustments may be necessary as new data comes to light or as initial assumptions are challenged.

By following these steps, you can ensure that your conceptual framework is not only well-defined but also adaptable to the changing dynamics of your research.

Practical Tips for Utilizing Conceptual Frameworks in Research

Effectively utilizing a conceptual framework in research not only streamlines the process but also enhances the clarity and coherence of your findings. Here are some practical tips to maximize the use of conceptual frameworks in your research endeavors.

  • Setting Clear Research Goals: Begin by defining precise objectives that are aligned with your research questions. This clarity will guide your entire research process, ensuring that every step you take is purposeful and directly contributes to your overall study aims. \
  • Maintaining Focus and Coherence: Throughout the research, consistently refer back to your conceptual framework to maintain focus. This will help in keeping your research aligned with the initial goals and prevent deviations that could dilute the effectiveness of your findings.
  • Data Analysis and Interpretation: Use your conceptual framework as a lens through which to view and interpret data. This approach ensures that the data analysis is not only systematic but also meaningful in the context of your research objectives. For more insights, explore Research Data Analysis Methods .
  • Presenting Research Findings: When it comes time to present your findings, structure your presentation around the conceptual framework . This will help your audience understand the logical flow of your research and how each part contributes to the whole.
  • Avoiding Common Pitfalls: Be vigilant about common errors such as overcomplicating the framework or misaligning the research methods with the framework’s structure. Keeping it simple and aligned ensures that the framework effectively supports your research.

By adhering to these tips and utilizing tools like 7 Essential Visual Tools for Social Work Assessment , researchers can ensure that their conceptual frameworks are not only robust but also practically applicable in their studies.

How Creately Enhances the Creation and Use of Conceptual Frameworks

Creating a robust conceptual framework is pivotal for effective research, and Creately’s suite of visual tools offers unparalleled support in this endeavor. By leveraging Creately’s features, researchers can visualize, organize, and analyze their research frameworks more efficiently.

  • Visual Mapping of Research Plans: Creately’s infinite visual canvas allows researchers to map out their entire research plan visually. This helps in understanding the complex relationships between different research variables and theories, enhancing the clarity and effectiveness of the research process.
  • Brainstorming with Mind Maps: Using Mind Mapping Software , researchers can generate and organize ideas dynamically. Creately’s intelligent formatting helps in brainstorming sessions, making it easier to explore multiple topics or delve deeply into specific concepts.
  • Centralized Data Management: Creately enables the importation of data from multiple sources, which can be integrated into the visual research framework. This centralization aids in maintaining a cohesive and comprehensive overview of all research elements, ensuring that no critical information is overlooked.
  • Communication and Collaboration: The platform supports real-time collaboration, allowing teams to work together seamlessly, regardless of their physical location. This feature is crucial for research teams spread across different geographies, facilitating effective communication and iterative feedback throughout the research process.

Moreover, the ability t Explore Conceptual Framework Examples directly within Creately inspires researchers by providing practical templates and examples that can be customized to suit specific research needs. This not only saves time but also enhances the quality of the conceptual framework developed.

In conclusion, Creately’s tools for creating and managing conceptual frameworks are indispensable for researchers aiming to achieve clear, structured, and impactful research outcomes.

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

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  • Theoretical framework
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Selecting and developing your framework, variables in a conceptual framework.

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Conceptual framework: Definition and theory

Theoretical and conceptual frameworks ultimately go hand in hand, but while there is significant overlap with theoretical perspectives and theoretical frameworks, understanding the essential differences is important when designing your research project.

conceptual framework in research models

Let's explore the idea of a conceptual framework, provide a few common examples, and discuss how to choose a framework for your study. Keep in mind that a conceptual framework will differ from a theoretical framework and that we will explore these differences in the next section.

In this section, we'll delve into the intricacies of conceptual frameworks and their role in qualitative research . They are essentially the scaffolding on which you hang your research questions and analysis . They define the concepts that you'll study and articulate the relationships among them.

Developing conceptual frameworks in research

At the most basic level, a conceptual framework is a visual or written product that explains, either graphically or in narrative form, the main things to be studied, the key factors, variables, or constructs, and any presumed relationships among them. It acts as a road map guiding the course of your research, directing what will be studied, and helping to organize and analyze the data.

The purpose of a conceptual framework

A conceptual framework serves multiple functions in a research project. It helps in clarifying the research problem and purpose, assists in refining the research questions, and guides the data collection and analysis process. It's the tool that ties all aspects of the study together, offering a coherent perspective for the researcher and readers to understand the research more holistically.

Relation between theoretical perspectives and conceptual frameworks

Theoretical perspectives offer overarching philosophies and assumptions that guide the research process, while conceptual frameworks are the specific devices that are derived from these perspectives to operationalize the study. If a theoretical perspective is the broad philosophical underpinning, a conceptual framework is a pragmatic approach that puts that philosophy into practice in the context of the study.

For instance, if you're working from a feminist theoretical perspective, your conceptual framework might involve specific constructs like gender roles, power dynamics , and societal norms, as well as the relationships between these constructs. The conceptual framework would be the lens through which you examine and interpret your data, guided by your theoretical perspective.

conceptual framework in research models

Critical theory

Critical theory is a theoretical perspective that seeks to confront social, historical, and ideological forces and structures that produce and constrain social problems. The corresponding conceptual framework might focus on constructs like power relations, historical context, and societal structures. For instance, a study on income inequality might have a conceptual framework involving constructs of socioeconomic status, institutional policies, and the distribution of resources.

Feminist theory

Feminist theory emphasizes the societal roles of gender and power relationships. A conceptual framework derived from this theory might involve constructs like gender roles, power dynamics, and societal norms. In a study about gender representation in media, a feminist conceptual framework could involve constructs such as stereotyping, representation, and societal expectations of gender.

conceptual framework in research models

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Choosing and developing your conceptual framework is a pivotal process in your research design. This framework will help guide your study, inform your methodology , and shape your analysis .

Factors to consider when choosing a framework

Your conceptual framework should be derived from and align with your chosen theoretical perspective , but there are other considerations as well. It should resonate with your research question , problem, or purpose and be applicable to the specific context or population you are studying. You should also consider the feasibility of operationalizing the constructs in your framework.

When selecting a conceptual framework, consider the following questions:

1. How does this framework relate to my research topic? 2. Can I use this framework to effectively address my research question(s)? 3. Does this framework resonate with the population and context I'm studying? 4. Can the constructs in this framework be feasibly operationalized in my study?

Steps in developing a conceptual framework

Developing your conceptual framework involves a few key steps:

1. Identify key constructs: Based on your theoretical perspective and research question(s) , what are the main constructs or variables that you need to explore in your study? 2. Clarify relationships among constructs: How do these constructs relate to each other? Are there presumed causal relationships, correlations, or other types of associations? 3. Define each construct: Clearly define what each construct means in the context of your study. This might also involve operationalizing each construct or defining the indicators you will use to measure or identify each construct. 4. Create a visual representation : It is often extremely helpful to create a visual representation of your conceptual framework to illustrate the constructs and their relationships. Map out the relationships among constructs to develop a holistic understanding of what you want to study.

conceptual framework in research models

Remember, your conceptual framework is not set in stone. You can start creating your conceptual framework based on your literature review and your own critical reflections. As you proceed with your study, you might need to refine or adapt your conceptual framework based on what you're learning from your data. Developing a robust framework is an iterative process that requires critical thinking, creativity, and flexibility.

A strong conceptual framework includes variables that refer to the constructs or characteristics that are being studied. They are the building blocks of your research study. It might be helpful to think about how the variables in your conceptual framework could be categorized as independent and dependent variables, which respectively influence and are influenced within the research study.

Independent variables and dependent variables

An independent variable is the characteristic or condition that is manipulated or selected by the researcher to determine its effect on the dependent variable. For example, in a study exploring the impact of classroom size on student engagement, classroom size would be the independent variable.

The dependent variable is the main outcome that the researcher is interested in studying or explaining. In the example given above, student engagement would be the dependent variable, as it's the outcome being observed for any changes in response to the independent variable (classroom size). In essence, defining these variables can help you identify the cause-and-effect relationships in your study. While it might be difficult to know beforehand exactly which variables will be important and how they relate to one another, this is a helpful thought exercise to flesh out potential relationships among variables you may want to study.

Relationships among variables

Within a conceptual framework, the dependent and independent variables are listed in addition to their proposed relationships to each other. The ways in which these variables influence one another form the crux of the propositions or assumptions that guide your research.

In a conceptual framework based on the theoretical perspective of constructivism, for instance, the independent variable might be a teaching method (as constructivists would argue that methods of instruction can shape learning), and the dependent variable could be the depth of student understanding. The proposed relationship between these variables might be that student-centered teaching methods lead to a deeper understanding, which would guide the data collection and analysis such that this proposition could be explored.

However, it is important to note that the terminology of independent and dependent variables is more typical of quantitative research , in which independent and dependent variables are operationalized in hypotheses that will be tested based on pre-established theory. In qualitative research , the relationships between variables are more fluid and open-ended because the focus is often more on understanding the phenomenon as a whole and building a contextualized understanding of the research problem. This can involve including new or unexpected variables and interrelationships that emerge during the study, thus extending previous theory or understanding that didn’t initially predict these relationships.

Thus, in your conceptual framework, rather than solely focusing on identifying independent and dependent variables, consider how various factors interact and influence one another within the context of your study. Your conceptual framework should provide a holistic picture of the complexity of the phenomenon you are studying.

conceptual framework in research models

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What is a Conceptual Framework?

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format.

Updated on August 28, 2023

a researcher putting together their conceptual framework for a manuscript

What are frameworks in research?

Both theoretical and conceptual frameworks have a significant role in research.  Frameworks are essential to bridge the gaps in research. They aid in clearly setting the goals, priorities, relationship between variables. Frameworks in research particularly help in chalking clear process details.

Theoretical frameworks largely work at the time when a theoretical roadmap has been laid about a certain topic and the research being undertaken by the researcher, carefully analyzes it, and works on similar lines to attain successful results. 

It varies from a conceptual framework in terms of the preliminary work required to construct it. Though a conceptual framework is part of the theoretical framework in a larger sense, yet there are variations between them.

The following sections delve deeper into the characteristics of conceptual frameworks. This article will provide insight into constructing a concise, complete, and research-friendly conceptual framework for your project.

Definition of a conceptual framework

True research begins with setting empirical goals. Goals aid in presenting successful answers to the research questions at hand. It delineates a process wherein different aspects of the research are reflected upon, and coherence is established among them. 

A conceptual framework is an underrated methodological approach that should be paid attention to before embarking on a research journey in any field, be it science, finance, history, psychology, etc. 

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format. Your conceptual framework establishes a link between the dependent and independent variables, factors, and other ideologies affecting the structure of your research.

A critical facet a conceptual framework unveils is the relationship the researchers have with their research. It closely highlights the factors that play an instrumental role in decision-making, variable selection, data collection, assessment of results, and formulation of new theories.

Consequently, if you, the researcher, are at the forefront of your research battlefield, your conceptual framework is the most powerful arsenal in your pocket.

What should be included in a conceptual framework?

A conceptual framework includes the key process parameters, defining variables, and cause-and-effect relationships. To add to this, the primary focus while developing a conceptual framework should remain on the quality of questions being raised and addressed through the framework. This will not only ease the process of initiation, but also enable you to draw meaningful conclusions from the same. 

A practical and advantageous approach involves selecting models and analyzing literature that is unconventional and not directly related to the topic. This helps the researcher design an illustrative framework that is multidisciplinary and simultaneously looks at a diverse range of phenomena. It also emboldens the roots of exploratory research. 

the components of a conceptual framework

Fig. 1: Components of a conceptual framework

How to make a conceptual framework

The successful design of a conceptual framework includes:

  • Selecting the appropriate research questions
  • Defining the process variables (dependent, independent, and others)
  • Determining the cause-and-effect relationships

This analytical tool begins with defining the most suitable set of questions that the research wishes to answer upon its conclusion. Following this, the different variety of variables is categorized. Lastly, the collected data is subjected to rigorous data analysis. Final results are compiled to establish links between the variables. 

The variables drawn inside frames impact the overall quality of the research. If the framework involves arrows, it suggests correlational linkages among the variables. Lines, on the other hand, suggest that no significant correlation exists among them. Henceforth, the utilization of lines and arrows should be done taking into cognizance the meaning they both imply.

Example of a conceptual framework

To provide an idea about a conceptual framework, let’s examine the example of drug development research. 

Say a new drug moiety A has to be launched in the market. For that, the baseline research begins with selecting the appropriate drug molecule. This is important because it:

  • Provides the data for molecular docking studies to identify suitable target proteins
  • Performs in vitro (a process taking place outside a living organism) and in vivo (a process taking place inside a living organism) analyzes

This assists in the screening of the molecules and a final selection leading to the most suitable target molecule. In this case, the choice of the drug molecule is an independent variable whereas, all the others, targets from molecular docking studies, and results from in vitro and in vivo analyses are dependent variables.

The outcomes revealed by the studies might be coherent or incoherent with the literature. In any case, an accurately designed conceptual framework will efficiently establish the cause-and-effect relationship and explain both perspectives satisfactorily.

If A has been chosen to be launched in the market, the conceptual framework will point towards the factors that have led to its selection. If A does not make it to the market, the key elements which did not work in its favor can be pinpointed by an accurate analysis of the conceptual framework.

an example of a conceptual framework

Fig. 2: Concise example of a conceptual framework

Important takeaways

While conceptual frameworks are a great way of designing the research protocol, they might consist of some unforeseen loopholes. A review of the literature can sometimes provide a false impression of the collection of work done worldwide while in actuality, there might be research that is being undertaken on the same topic but is still under publication or review. Strong conceptual frameworks, therefore, are designed when all these aspects are taken into consideration and the researchers indulge in discussions with others working on similar grounds of research.

Conceptual frameworks may also sometimes lead to collecting and reviewing data that is not so relevant to the current research topic. The researchers must always be on the lookout for studies that are highly relevant to their topic of work and will be of impact if taken into consideration. 

Another common practice associated with conceptual frameworks is their classification as merely descriptive qualitative tools and not actually a concrete build-up of ideas and critically analyzed literature and data which it is, in reality. Ideal conceptual frameworks always bring out their own set of new ideas after analysis of literature rather than simply depending on facts being already reported by other research groups.

So, the next time you set out to construct your conceptual framework or improvise on your previous one, be wary that concepts for your research are ideas that need to be worked upon. They are not simply a collection of literature from the previous research.

Final thoughts

Research is witnessing a boom in the methodical approaches being applied to it nowadays. In contrast to conventional research, researchers today are always looking for better techniques and methods to improve the quality of their research. 

We strongly believe in the ideals of research that are not merely academic, but all-inclusive. We strongly encourage all our readers and researchers to do work that impacts society. Designing strong conceptual frameworks is an integral part of the process. It gives headway for systematic, empirical, and fruitful research.

Vridhi Sachdeva, MPharm Bachelor of PharmacyGuru Nanak Dev University, Amritsar

Vridhi Sachdeva, MPharm

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Development of Conceptual Models to Guide Public Health Research, Practice, and Policy: Synthesizing Traditional and Contemporary Paradigms

Sonya s. brady.

Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, 55454, USA

Linda Brubaker

Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, 92037, USA

Cynthia S. Fok

Department of Urology, University of Minnesota Medical School, Minneapolis, MN, 55454, USA

Sheila Gahagan

Division of Academic General Pediatrics, University of California San Diego, San Diego, CA, 92093, USA

Cora E. Lewis

Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

Jessica Lewis

Yale School of Public Health, New Haven, CT, 06520, USA

Jerry L. Lowder

Division of Female Pelvic Medicine and Reconstructive Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA

Jesse Nodora

Department of Family Medicine and Public Health and Moores UC San Diego Cancer Center, University of California San Diego, La Jolla, CA, 92161, USA

Ann Stapleton

Department of Medicine, University of Washington, Seattle, WA, 98195, USA

Mary H. Palmer

School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

This applied paper is intended to serve as a “how to” guide for public health researchers, practitioners, and policy makers who are interested in building conceptual models to convey their ideas to diverse audiences. Conceptual models can provide a visual representation of specific research questions. They also can show key components of programs, practices, and policies designed to promote health. Conceptual models may provide improved guidance for prevention and intervention efforts if they are based on frameworks that integrate social ecological and biological influences on health and incorporate health equity and social justice principles. To enhance understanding and utilization of this guide, we provide examples of conceptual models developed by the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium. PLUS is a transdisciplinary U.S. scientific network established by the National Institutes of Health in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Research Consortium is developing conceptual models to guide its prevention research agenda. Research findings may in turn influence future public health practices and policies. This guide can assist others in framing diverse public health and prevention science issues in innovative, potentially transformative ways.

Public health and prevention science students, researchers, practitioners, and policy makers all stand to benefit by becoming skilled in the development of conceptual models. Over 25 years ago, Jo Anne Earp and Susan Ennett (1991) described how a conceptual model could be used to depict the mechanisms by which a selected set of risk and protective factors may be associated with a health behavior or outcome of interest, as well as the conditions under which such associations are typically observed. This work demonstrated how conceptual models can be used to provide a visual representation of specific research questions and display the key components of prevention and intervention programs, practices, and policies designed to promote health. Since Earp and Ennett’s contribution, many publications that can be used to generate conceptual models have been introduced to the public health sphere. These writings describe frameworks that integrate social ecological and biological influences on health and highlight the potential for health equity and social justice principles to guide public health research, practice, and policy. By integrating diverse perspectives, those who design conceptual models can consider a wide range of factors that may influence health. A better understanding of what influences health can lead to the development of more effective health promotion programs, practices, and policies, as well as more efficient use of limited public health resources. Conceptual model development is an increasingly valued skill. For example, the National Institutes of Health have called for the inclusion of conceptual models when teams of researchers and practitioners respond to specific requests for proposals to conduct research on health promotion, including mental health (RFA-MH-18-705), bladder health (RFA-DK-19-015), and shared decision-making between patients and providers (PA-16-424; NIH, n.d. ).

This paper is intended to serve as a contemporary guide for building conceptual models. It is consistent with the mission of Health Promotion Practice to publish practical tools that advance the science and art of health promotion and disease prevention, particularly with respect to achieving health equity, addressing social determinants of health, and advancing evidence-based health promotion practice. To enhance understanding, examples of conceptual model development are provided from the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium, a transdisciplinary scientific network established by the National Institute of Diabetes and Digestive and Kidney Diseases in 2015 to study bladder health and prevention of lower urinary tract symptoms (LUTS) in girls and women ( Harlow et al., 2018 ). LUTS encompass a variety of bothersome bladder symptoms, including urgency urinary incontinence (i.e., strong urge “to go” with urine loss before reaching a toilet), stress urinary incontinence (i.e., urine loss with physical activity or increases in abdominal pressure such as a cough or sneeze), bothersome frequent and/or urgent urination, nocturnal enuresis (i.e., bed-wetting), difficulty urinating, dribbling after urination, and bladder or urethral pain before, during, or after urination ( Abrams et al., 2010 ; Haylen et al., 2010). LUTS are common. For example, more than 200 million people worldwide and over 15% of women aged 40 years or older experience urinary incontinence, one of the most prevalent LUTS ( Minassian, Bazi, & Stewart, 2017 ; Norton & Brubaker, 2006 ).

While many multidisciplinary research networks focus on clinical treatment of LUTS, the PLUS Consortium stands alone in its focus on bladder health promotion and prevention of LUTS. Consistent with the World Health Organization’s (WHO) definition of health (WHO, 2006), the PLUS Consortium conceptualizes bladder health as “a complete state of physical, mental, and social well-being related to bladder function, and not merely the absence of LUTS,” with function that “permits daily activities, adapts to short term physical or environmental stressors, and allows optimal well-being (e.g., travel; exercise; social, occupational, or other activities)” ( Lukacz et al., 2018 ).

Conceptual models are different from other tools and concepts.

Table 1 highlights the distinction between conceptual models and closely related visual tools and concepts. The contrast between conceptual frameworks and conceptual models is of particular relevance to the present guide. A research-oriented conceptual framework encapsulates what is possible to study and is intentionally comprehensive; in contrast, a research-oriented conceptual model encapsulates what a team has prioritized and chosen to study and is intentionally focused in scope ( Earp & Ennett, 1991 ; Brady et al., 2018 ). Similarly, conceptual frameworks and models may depict the “universe” and selected focus, respectively, of public health practices and policies. The contrast between a theory and conceptual model is also of particular relevance to the present guide. While both theories and conceptual models describe associations among constructs in order to explain or predict outcomes, a theory is intentionally broad with respect to application. It can guide the development of one or more conceptual models to address a specific public health behavior or outcome. While a review of prominent theories is beyond the scope of this paper, several public health textbooks provide an overview of theories that may be used to guide etiologic research and health promotion programs, practices, and policies (e.g., DiClemente, Salazar, & Crosby, 2019 ; Edberg, 2015 ; Glanz, Rimer, & Viswanath, 2015 ; Simons-Morton, McLeroy, & Wedndel, 2012 ).

Distinctions between conceptual models and other visual tools and concepts used in public health and related disciplines.

Traditional and contemporary conceptualizations of public health can identify a broad range of factors that may function as determinants of health.

Traditional conceptual frameworks include social ecological and biopsychosocial models. Social ecological models , a foundation of public health approaches for more than 40 years ( McLeroy, Bibeau, Steckler, & Glanz, 1988 ; Sallis & Owen, 2015 ; Richard, Gauvin, & Raine, 2011 ), situate individuals within an ecosystem of risk and protective factors that extend outward from the intrapersonal level (e.g., biology, psychology) through the interpersonal (e.g., family, peers, partner), institutional (e.g., school, workplace, health clinic), community (e.g., cultural norms), and societal (e.g., policies, laws, economics) levels. These nested spheres of influence interact to produce individual and population health. Similarly, the biopsychosocial model posits that health is defined by a complex reciprocal interaction of biological, psychological, and social factors ( Engel, 1981 ). Given the focus of this paper, we note that both social ecological and biopsychosocial models are more consistent with the definition of a conceptual framework than a conceptual model (see Table 1 ).

Contemporary conceptualizations of public health enhance traditional frameworks by more explicitly integrating biology and social ecology, adopting life course perspectives, and incorporating health equity, social justice, and community engagement principles to guide research, practice, and policy. The Society-Behavior-Biology Nexus depicts nested spheres of influences both within and outside of an individual, who moves through life stages from infancy to old age ( Glass & McAtee, 2006 ). Systems of biological organization include multi-organ systems, cellular and molecular influences, and the genomic substrate. Levels of ecology include the micro (e.g., family, social networks), mezzo (e.g., schools, worksites, communities, healthcare systems), macro (e.g., states, nations), and global (e.g., geopolitics, environment). Biology and social ecology are integrated through the multi-level concept of embodiment (e.g., gene-environment interactions; impact of varying social-ecological resources on biology within and across populations) ( Glass & McAtee, 2006 ; Krieger, 2005 ). Social determinants are framed as societal constraints against and opportunities for health – risk regulators – which include material conditions; discriminatory practices, policies, and attitudes; neighborhood and community conditions; behavioral norms, rules, and expectations; conditions of work; and laws, policies, and regulations. Risk regulators can impact behavior or become embodied with respect to biological function ( Glass & McAtee, 2006 ; Krieger, 2005 ).

The WHO Conceptual Framework for Action on Social Determinants of Health describes how the structure of societies (i.e., governance, policies, values) determines population health ( Solar & Irwin, 2010 ). Social stratification by race, ethnicity, sex, gender, social class, and other factors leads to social hierarchies, which in turn shape social determinants of health. Distal structural determinants of health inequities (e.g., public policy, macroeconomics) are distinguished from more proximal social determinants of health (e.g., living and working conditions). The WHO framework asserts that societies produce health and disease, obligating policy makers to promote health equity and redress structural factors that produce under-resourced communities. Without such attention, health inequities evolve, often widening over time and across generations. The WHO framework can inform conceptual model development by encouraging the consideration of determinants at distal, structural levels (e.g., national policies).

Research teams have utilized contemporary conceptualizations of public health to promote health equity and social justice ( Warnecke et al., 2008 ; Balazs & Ray, 2014 ). For example, the National Institutes of Health (NIH) sponsored Centers for Population Health and Health Disparities developed a framework to show how distal factors (population-level policies and social conditions, institutional contexts) influence intermediate social context (e.g., collective efficacy, social capital), social relationships (e.g., networks, support, and influence), and physical context (e.g., building quality, neighborhood stability), which in turn influence factors that are more proximal to health (individual demographics and risk behaviors, biologic responses and pathways) ( Warnecke et al., 2008 ). The Energy and Resources Group at the University of California, Berkeley developed a framework to display mechanisms through which natural, built, and sociopolitical factors, along with state, county, and community actors, can create drinking water disparities ( Balazs & Ray, 2014 ). These frameworks highlight the key role of distal structural factors in both generating health inequities and remedying them.

Community partners can aid in developing conceptual models.

Increasingly, teams are incorporating community-engaged approaches in the development of research, practice, and policy (e.g., community members actively contributing to problem definition, agenda setting, implementation, and dissemination) ( Warnecke et al., 2008 ; O’Mara-Eves et al., 2013 ). Different resources exist to guide community engagement and enhance the likelihood of sustained, relevant action. For example, Lezine and Reed (2007) outlined different steps to build and apply political will in the development and implementation of public health policy; their approach integrates scientific evidence and community participation. Cacari-Stone and colleagues (2014) developed a conceptual model to show how community-based participatory research (CBPR), one approach to community engagement, can lead to policy change.

Three Steps of Conceptual Model Development.

The development of conceptual models can be divided into three basic steps: (1) identify resources for idea generation; (2) consider risk and protective factors; and (3) select factors for inclusion in the conceptual model. First, team members identify existing conceptual frameworks and models, theories, and key stakeholders (e.g., practitioners, policy makers, community members) that will serve as resources for idea generation. This step defines the “universe” of factors that can be studied in relation to specific health behaviors or outcomes of interest. Second, team members systematically consider risk and protective factors suggested by resources. This step highlights the importance of carefully selecting resources for idea generation; the risk and protective factors considered by a team will be constrained by its selected frameworks and models, theories, and stakeholders. Existing evidence linking risk and protective factors to the health behaviors or outcomes under study, as well as potential effect modifiers and confounders, can be identified through literature reviews. When data are insufficient, a team may wish to conduct key stakeholder interviews, focus groups, and other forms of hypothesis-generating data collection. The third step in the development of conceptual models is to narrow down considered risk and protective factors to those that will be included in the conceptual model. This can be achieved through a combination of theoretically-based, key stakeholder-based, and evidence-based rationales. Theories point to clusters of risk and protective factors that could be studied in relation to health behaviors or outcomes of interest, or targeted through prevention or intervention efforts. Key stakeholders can assess the relevance of different theories to a given public health context and suggest additional risk and protective factors that seem critical to the context. Findings from the extant literature can provide evidence in support of different links in the conceptual model.

If the intent of building a conceptual model is to develop an evidence-based program, practice, or policy, a team can conduct a literature review to answer the following “narrowing down” questions: (a) Is the risk or protective factor strongly linked to the health behavior or outcome of interest? (b) Have previous prevention or intervention programs, practices, or policies shown that the risk or protective factor is feasible to modify? (c) Was health improved as a result of modifying the risk or protective factor? Risk and protective factors can be retained in the conceptual model if they are strongly supported by evidence and judged highly relevant to context.

When the intent of building a conceptual model is to conduct research to better understand a health behavior or outcome, a team may choose to consult existing theories, key stakeholders, and the evidence-base for guidance in selecting risk and protective factors. To maximize potential public health impact, a team can answer the following “narrowing down” question: What potential risk and protective factors are judged to be highly likely to influence health behaviors or outcomes of interest? Ideally, the answers to public health research questions will expand the evidence base in a way that can directly inform programs, practices, and policies. Expansion of the evidence-base can be accomplished in a variety of potentially transformative ways, including the synthesis of ideas from more than one discipline and the application of paradigms from one discipline to another.

Regardless of the approach and rationale used to select risk and protective factors, the utility of the conceptual model may be enhanced by answering the final three sets of questions: (a) Have key “mechanistic factors” been considered and included in the model? What biological, psychological, and social processes might explain links between identified risk and protective factors and health behaviors or outcomes of interest? (b) Have key “upstream factors” been considered and included in the model? For example, are there societal and institutional policies and practices that serve as facilitators or barriers to health? (c) Have key “effect modifiers” been considered and included in the model? For example, are there factors that might make prevention or intervention programs, practices, or policies more or less effective among specific communities and populations?

Examples from the PLUS Research Consortium.

The PLUS Consortium is comprised of a transdisciplinary network of professionals, including community advocates, health care professionals, and scientists specializing in pediatrics, adolescent medicine, gerontology and geriatrics, nursing, midwifery, behavioral medicine, preventive medicine, psychiatry, neuroendocrinology, reproductive medicine, female pelvic medicine and reconstructive surgery, urology, infectious diseases, clinical and social epidemiology, prevention science, medical sociology, psychology, women’s studies, sexual and gender minority health, community-engaged research, community health promotion, scale development, research methods, and biostatistics. The PLUS Consortium has developed several conceptual models to guide research questions that will test whether specific risk and protective factors contribute to LUTS and bladder health.

Because the evidence-base for LUTS prevention is sparse, the traditional and contemporary conceptualizations of public health reviewed above, as well as expertise of PLUS investigators, were used as key resources to identify potential risk and protective factors for study (Step 1). Traditional and contemporary conceptualizations of public health encouraged consortium members to step outside of their disciplinary “comfort zones” to integrate social ecological and biological influences on health across the life course and consider the potential for health equity and social justice principles to guide the consortium’s prevention research agenda. While all of the conceptualizations reviewed above were considered, Glass and McAtee’s Society-Behavior-Biology Nexus was particularly influential because it visually represented different levels of social ecology and biology across the life course, as well as the process of embodiment. PLUS members served as an initial key stakeholder group that generated a conceptual framework and over 400 risk and protective factors prioritized for study in relation to bladder health and LUTS (Step 2) ( Brady et al., 2018 ). The conceptual models presented in this paper represent the work of subsets of consortium members who designed models to guide specific research questions (Step 3). Models were designed with the assistance of public health and prevention science team members who were familiar with social ecological frameworks and the development of conceptual models. Initial development of models occurred in real time during in-person and virtual (WebEx) meetings. This was often followed by revision of models via emailed chains of conversation. One person with experience in conceptual model development was responsible for integrating and communicating comments and mutual decisions, as well as revising the models.

Each conceptual model featured in this paper represents hypothesized associations between constructs; some links in each model are supported by existing evidence, while others are based on theoretical or biological plausibility. Figure 1 highlights institutional-level factors in relation to bladder health and LUTS, while Figure 2 highlights family- and community-level factors and Figure 3 highlights societal and commercial factors.

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Work-related structural and social influences on musculoskeletal function and bladder health: Hypothesized mechanisms.

Explanation of Pathways: Four different work-related factors (shaded boxes) affect different aspects of musculoskeletal function, which in turn affect bladder health and LUTS. Workplace physical and psychological demands directly affect musculoskeletal function. Workplace ergonomics and travel/commute patterns indirectly affect musculoskeletal function through prolonged sitting or standing and posture (mediation pathways).

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Trajectories of risk and resilience among individuals and communities exposed to ACEs and traumatic stressors: Hypothesized mechanisms.

Explanation of Pathways: Executive functioning difficulties and central nervous system dysregulation are shown in a single, partitioned box because these constructs are hypothesized to covary in their manifestation. Direct effects between two adjacent constructs are shown by solid lines (1a, 2a, 3a, 4, 5); effect modification by resources for resilience (shaded box) is shown by dashed lines (1b, 2b, 3b). ADHD: Attention-Deficit/Hyperactivity Disorder.

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Societal and commercial influences on bladder health and LUTS: Hypothesized mechanisms involving fast food and soda.

Explanation of Pathways: This conceptual model highlights hypothesized mechanisms (mediators) that can explain associations between societal and commercial factors (shaded boxes) and bladder health and LUTS. This model can guide a set of statistical analyses that require the identification of predictor, mediating, and outcome variables. The model does not reflect the full complexity of associations that likely exist among constructs (e.g., bi-directional associations, feedback loops; see Systems Model entry in Table 1 ).

Figure 1 depicts a basic conceptual model showing how specific work-related structural and social factors may influence musculoskeletal function, which in turn may impact bladder health and LUTS development. Four key aspects of musculoskeletal dysfunction are overuse injury, strain, pain, and weakness (see center-right of Figure 1 ), which may be directly and indirectly influenced by work-related factors. The top, bottom, and left-most boxes depict work-related factors that are external to the individual and arguably imposed by society and institutions. Workplace physical and psychological demands are shown to directly impact musculoskeletal function. Workplace physical demands (e.g., repetitive heavy lifting) may result in musculoskeletal dysfunction, which in turn may lead to LUTS ( Park & Palmer, 2015 ). In addition, workplace psychological demands (e.g., job performance pressures, conflict with coworkers, inequitable expectations and evaluations of work) may be accompanied by stress, anxiety, and other forms of negative affect ( Larsman, Kadefors, & Sandsjö, 2013 ), which may lead to chronically increased pelvic floor muscle dysfunction and LUTS ( van der Velde, Laan, & Everaerd, 2001 ). Workplace ergonomics (e.g., improper chair or desk height) and travel/commute patterns (e.g., daily, long commutes and long airplane flights) may indirectly impact musculoskeletal dysfunction through prolonged sitting or standing and poor posture ( Barone Gibbs et al., 2018 ).

Additional research is needed to support hypothesized associations in Figure 1 , which are based in large part on the authors’ clinical and community-based observations. If different links are supported, corresponding workplace policies and practices can be promoted to ensure that physical demands are offset by varying the type and intensity of activity and providing breaks; psychological demands are fair, reasonable, and offset by supports; and workplace ergonomics are conducive to the health of all employees, regardless of status within the organization. In addition, local and state governments can support policies and practices that ensure adequate access to acceptable bathroom facilities along transportation routes and when possible, within public transportation conveyances.

Figure 2 shows an example of a more complex conceptual model. A trajectory of risk among individuals or communities exposed to adverse childhood experiences (ACEs) (e.g., abuse, neglect, household disruptions) (Felitti et al., 1998) and other traumatic stressors can be seen by following the solid lines from left to right. ACEs and traumatic stressors indirectly affect local dysregulation through two potential pathways: (I) development of executive functioning difficulties and central nervous system dysregulation (shown by 1a links) ( Nusslock & Miller, 2016 ; Smith et al., 2016 ), which in turn lead to local dysregulation (shown by link 4) ( Kanter et al., 2016 ); and (II) development of depression, anxiety, and ADHD symptoms (shown by 2a links), which in turn lead to executive functioning difficulties and central nervous system dysregulation (shown by link 3a) ( Nusslock & Miller, 2016 ), which then leads to local dysregulation (shown by link 4) ( Kanter et al., 2016 ; Yousefichaijan, Sharafkhah, Rafiei, & Salehi, 2016 ). Constructs that explain associations between stressful life circumstances and LUTS may collectively be thought of as a “chain of mediation,” in that they lie along a hypothesized causal, sequential pathway. Figure 2 also shows how a trajectory of risk/chain of mediation may be weakened or broken at different points along the pathway. The dashed lines of Figure 2 show modification of effects (“effect modification”) by resources for resilience (i.e., coping, social support). Effects of stressful life circumstances on LUTS are weakened in the presence of resources for resilience (shown by the dashed lines 1b, 2b, and 3b).

Although several of the links in Figure 2 are supported by evidence, additional research is needed. Figure 2 illustrates the importance of structural factors that stratify the citizens of a society into communities that are more or less likely to experience adverse childhood experiences and traumatic stressors, and have more or less opportunities to garner resources for resilience ( Glass & McAtee, 2006 ; Solar & Irwin, 2010 ; Warnecke et al., 2008 ). Policies attempting to ensure equitable allocation of resources, including but not limited to health care, are essential to preventing and weakening trajectories of risk that disproportionately impact under-resourced communities and families.

Figure 3 , our final example, highlights broader, societal and commercial influences on bladder health and LUTS, along with environmental, behavioral, and biological mechanisms specific to fast food and soda consumption. Consistent with the WHO Conceptual Framework for Action on Social Determinants of Health ( Solar & Irwin, 2010 ), Figure 3 begins with societal structures. Governance and policies shape the built environments of communities, in part through zoning of fast food restaurants, convenience stores, grocery stores, and farmers markets; these, in turn, impact the availability of fast food and soda in communities ( Sallis & Glanz, 2009 ). Additional policies can impact the affordability of fast food and soda relative to healthy products (e.g., taxation of sugar-sweetened beverages; subsidies for fresh produce) ( Franck, Grandi, & Eisenberg, 2013 ), as well as the advertising and marketing of fast food and beverages, especially towards children ( Harris et al., 2015 ). Low-income communities of color in the United States have historically received fewer resources as a result of inequitable policies; they have also been targeted by the fast food and soda industries ( Sallis & Glanz, 2009 ; Harris et al., 2015 ).

Availability, relative affordability, advertising, and marketing of fast food and soda within a community increase the likelihood that residents will consume “super-sized” food portions and soda, which contributes to obesity ( Sallis & Glanz, 2009 ; Harris et al., 2015 ). Obesity may directly impact LUTS by intra-abdominal pressure on the bladder ( Bavendam et al., 2016 ); it may also impact LUTS through diabetes-related mechanisms, including neurogenic bladder and urinary tract infections ( Bavendam et al., 2016 ; Podnar & Vodusek, 2015 ). Diet soda, which many individuals embrace as a means to reduce caloric intake and combat obesity, contains components that may increase urine volume (caffeine) and harm the health of the bladder lining (artificial sweeteners, carbonation/acidity) (Robinson, Hanna-Mitchell, Rantell, Thiagamoorthy, & Cardozo, 2015). A healthy bladder may be maintained or restored by healthy food and beverage choices; Figure 3 highlights constraints on healthy choices that are determined by upstream, societal factors.

Because the PLUS Research Consortium is just beginning its prevention research agenda, its current models are intended to guide etiologic research, as opposed to selection, implementation, and evaluation of health promotion and prevention strategies. Broader planning frameworks exist for this purpose, including PRECEDE-PROCEED and intervention mapping ( Bartholomew, Markham, Mullen, & Fernández, 2015 ; Bartholomew, Parcel, & Kok, 1998 ; Green & Kreuter, 2005 ), the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Strategic Prevention Framework (2017) , and the Center for Disease Control and Prevention’s (CDC) Framework for Program Evaluation in Public Health (1999) . These frameworks not only guide practitioners in assessing risk and protective factors at different levels of social ecology that may influence health, but also provide a structure for applying theories and conceptual models to the planning and evaluation of health promotion programs, practices, and policies. The PLUS Research Consortium will utilize existing planning frameworks when its work progresses to the point of designing, implementing, and evaluating bladder health promotion and LUTS prevention strategies through research.

Lessons Learned and Recommendations for Other Conceptual Model Development Teams.

After developing the conceptual models and supporting materials presented in this paper, authors reflected on lessons they had learned and what they would recommend to other teams.

Recommendation 1: Develop a shared language.

Students, researchers, practitioners, and policy makers interested in developing conceptual models may benefit from reviewing the terms in Table 1 , determining what is consistent with and distinct from their own discipline and training, and identifying additional tools and concepts that could aid in conceptual model development. Few of this paper’s authors were initially familiar with all of the visual tools and related concepts defined in Table 1 . Terms were added not only by authors, but also by other PLUS Consortium members (e.g., epidemiologists recommended the inclusion of “directed acyclic graph” and “systems model”). Teams who are developing conceptual models may develop a shared language through the process of reviewing, adding, and defining terms.

Recommendation 2: Establish a conceptual framework before developing a conceptual model.

Authors appreciated the distinction between conceptual frameworks and models, particularly with respect to how a framework could be a starting point to broaden one’s conceptualization of health beyond one’s own disciplinary training. Consortium members valued the integration of social ecological, behavioral, and biological perspectives of what influences health, as well as the opportunity to incorporate multiple levels of influence into a single conceptual model and corresponding set of research questions. Consortium members appreciated how the creation and refinement of conceptual models could then assist in clarifying specific research questions; identifying potential pathways through which different risk and protective factors may influence a health outcome; examining and challenging one’s own disciplinary assumptions; and articulating what is known or speculative with respect to the factors that influence health.

Recommendation 3: Seek to develop a diverse team and solicit input from others.

Authors appreciated how steps of conceptual model development included the consideration of how community partners and other key stakeholders can become involved in the process of development. By design, the PLUS Research Consortium includes community advocates, community-engaged researchers, and health care professionals and scientists representing a broad array of disciplines. Authors did not reach beyond the PLUS Consortium to develop the conceptual models featured in this paper, in part because the present paper was intended to describe the process of conceptual model development, rather than to present definitive models. Other conceptual model development teams may benefit from soliciting the input of individuals who are not well represented on their team, including community members, researchers, practitioners, and policy makers.

Recommendation 4: Anticipate and embrace the iterative, “trial and error” nature of conceptual model development.

Early in the process of developing conceptual models, authors developed a shared understanding that it was not necessary for all proposed links in a conceptual model to be informed by existing evidence. Theory, clinical observations, and the lived experience of community members are valid sources of information, as well. Authors also came to appreciate that it was not necessary to develop the “perfect” model during a first attempt to understand a health behavior or outcome, or to select the key components of an evidence-based program, practice, or policy. Indeed, attempting to achieve perfection may stifle creativity and innovation. The conceptual models presented in this paper were developed iteratively, both within the team of authors and consortium members who assisted in their development (see Acknowledgements ). Conceptual models should be evaluated through research, which may support or fail to support proposed links in a model. Conceptual models are meant to be refined, not only during their initial stage of development, but also in response to new information that is gleaned through subsequent research.

Summary and Conclusion.

Researchers, practitioners, and policy makers can use conceptual models to convey ideas to diverse audiences. We posit that conceptual models may have the greatest impact on public health if they integrate social ecological and biological influences on health and highlight the potential for health equity and social justice principles to guide public health research, practice, and policy. To illustrate this point, we have provided examples of conceptual model development from the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium, a transdisciplinary scientific network established in the United States in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Consortium is developing conceptual models to guide its bladder health promotion and LUTS prevention research agenda. In concert with other researchers and community partners, the PLUS Consortium will be poised to inform future public health practices and policies. We hope our shared work will assist others in framing diverse public health matters in innovative, potentially transformative ways.

Acknowledgements

The authors acknowledge special contributions to featured conceptual models by the following PLUS Research Consortium members: Amanda Berry, Neill Epperson, Colleen Fitzgerald, Missy Lavender, Ariana Smith, and Beverly Williams. The authors also acknowledge the foundational work of Jo Anne Earp, Professor Emerita, and Susan T. Ennett, Professor, Department of Health Behavior, Gillings School of Public Health, University of North Carolina, Chapel Hill. Dr. Earp and Dr. Ennett’s pioneering “how to” guide for building conceptual models, published in 1991, inspired the present guide. In addition, the authors acknowledge Kenneth L. McLeroy, Professor Emeritus and retired Regents and Distinguished Professor, School of Public Health, Texas A&M University, for helpful discussion about manuscript content.

Participating PLUS research centers at the time of this writing are as follows:

Loyola University Chicago - 2160 S. 1 st Avenue, Maywood, Il 60153-3328

Linda Brubaker, MD, MS, Multi-PI; Elizabeth Mueller, MD, MSME, Multi-PI; Colleen M. Fitzgerald, MD, MS, Investigator; Cecilia T. Hardacker, RN, MSN, Investigator; Jeni Hebert-Beirne, PhD, MPH, Investigator; Missy Lavender, MBA, Investigator; David A. Shoham, PhD, Investigator

University of Alabama at Birmingham - 1720 2nd Ave South, Birmingham, AL 35294

Kathryn Burgio, PhD, PI; Cora E. Lewis, MD, MSPH, Investigator; Alayne Markland, DO, MSc, Investigator; Gerald McGwin, PhD, Investigator; Beverly Williams, PhD, Investigator

University of California San Diego - 9500 Gilman Drive, La Jolla, CA 92093-0021

Emily S. Lukacz, MD, PI; Sheila Gahagan, MD, MPH, Investigator; D. Yvette LaCoursiere, MD, MPH, Investigator; Jesse N. Nodora, DrPH, Investigator

University of Michigan - 500 S. State Street, Ann Arbor, MI 48109

Janis M. Miller, PhD, MSN, PI; Lawrence Chin-I An, MD, Investigator; Lisa Kane Low, PhD, MS, CNM, Investigator

University of Pennsylvania – Urology, 3rd FL West, Perelman Bldg, 34th & Spruce St, Philadelphia, PA 19104

Diane Kaschak Newman, DNP, ANP-BC, FAAN PI; Amanda Berry, PhD, CRNP, Investigator; C. Neill Epperson, MD, Investigator; Kathryn H. Schmitz, PhD, MPH, FACSM, FTOS, Investigator; Ariana L. Smith, MD, Investigator; Ann Stapleton, MD, FIDSA, FACP, Investigator; Jean Wyman, PhD, RN, FAAN, Investigator

Washington University in St. Louis - One Brookings Drive, St. Louis, MO 63130

Siobhan Sutcliffe, PhD, PI; Colleen McNicholas, DO, MSc, Investigator; Aimee James, PhD, MPH, Investigator; Jerry Lowder, MD, MSc, Investigator;

Yale University - PO Box 208058 New Haven, CT 06520-8058

Leslie Rickey, MD, PI; Deepa Camenga, MD, MHS, Investigator; Shayna D. Cunningham, PhD, Investigator; Toby Chai, MD, Investigator; Jessica B. Lewis, PhD, MFT, Investigator

Steering Committee Chair: Mary H. Palmer, PhD, RN: University of North Carolina

NIH Program Office: National Institute of Diabetes and Digestive and Kidney Diseases, Division of Kidney, Urologic, and Hematologic Diseases, Bethesda, MD

NIH Project Scientist: Tamara Bavendam MD, MS; Project Officer: Ziya Kirkali, MD; Scientific Advisors: Chris Mullins, PhD and Jenna Norton, MPH; Scientific and Data Coordinating Center (SDCC): University of Minnesota - 3 Morrill Hall, 100 Church St. S.E., Minneapolis MN 55455

Bernard Harlow, PhD, Multi-PI; Kyle Rudser, PhD, Multi-PI; Sonya S. Brady, PhD, Investigator; John Connett, PhD, Investigator; Haitao Chu, MD, PhD, Investigator; Cynthia Fok, MD, MPH, Investigator; Todd Rockwood, PhD, Investigator; Melissa Constantine, PhD, MPAff, Investigator

This work of the Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium was supported by the National Institutes of Health (NIH) through cooperative agreements (grant numbers U01DK106786, U01DK106853, U01DK106858, U01DK106898, U01DK106893, U01DK106827, U01DK106908, U01DK106892). Additional support was provided by the National Institute on Aging, NIH Office of Research on Women’s Health, and NIH Office of Behavioral and Social Sciences Research. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

Contributor Information

Sonya S. Brady, Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, 55454, USA.

Linda Brubaker, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, 92037, USA.

Cynthia S. Fok, Department of Urology, University of Minnesota Medical School, Minneapolis, MN, 55454, USA.

Sheila Gahagan, Division of Academic General Pediatrics, University of California San Diego, San Diego, CA, 92093, USA.

Cora E. Lewis, Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.

Jessica Lewis, Yale School of Public Health, New Haven, CT, 06520, USA.

Jerry L. Lowder, Division of Female Pelvic Medicine and Reconstructive Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA.

Jesse Nodora, Department of Family Medicine and Public Health and Moores UC San Diego Cancer Center, University of California San Diego, La Jolla, CA, 92161, USA.

Ann Stapleton, Department of Medicine, University of Washington, Seattle, WA, 98195, USA.

Mary H. Palmer, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

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How to Make a Conceptual Framework

How to Make a Conceptual Framework

  • 6-minute read
  • 2nd January 2022

What is a conceptual framework? And why is it important?

A conceptual framework illustrates the relationship between the variables of a research question. It’s an outline of what you’d expect to find in a research project.

Conceptual frameworks should be constructed before data collection and are vital because they map out the actions needed in the study. This should be the first step of an undergraduate or graduate research project.

What Is In a Conceptual Framework?

In a conceptual framework, you’ll find a visual representation of the key concepts and relationships that are central to a research study or project . This can be in form of a diagram, flow chart, or any other visual representation. Overall, a conceptual framework serves as a guide for understanding the problem being studied and the methods being used to investigate it.

Steps to Developing the Perfect Conceptual Framework

  • Pick a question
  • Conduct a literature review
  • Identify your variables
  • Create your conceptual framework

1. Pick a Question

You should already have some idea of the broad area of your research project. Try to narrow down your research field to a manageable topic in terms of time and resources. From there, you need to formulate your research question. A research question answers the researcher’s query: “What do I want to know about my topic?” Research questions should be focused, concise, arguable and, ideally, should address a topic of importance within your field of research.

An example of a simple research question is: “What is the relationship between sunny days and ice cream sales?”

2. Conduct a Literature Review

A literature review is an analysis of the scholarly publications on a chosen topic. To undertake a literature review, search for articles with the same theme as your research question. Choose updated and relevant articles to analyze and use peer-reviewed and well-respected journals whenever possible.

For the above example, the literature review would investigate publications that discuss how ice cream sales are affected by the weather. The literature review should reveal the variables involved and any current hypotheses about this relationship.

3. Identify Your Variables

There are two key variables in every experiment: independent and dependent variables.

Independent Variables

The independent variable (otherwise known as the predictor or explanatory variable) is the expected cause of the experiment: what the scientist changes or changes on its own. In our example, the independent variable would be “the number of sunny days.”

Dependent Variables

The dependent variable (otherwise known as the response or outcome variable) is the expected effect of the experiment: what is being studied or measured. In our example, the dependent variable would be “the quantity of ice cream sold.”

Next, there are control variables.

Control Variables

A control variable is a variable that may impact the dependent variable but whose effects are not going to be measured in the research project. In our example, a control variable could be “the socioeconomic status of participants.” Control variables should be kept constant to isolate the effects of the other variables in the experiment.

Finally, there are intervening and extraneous variables.

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Intervening Variables

Intervening variables link the independent and dependent variables and clarify their connection. In our example, an intervening variable could be “temperature.”

Extraneous Variables

Extraneous variables are any variables that are not being investigated but could impact the outcomes of the study. Some instances of extraneous variables for our example would be “the average price of ice cream” or “the number of varieties of ice cream available.” If you control an extraneous variable, it becomes a control variable.

4. Create Your Conceptual Framework

Having picked your research question, undertaken a literature review, and identified the relevant variables, it’s now time to construct your conceptual framework. Conceptual frameworks are clear and often visual representations of the relationships between variables.

We’ll start with the basics: the independent and dependent variables.

Our hypothesis is that the quantity of ice cream sold directly depends on the number of sunny days; hence, there is a cause-and-effect relationship between the independent variable (the number of sunny days) and the dependent and independent variable (the quantity of ice cream sold).

Next, introduce a control variable. Remember, this is anything that might directly affect the dependent variable but is not being measured in the experiment:

Finally, introduce the intervening and extraneous variables. 

The intervening variable (temperature) clarifies the relationship between the independent variable (the number of sunny days) and the dependent variable (the quantity of ice cream sold). Extraneous variables, such as the average price of ice cream, are variables that are not controlled and can potentially impact the dependent variable.

Are Conceptual Frameworks and Research Paradigms the Same?

In simple terms, the research paradigm is what informs your conceptual framework. In defining our research paradigm we ask the big questions—Is there an objective truth and how can we understand it? If we decide the answer is yes, we may be working with a positivist research paradigm and will choose to build a conceptual framework that displays the relationship between fixed variables. If not, we may be working with a constructivist research paradigm, and thus our conceptual framework will be more of a loose amalgamation of ideas, theories, and themes (a qualitative study). If this is confusing–don’t worry! We have an excellent blog post explaining research paradigms in more detail.

Where is the Conceptual Framework Located in a Thesis?

This will depend on your discipline, research type, and school’s guidelines, but most papers will include a section presenting the conceptual framework in the introduction, literature review, or opening chapter. It’s best to present your conceptual framework after presenting your research question, but before outlining your methodology.

Can a Conceptual Framework be Used in a Qualitative Study?

Yes. Despite being less clear-cut than a quantitative study, all studies should present some form of a conceptual framework. Let’s say you were doing a study on care home practices and happiness, and you came across a “happiness model” constructed by a relevant theorist in your literature review. Your conceptual framework could be an outline or a visual depiction of how you will use this model to collect and interpret qualitative data for your own study (such as interview responses). Check out this useful resource showing other examples of conceptual frameworks for qualitative studies .

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How To Make Conceptual Framework (With Examples and Templates)

How To Make Conceptual Framework (With Examples and Templates)

We all know that a research paper has plenty of concepts involved. However, a great deal of concepts makes your study confusing.

A conceptual framework ensures that the concepts of your study are organized and presented comprehensively. Let this article guide you on how to make the conceptual framework of your study.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

At a glance: free conceptual framework templates.

Too busy to create a conceptual framework from scratch? No problem. We’ve created templates for each conceptual framework so you can start on the right foot. All you need to do is enter the details of the variables. Feel free to modify the design according to your needs. Please read the main article below to learn more about the conceptual framework.

Conceptual Framework Template #1: Independent-Dependent Variable Model

Conceptual framework template #2: input-process-output (ipo) model, conceptual framework template #3: concept map, what is a conceptual framework.

A conceptual framework shows the relationship between the variables of your study.  It includes a visual diagram or a model that summarizes the concepts of your study and a narrative explanation of the model presented.

Why Should Research Be Given a Conceptual Framework?

Imagine your study as a long journey with the research result as the destination. You don’t want to get lost in your journey because of the complicated concepts. This is why you need to have a guide. The conceptual framework keeps you on track by presenting and simplifying the relationship between the variables. This is usually done through the use of illustrations that are supported by a written interpretation.

Also, people who will read your research must have a clear guide to the variables in your study and where the research is heading. By looking at the conceptual framework, the readers can get the gist of the research concepts without reading the entire study. 

Related: How to Write Significance of the Study (with Examples)

What Is the Difference Between Conceptual Framework and Theoretical Framework?

Both of them show concepts and ideas of your study. The theoretical framework presents the theories, rules, and principles that serve as the basis of the research. Thus, the theoretical framework presents broad concepts related to your study. On the other hand, the conceptual framework shows a specific approach derived from the theoretical framework. It provides particular variables and shows how these variables are related.

Let’s say your research is about the Effects of Social Media on the Political Literacy of College Students. You may include some theories related to political literacy, such as this paper, in your theoretical framework. Based on this paper, political participation and awareness determine political literacy.

For the conceptual framework, you may state that the specific form of political participation and awareness you will use for the study is the engagement of college students on political issues on social media. Then, through a diagram and narrative explanation, you can show that using social media affects the political literacy of college students.

What Are the Different Types of Conceptual Frameworks?

The conceptual framework has different types based on how the research concepts are organized 1 .

1. Taxonomy

In this type of conceptual framework, the phenomena of your study are grouped into categories without presenting the relationship among them. The point of this conceptual framework is to distinguish the categories from one another.

2. Visual Presentation

In this conceptual framework, the relationship between the phenomena and variables of your study is presented. Using this conceptual framework implies that your research provides empirical evidence to prove the relationship between variables. This is the type of conceptual framework that is usually used in research studies.

3. Mathematical Description

In this conceptual framework, the relationship between phenomena and variables of your study is described using mathematical formulas. Also, the extent of the relationship between these variables is presented with specific quantities.

How To Make Conceptual Framework: 4 Steps

1. identify the important variables of your study.

There are two essential variables that you must identify in your study: the independent and the dependent variables.

An independent variable is a variable that you can manipulate. It can affect the dependent variable. Meanwhile, the dependent variable is the resulting variable that you are measuring.

You may refer to your research question to determine your research’s independent and dependent variables.

Suppose your research question is: “Is There a Significant Relationship Between the Quantity of Organic Fertilizer Used and the Plant’s Growth Rate?” The independent variable of this study is the quantity of organic fertilizer used, while the dependent variable is the plant’s growth rate.

2. Think About How the Variables Are Related

Usually, the variables of a study have a direct relationship. If a change in one of your variables leads to a corresponding change in another, they might have this kind of relationship.

However, note that having a direct relationship between variables does not mean they already have a cause-and-effect relationship 2 . It takes statistical analysis to prove causation between variables.

Using our example earlier, the quantity of organic fertilizer may directly relate to the plant’s growth rate. However, we are not sure that the quantity of organic fertilizer is the sole reason for the plant’s growth rate changes.

3. Analyze and Determine Other Influencing Variables

Consider analyzing if other variables can affect the relationship between your independent and dependent variables 3 .

4. Create a Visual Diagram or a Model

Now that you’ve identified the variables and their relationship, you may create a visual diagram summarizing them.

Usually, shapes such as rectangles, circles, and arrows are used for the model. You may create a visual diagram or model for your conceptual framework in different ways. The three most common models are the independent-dependent variable model, the input-process-output (IPO) model, and concept maps.

a. Using the Independent-Dependent Variable Model

You may create this model by writing the independent and dependent variables inside rectangles. Then, insert a line segment between them, connecting the rectangles. This line segment indicates the direct relationship between these variables. 

Below is a visual diagram based on our example about the relationship between organic fertilizer and a plant’s growth rate. 

conceptual framework 1

b. Using the Input-Process-Output (IPO) Model

If you want to emphasize your research process, the input-process-output model is the appropriate visual diagram for your conceptual framework.

To create your visual diagram using the IPO model, follow these steps:

  • Determine the inputs of your study . Inputs are the variables you will use to arrive at your research result. Usually, your independent variables are also the inputs of your research. Let’s say your research is about the Level of Satisfaction of College Students Using Google Classroom as an Online Learning Platform. You may include in your inputs the profile of your respondents and the curriculum used in the online learning platform.
  • Outline your research process. Using our example above, the research process should be like this: Data collection of student profiles → Administering questionnaires → Tabulation of students’ responses → Statistical data analysis.
  • State the research output . Indicate what you are expecting after you conduct the research. In our example above, the research output is the assessed level of satisfaction of college students with the use of Google Classroom as an online learning platform.
  • Create the model using the research’s determined input, process, and output.

Presented below is the IPO model for our example above.

conceptual framework 2

c. Using Concept Maps

If you think the two models presented previously are insufficient to summarize your study’s concepts, you may use a concept map for your visual diagram.

A concept map is a helpful visual diagram if multiple variables affect one another. Let’s say your research is about Coping with the Remote Learning System: Anxiety Levels of College Students. Presented below is the concept map for the research’s conceptual framework:

conceptual framework 3

5. Explain Your Conceptual Framework in Narrative Form

Provide a brief explanation of your conceptual framework. State the essential variables, their relationship, and the research outcome.

Using the same example about the relationship between organic fertilizer and the growth rate of the plant, we can come up with the following explanation to accompany the conceptual framework:

Figure 1 shows the Conceptual Framework of the study. The quantity of the organic fertilizer used is the independent variable, while the plant’s growth is the research’s dependent variable. These two variables are directly related based on the research’s empirical evidence.

Conceptual Framework in Quantitative Research

You can create your conceptual framework by following the steps discussed in the previous section. Note, however, that quantitative research has statistical analysis. Thus, you may use arrows to indicate a cause-and-effect relationship in your model. An arrow implies that your independent variable caused the changes in your dependent variable.

Usually, for quantitative research, the Input-Process-Output model is used as a visual diagram. Here is an example of a conceptual framework in quantitative research:

Research Topic : Level of Effectiveness of Corn (Zea mays) Silk Ethanol Extract as an Antioxidant

conceptual framework 4

Conceptual Framework in Qualitative Research

Again, you can follow the same step-by-step guide discussed previously to create a conceptual framework for qualitative research. However, note that you should avoid using one-way arrows as they may indicate causation . Qualitative research cannot prove causation since it uses only descriptive and narrative analysis to relate variables.

Here is an example of a conceptual framework in qualitative research:

Research Topic : Lived Experiences of Medical Health Workers During Community Quarantine

conceptual framework 5

Conceptual Framework Examples

Presented below are some examples of conceptual frameworks.

Research Topic : Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract in the Blood Glucose Level of Swiss Mice (Mus musculus)

conceptual framework 6

Figure 1 presents the Conceptual Framework of the study. The quantity of gabi leaf extract is the independent variable, while the Swiss mice’s blood glucose level is the study’s dependent variable. This study establishes a direct relationship between these variables through empirical evidence and statistical analysis . 

Research Topic : Level of Effectiveness of Using Social Media in the Political Literacy of College Students

conceptual framework 7

Figure 1 shows the Conceptual Framework of the study. The input is the profile of the college students according to sex, year level, and the social media platform being used. The research process includes administering the questionnaires, tabulating students’ responses, and statistical data analysis and interpretation. The output is the effectiveness of using social media in the political literacy of college students.

Research Topic: Factors Affecting the Satisfaction Level of Community Inhabitants

conceptual framework 8

Figure 1 presents a visual illustration of the factors that affect the satisfaction level of community inhabitants. As presented, environmental, societal, and economic factors influence the satisfaction level of community inhabitants. Each factor has its indicators which are considered in this study.

Tips and Warnings

  • Please keep it simple. Avoid using fancy illustrations or designs when creating your conceptual framework. 
  • Allot a lot of space for feedback. This is to show that your research variables or methodology might be revised based on the input from the research panel. Below is an example of a conceptual framework with a spot allotted for feedback.

conceptual framework 9

Frequently Asked Questions

1. how can i create a conceptual framework in microsoft word.

First, click the Insert tab and select Shapes . You’ll see a wide range of shapes to choose from. Usually, rectangles, circles, and arrows are the shapes used for the conceptual framework. 

conceptual framework 10

Next, draw your selected shape in the document.

conceptual framework 11

Insert the name of the variable inside the shape. You can do this by pointing your cursor to the shape, right-clicking your mouse, selecting Add Text , and typing in the text.

conceptual framework 12

Repeat the same process for the remaining variables of your study. If you need arrows to connect the different variables, you can insert one by going to the Insert tab, then Shape, and finally, Lines or Block Arrows, depending on your preferred arrow style.

2. How to explain my conceptual framework in defense?

If you have used the Independent-Dependent Variable Model in creating your conceptual framework, start by telling your research’s variables. Afterward, explain the relationship between these variables. Example: “Using statistical/descriptive analysis of the data we have collected, we are going to show how the <state your independent variable> exhibits a significant relationship to <state your dependent variable>.”

On the other hand, if you have used an Input-Process-Output Model, start by explaining the inputs of your research. Then, tell them about your research process. You may refer to the Research Methodology in Chapter 3 to accurately present your research process. Lastly, explain what your research outcome is.

Meanwhile, if you have used a concept map, ensure you understand the idea behind the illustration. Discuss how the concepts are related and highlight the research outcome.

3. In what stage of research is the conceptual framework written?

The research study’s conceptual framework is in Chapter 2, following the Review of Related Literature.

4. What is the difference between a Conceptual Framework and Literature Review?

The Conceptual Framework is a summary of the concepts of your study where the relationship of the variables is presented. On the other hand, Literature Review is a collection of published studies and literature related to your study. 

Suppose your research concerns the Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract on Swiss Mice (Mus musculus). In your conceptual framework, you will create a visual diagram and a narrative explanation presenting the quantity of gabi leaf extract and the mice’s blood glucose level as your research variables. On the other hand, for the literature review, you may include this study and explain how this is related to your research topic.

5. When do I use a two-way arrow for my conceptual framework?

You will use a two-way arrow in your conceptual framework if the variables of your study are interdependent. If variable A affects variable B and variable B also affects variable A, you may use a two-way arrow to show that A and B affect each other.

Suppose your research concerns the Relationship Between Students’ Satisfaction Levels and Online Learning Platforms. Since students’ satisfaction level determines the online learning platform the school uses and vice versa, these variables have a direct relationship. Thus, you may use two-way arrows to indicate that the variables directly affect each other.

  • Conceptual Framework – Meaning, Importance and How to Write it. (2020). Retrieved 27 April 2021, from https://afribary.com/knowledge/conceptual-framework/
  • Correlation vs Causation. Retrieved 27 April 2021, from https://www.jmp.com/en_ph/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html
  • Swaen, B., & George, T. (2022, August 22). What is a conceptual framework? Tips & Examples. Retrieved December 5, 2022, from https://www.scribbr.com/methodology/conceptual-framework/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

conceptual framework in research models

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

Browse all articles written by Jewel Kyle Fabula

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Theoretical vs Conceptual Framework

What they are & how they’re different (with examples)

By: Derek Jansen (MBA) | Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, sooner or later you’re bound to run into the terms theoretical framework and conceptual framework . These are closely related but distinctly different things (despite some people using them interchangeably) and it’s important to understand what each means. In this post, we’ll unpack both theoretical and conceptual frameworks in plain language along with practical examples , so that you can approach your research with confidence.

Overview: Theoretical vs Conceptual

What is a theoretical framework, example of a theoretical framework, what is a conceptual framework, example of a conceptual framework.

  • Theoretical vs conceptual: which one should I use?

A theoretical framework (also sometimes referred to as a foundation of theory) is essentially a set of concepts, definitions, and propositions that together form a structured, comprehensive view of a specific phenomenon.

In other words, a theoretical framework is a collection of existing theories, models and frameworks that provides a foundation of core knowledge – a “lay of the land”, so to speak, from which you can build a research study. For this reason, it’s usually presented fairly early within the literature review section of a dissertation, thesis or research paper .

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Let’s look at an example to make the theoretical framework a little more tangible.

If your research aims involve understanding what factors contributed toward people trusting investment brokers, you’d need to first lay down some theory so that it’s crystal clear what exactly you mean by this. For example, you would need to define what you mean by “trust”, as there are many potential definitions of this concept. The same would be true for any other constructs or variables of interest.

You’d also need to identify what existing theories have to say in relation to your research aim. In this case, you could discuss some of the key literature in relation to organisational trust. A quick search on Google Scholar using some well-considered keywords generally provides a good starting point.

foundation of theory

Typically, you’ll present your theoretical framework in written form , although sometimes it will make sense to utilise some visuals to show how different theories relate to each other. Your theoretical framework may revolve around just one major theory , or it could comprise a collection of different interrelated theories and models. In some cases, there will be a lot to cover and in some cases, not. Regardless of size, the theoretical framework is a critical ingredient in any study.

Simply put, the theoretical framework is the core foundation of theory that you’ll build your research upon. As we’ve mentioned many times on the blog, good research is developed by standing on the shoulders of giants . It’s extremely unlikely that your research topic will be completely novel and that there’ll be absolutely no existing theory that relates to it. If that’s the case, the most likely explanation is that you just haven’t reviewed enough literature yet! So, make sure that you take the time to review and digest the seminal sources.

Need a helping hand?

conceptual framework in research models

A conceptual framework is typically a visual representation (although it can also be written out) of the expected relationships and connections between various concepts, constructs or variables. In other words, a conceptual framework visualises how the researcher views and organises the various concepts and variables within their study. This is typically based on aspects drawn from the theoretical framework, so there is a relationship between the two.

Quite commonly, conceptual frameworks are used to visualise the potential causal relationships and pathways that the researcher expects to find, based on their understanding of both the theoretical literature and the existing empirical research . Therefore, the conceptual framework is often used to develop research questions and hypotheses .

Let’s look at an example of a conceptual framework to make it a little more tangible. You’ll notice that in this specific conceptual framework, the hypotheses are integrated into the visual, helping to connect the rest of the document to the framework.

example of a conceptual framework

As you can see, conceptual frameworks often make use of different shapes , lines and arrows to visualise the connections and relationships between different components and/or variables. Ultimately, the conceptual framework provides an opportunity for you to make explicit your understanding of how everything is connected . So, be sure to make use of all the visual aids you can – clean design, well-considered colours and concise text are your friends.

Theoretical framework vs conceptual framework

As you can see, the theoretical framework and the conceptual framework are closely related concepts, but they differ in terms of focus and purpose. The theoretical framework is used to lay down a foundation of theory on which your study will be built, whereas the conceptual framework visualises what you anticipate the relationships between concepts, constructs and variables may be, based on your understanding of the existing literature and the specific context and focus of your research. In other words, they’re different tools for different jobs , but they’re neighbours in the toolbox.

Naturally, the theoretical framework and the conceptual framework are not mutually exclusive . In fact, it’s quite likely that you’ll include both in your dissertation or thesis, especially if your research aims involve investigating relationships between variables. Of course, every research project is different and universities differ in terms of their expectations for dissertations and theses, so it’s always a good idea to have a look at past projects to get a feel for what the norms and expectations are at your specific institution.

Want to learn more about research terminology, methods and techniques? Be sure to check out the rest of the Grad Coach blog . Alternatively, if you’re looking for hands-on help, have a look at our private coaching service , where we hold your hand through the research process, step by step.

conceptual framework in research models

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20 Comments

CIPTA PRAMANA

Thank you for giving a valuable lesson

Muhammed Ebrahim Feto

good thanks!

Benson Wandago

VERY INSIGHTFUL

olawale rasaq

thanks for given very interested understand about both theoritical and conceptual framework

Tracey

I am researching teacher beliefs about inclusive education but not using a theoretical framework just conceptual frame using teacher beliefs, inclusive education and inclusive practices as my concepts

joshua

good, fantastic

Melese Takele

great! thanks for the clarification. I am planning to use both for my implementation evaluation of EmONC service at primary health care facility level. its theoretical foundation rooted from the principles of implementation science.

Dorcas

This is a good one…now have a better understanding of Theoretical and Conceptual frameworks. Highly grateful

Ahmed Adumani

Very educating and fantastic,good to be part of you guys,I appreciate your enlightened concern.

Lorna

Thanks for shedding light on these two t opics. Much clearer in my head now.

Cor

Simple and clear!

Alemayehu Wolde Oljira

The differences between the two topics was well explained, thank you very much!

Ntoks

Thank you great insight

Maria Glenda O. De Lara

Superb. Thank you so much.

Sebona

Hello Gradcoach! I’m excited with your fantastic educational videos which mainly focused on all over research process. I’m a student, I kindly ask and need your support. So, if it’s possible please send me the PDF format of all topic provided here, I put my email below, thank you!

Pauline

I am really grateful I found this website. This is very helpful for an MPA student like myself.

Adams Yusif

I’m clear with these two terminologies now. Useful information. I appreciate it. Thank you

Ushenese Roger Egin

I’m well inform about these two concepts in research. Thanks

Omotola

I found this really helpful. It is well explained. Thank you.

olufolake olumogba

very clear and useful. information important at start of research!!

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  • Chapter 1: Home
  • Narrowing Your Topic
  • Problem Statement
  • Purpose Statement

Defining The Conceptual Framework

Making a conceptual framework, conceptual framework for dmft students, conceptual framework guide, example frameworks, additional framework resources.

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What is it?

  • The researcher’s understanding/hypothesis/exploration of either an existing framework/model or how existing concepts come together to inform a particular problem. Shows the reader how different elements come together to facilitate research and a clear understanding of results.
  • Informs the research questions/methodology (problem statement drives framework drives RQs drives methodology)
  • A tool (linked concepts) to help facilitate the understanding of the relationship among concepts or variables in relation to the real-world. Each concept is linked to frame the project in question.
  • Falls inside of a larger theoretical framework (theoretical framework = explains the why and how of a particular phenomenon within a particular body of literature).
  • Can be a graphic or a narrative – but should always be explained and cited
  • Can be made up of theories and concepts

What does it do?

  • Explains or predicts the way key concepts/variables will come together to inform the problem/phenomenon
  • Gives the study direction/parameters
  • Helps the researcher organize ideas and clarify concepts
  • Introduces your research and how it will advance your field of practice. A conceptual framework should include concepts applicable to the field of study. These can be in the field or neighboring fields – as long as important details are captured and the framework is relevant to the problem. (alignment)

What should be in it?

  • Variables, concepts, theories, and/or parts of other existing frameworks

How to make a conceptual framework

  • With a topic in mind, go to the body of literature and start identifying the key concepts used by other studies. Figure out what’s been done by other researchers, and what needs to be done (either find a specific call to action outlined in the literature or make sure your proposed problem has yet to be studied in your specific setting). Use what you find needs to be done to either support a pre-identified problem or craft a general problem for study. Only rely on scholarly sources for this part of your research.
  • Begin to pull out variables, concepts, theories, and existing frameworks explained in the relevant literature.
  • If you’re building a framework, start thinking about how some of those variables, concepts, theories, and facets of existing frameworks come together to shape your problem. The problem could be a situational condition that requires a scholar-practitioner approach, the result of a practical need, or an opportunity to further an applicational study, project, or research. Remember, if the answer to your specific problem exists, you don’t need to conduct the study.
  • The actionable research you’d like to conduct will help shape what you include in your framework. Sketch the flow of your Applied Doctoral Project from start to finish and decide which variables are truly the best fit for your research.
  • Create a graphic representation of your framework (this part is optional, but often helps readers understand the flow of your research) Even if you do a graphic, first write out how the variables could influence your Applied Doctoral Project and introduce your methodology. Remember to use APA formatting in separating the sections of your framework to create a clear understanding of the framework for your reader.
  • As you move through your study, you may need to revise your framework.
  • Note for qualitative/quantitative research: If doing qualitative, make sure your framework doesn’t include arrow lines, which could imply causal or correlational linkages.
  • Conceptural and Theoretical Framework for DMFT Students This document is specific to DMFT students working on a conceptual or theoretical framework for their applied project.
  • Conceptual Framework Guide Use this guide to determine the guiding framework for your applied dissertation research.

Let’s say I’ve just taken a job as manager of a failing restaurant. Throughout the first week, I notice the few customers they have are leaving unsatisfied. I need to figure out why and turn the establishment into a thriving restaurant. I get permission from the owner to do a study to figure out exactly what we need to do to raise levels of customer satisfaction. Since I have a specific problem and want to make sure my research produces valid results, I go to the literature to find out what others are finding about customer satisfaction in the food service industry. This particular restaurant is vegan focused – and my search of the literature doesn’t say anything specific about how to increase customer service in a vegan atmosphere, so I know this research needs to be done.

I find out there are different types of satisfaction across other genres of the food service industry, and the one I’m interested in is cumulative customer satisfaction. I then decide based on what I’m seeing in the literature that my definition of customer satisfaction is the way perception, evaluation, and psychological reaction to perception and evaluation of both tangible and intangible elements of the dining experience come together to inform customer expectations. Essentially, customer expectations inform customer satisfaction.

I then find across the literature many variables could be significant in determining customer satisfaction. Because the following keep appearing, they are the ones I choose to include in my framework: price, service, branding (branched out to include physical environment and promotion), and taste. I also learn by reading the literature, satisfaction can vary between genders – so I want to make sure to also collect demographic information in my survey. Gender, age, profession, and number of children are a few demographic variables I understand would be helpful to include based on my extensive literature review.

Note: this is a quantitative study. I’m including all variables in this study, and the variables I am testing are my independent variables. Here I’m working to see how each of the independent variables influences (or not) my dependent variable, customer satisfaction. If you are interested in qualitative study, read on for an example of how to make the same framework qualitative in nature.

Also note: when you create your framework, you’ll need to cite each facet of your framework. Tell the reader where you got everything you’re including. Not only is it in compliance with APA formatting, but also it raises your credibility as a researcher. Once you’ve built the narrative around your framework, you may also want to create a visual for your reader.

See below for one example of how to illustrate your framework:

conceptual framework in research models

If you’re interested in a qualitative study, be sure to omit arrows and other notations inferring statistical analysis. The only time it would be inappropriate to include a framework in qualitative study is in a grounded theory study, which is not something you’ll do in an applied doctoral study.

A visual example of a qualitative framework is below:

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Some additional helpful resources in constructing a conceptual framework for study:

  • Problem Statement, Conceptual Framework, and Research Question. McGaghie, W. C.; Bordage, G.; and J. A. Shea (2001). Problem Statement, Conceptual Framework, and Research Question. Retrieved on January 5, 2015 from http://goo.gl/qLIUFg
  • Building a Conceptual Framework: Philosophy, Definitions, and Procedure
  • https://www.scribbr.com/dissertation/conceptual-framework/
  • https://www.projectguru.in/developing-conceptual-framework-in-a-research-paper/

Conceptual Framework Research

A conceptual framework is a synthetization of interrelated components and variables which help in solving a real-world problem. It is the final lens used for viewing the deductive resolution of an identified issue (Imenda, 2014). The development of a conceptual framework begins with a deductive assumption that a problem exists, and the application of processes, procedures, functional approach, models, or theory may be used for problem resolution (Zackoff et al., 2019). The application of theory in traditional theoretical research is to understand, explain, and predict phenomena (Swanson, 2013). In applied research the application of theory in problem solving focuses on how theory in conjunction with practice (applied action) and procedures (functional approach) frames vision, thinking, and action towards problem resolution. The inclusion of theory in a conceptual framework is not focused on validation or devaluation of applied theories. A concise way of viewing the conceptual framework is a list of understood fact-based conditions that presents the researcher’s prescribed thinking for solving the identified problem. These conditions provide a methodological rationale of interrelated ideas and approaches for beginning, executing, and defining the outcome of problem resolution efforts (Leshem & Trafford, 2007).

The term conceptual framework and theoretical framework are often and erroneously used interchangeably (Grant & Osanloo, 2014). Just as with traditional research, a theory does not or cannot be expected to explain all phenomenal conditions, a conceptual framework is not a random identification of disparate ideas meant to incase a problem. Instead it is a means of identifying and constructing for the researcher and reader alike an epistemological mindset and a functional worldview approach to the identified problem.

Grant, C., & Osanloo, A. (2014). Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House. ” Administrative Issues Journal: Connecting Education, Practice, and Research, 4(2), 12–26

Imenda, S. (2014). Is There a Conceptual Difference between Theoretical and Conceptual Frameworks? Sosyal Bilimler Dergisi/Journal of Social Sciences, 38(2), 185.

Leshem, S., & Trafford, V. (2007). Overlooking the conceptual framework. Innovations in Education & Teaching International, 44(1), 93–105. https://doi-org.proxy1.ncu.edu/10.1080/14703290601081407

Swanson, R. (2013). Theory building in applied disciplines . San Francisco: Berrett-Koehler Publishers.

Zackoff, M. W., Real, F. J., Klein, M. D., Abramson, E. L., Li, S.-T. T., & Gusic, M. E. (2019). Enhancing Educational Scholarship Through Conceptual Frameworks: A Challenge and Roadmap for Medical Educators . Academic Pediatrics, 19(2), 135–141. https://doi-org.proxy1.ncu.edu/10.1016/j.acap.2018.08.003

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Conceptual Framework and Theory Development

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conceptual framework in research models

  • George P. Moschis 2  

A conceptual framework is a structure that researchers use to best explain the relationship they expect to see between variables, or the characteristics of the phenomenon they investigate. This term is used often interchangeably with “model” and “theory.” And although all conceptual frameworks and models are not necessarily theories, theories and theoretical frameworks are normally viewed as conceptual frameworks. A conceptual framework forms the basic foundation for conducting a study, helping to summarize either the basic constructs and their expected relationships in a deductive investigation, or the outcomes of the inductive inquiry. They evolve and change as knowledge accumulates (Miles et al., 2014). As Miles et al. (2014: 20) put it: “Conceptual frameworks are simply the current version of the researcher’s map of the territory being investigated. As the explorer’s knowledge of the terrain improves, the map becomes correspondingly more differentiated and integrated.” Thus, a conceptual framework becomes increasingly refined, elaborate, and comprehensive as knowledge accumulates and forms the basis for its expansion and revision, which may also suggest the use of different methodologies (Ravitch & Riggan, 2016).

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Use of systems thinking and adapted group model building methods to understand patterns of technology use among older adults with type 1 diabetes: a preliminary process evaluation

  • Anna R. Kahkoska 1 , 2 , 3 ,
  • Cambray Smith 4 ,
  • Laura A. Young 2 &
  • Kristen Hassmiller Lich 4  

BMC Medical Research Methodology volume  24 , Article number:  126 ( 2024 ) Cite this article

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A growing number of older adults (ages 65+) live with Type 1 diabetes. Simultaneously, technologies such as continuous glucose monitoring (CGM) have become standard of care. There is thus a need to understand better the complex dynamics that promote use of CGM (and other care innovations) over time in this age group. Our aim was to adapt methods from systems thinking, specifically a participatory approach to system dynamics modeling called group model building (GMB), to model the complex experiences that may underlie different trajectories of CGM use among this population. Herein, we report on the feasibility, strengths, and limitations of this methodology.

We conducted a series of GMB workshops and validation interviews to collect data in the form of questionnaires, diagrams, and recordings of group discussion. Data were integrated into a conceptual diagram of the “system” of factors associated with uptake and use of CGM over time. We evaluate the feasibility of each aspect of the study, including the teaching of systems thinking to older adult participants. We collected participant feedback on positive aspects of their experiences and areas for improvement.

We completed nine GMB workshops with older adults and their caregivers ( N  = 33). Each three-hour in-person workshop comprised: (1) questionnaires; (2) the GMB session, including both didactic components and structured activities; and (3) a brief focus group discussion. Within the GMB session, individual drawing activities proved to be the most challenging for participants, while group activities and discussion of relevant dynamics over time for illustrative (i.e., realistic but not real) patients yielded rich engagement and sufficient information for system diagramming. Study participants liked the opportunity to share experiences with peers, learning and enhancing their knowledge, peer support, age-specific discussions, the workshop pace and structure, and the systems thinking framework. Participants gave mixed feedback on the workshop duration.

Conclusions

The study demonstrates preliminary feasibility, acceptability, and the value of GMB for engaging older adults about key determinants of complex health behaviors over time. To our knowledge, few studies have extended participatory systems science methods to older adult stakeholders. Future studies may utilize this methodology to inform novel approaches for supporting health across the lifespan.

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Type 1 diabetes is a chronic disease in which the pancreas no longer produces insulin, the hormone critical for blood glucose homeostasis [ 1 ]. Exposure to elevated blood glucose levels (i.e., hyperglycemia) over time is associated with the development of multiple chronic complications, including neuropathy, retinopathy, nephropathy, and cardiovascular disease, while episodes of low blood sugar (i.e., hypoglycemia) can be life-threatening and require urgent attention [ 1 , 2 ]. As a result, constant self-management is required to maintain blood glucose levels as near-normal as possible. Unfortunately, self-management is made challenging by dynamic insulin needs, which can be influenced by dietary intake, physical activity, stress, and illness — and thus vary hour-to-hour, day-to-day, and over longer arcs of time impacting health in important ways [ 2 ]. As a result, individuals living with Type 1 diabetes are tasked with regularly measuring their blood glucose levels, assessing and accounting for dietary intake, dosing and timing exogenous insulin delivered through injection or insulin pump modalities, responding to hyper- and hypoglycemia, and accounting for other factors such as physical activity, stress, and illness [ 2 ].

Older adults with type 1 diabetes

As the US older adult population (≥ 65 years) grows and the life expectancy associated with a diagnosis of Type 1 diabetes increases, a sizable population of older adults living with Type 1 diabetes has emerged; this population is expected to continue expanding in upcoming years [ 3 ]. From a clinical perspective, care and management of Type 1 diabetes in older adulthood is often complex, as patients vary according to age, functional health, presence of frailty, and comorbidity profiles [ 4 ]. Compared to younger adults living with Type 1 diabetes for whom the primary focus of care and self-management is on robust glucose control, older adults living with Type 1 diabetes should primarily be focusing on the avoidance of hypoglycemia. Older adults have an increased risk for hypoglycemia, which remains a grave clinical concern due to high morbidity and mortality [ 4 , 5 , 6 ]. In addition to ensuring patient safety through the avoidance of hypoglycemia, accommodating patient preferences and preserving quality of life have been outlined as objectives for care [ 7 , 8 , 9 , 10 ]. However, more specific and applied data to guide care in this population are currently scant, largely owing to relatively recent expansion of this patient population [ 4 , 5 , 10 ].

Technologic approaches for type 1 diabetes management

New technologic approaches for both glucose monitoring and insulin delivery have been developed to improve strategies for Type 1 diabetes management [ 2 ]. One such development is continuous glucose monitoring (CGM), a remote monitoring approach to blood glucose measurement. CGM systems include three components: an on-body sensor with a subcutaneous catheter to measure interstitial glucose approximately every five minutes, a Bluetooth “transmitter,” and an external receiver that displays the real-time blood glucose [ 11 , 12 ]. CGM is offered currently in two forms, including real-time CGM, or systems that measure and display real-time or near real-time glucose levels at all time, and intermittently scanned CGM, systems that require individuals to scan their device against the sensor to access glucose information [ 12 ]. Both types of CGM offer several major advantages over alternative, invasive self-monitoring approaches for blood glucose, which require individuals to frequently obtain a small blood sample via finger prick and use a glucometer to measure glucose levels therein. These advantages include access to real-time or near-real time blood glucose information, data on glucose trends (including the rate of rising and falling glucose levels), and less invasive testing methods. Based on growing evidence to suggest clinical and patient-oriented benefits of CGM use, including improved glycemic control and psychosocial wellbeing, clinical practice guidelines now suggest that CGM be offered for all adults with Type 1 diabetes [ 12 ]. Practice guidelines specify that adults with diabetes must be capable of using CGM themselves, which may include help from a caregiver, and the specific selection of device should reflect individual patient circumstances, preferences, and clinical needs [ 12 ].

Benefits and challenges of continuous glucose monitoring for older adults with type 1 diabetes

Despite the advantages, a major knowledge gap exists regarding how older adults with Type 1 diabetes interact with, and may ultimately benefit from, diabetes technology like CGM. This gap was highlighted as a critical area for future research in a 2020 consensus statement published on behalf of the International Geriatric Diabetes Society [ 4 ]. Data from efficacy-based studies suggest that CGM may confer a significant safety benefit for this age group; in a randomized control trial, use of CGM modestly reduced hypoglycemia over six months among older adults with T1D [ 13 ]. The trial measured the duration of hypoglycemia, or the time that blood glucose levels were below 70 mg/dL [ 13 ]. Importantly, the reduction in hypoglycemia occurred concurrently with improvements in overall glycemic control, as measured by hemoglobin A1c as well as the time-in-range, or duration of time that blood glucose levels were measured between 70 mg/dL and 180 mg/dL [ 13 ]. This finding was important in showing that the reduction in hypoglycemia did not come at the cost of more time spent in hyperglycemic ranges. A handful of observational studies have further reinforced positive effects of CGM in older adults, including decreased hypoglycemia [ 14 ], reduced hemoglobin A1c and glycemic variability [ 15 ], and increased well-being and feelings of security [ 16 ].

Although estimates of the prevalence of CGM use in real-world populations of older adults vary, they range between approximately 30–70% in various studies and settings, suggesting opportunities to increase uptake [ 14 , 17 , 18 ]. It is further established that the general use of medical technology may represent a complicated issue for older adults, particularly with regards to unique, age-specific barriers and the range of biopsychosocial needs that exist across the population [ 19 ]. For example, physical symptoms, functional limitations, barriers to care, and psychosocial wellbeing all impact on disease self-management and may impact technology uptake. The growing number of chronic medical conditions accrued in older adulthood lends further complexity to integrating tools that may help improve quality of life. Accessibility features are lacking, including those to address changes to dexterity, visual acuity, and hearing loss [ 19 ]. From a psychosocial perspective, older adults may find learning new technologies to be challenging [ 14 ] and may require more time for education and training to use CGM and learn to interpret data [ 17 ]. Compared to younger adults, studies using questionnaire data have shown older adults perceive substantially higher burdens of technology such as CGM, including concerns that sensor readings cannot be trusted, information from CGM may cause too much worry, and that the technology will be too hard to understand [ 17 ]. Interestingly, differences in perceived burdens were substantially less pronounced across age groups in those who use CGM, suggesting that with adequate time, training, and support, older adults can use CGM effectively and experience clinical benefits [ 13 , 15 , 17 ]. However, complex interventions such as this are often plagued with challenges [ 20 ], and so identifying the most critical elements to support success as efficiently as possible will increase the likelihood of successful translation across the broader population.

Objective of the study

There are very limited data on what supports uptake and sustained use of CGM from the perspective of patients and their caregivers, how this technology impacts disease self-management, lived experiences, and clinical outcomes, and what suboptimal responses to technologic approaches over time may occur and why [ 4 , 21 ]. Our objective thus was to understand the complex nature of older adults’ experiences associated with initiating and sustaining use of CGM, including changes in different clinical, behavioral, and psychosocial variables over time, and how these variables interact to ultimately produce patterns of effective use versus less effective use or nonuse.

These data are needed to inform how clinical recommendations and supports can be developed to help all individuals with Type 1 diabetes incorporate the ever-evolving technologic aspects of diabetes management into their care regimes, regardless of biologic, clinical, and psychosocial differences [ 22 ]. As the population of older adults with diabetes grows, these data are also needed to ensure existing and emerging diabetes technology remain accessible across the lifespan.

Selection of the research methodology

We applied concepts and methods from systems science, specifically Group Model Building (GMB) – a stakeholder-engaged approach to systems thinking from the system dynamics perspective. Key terms relevant for systems thinking and system dynamics are shown in Table  1 along with their definitions.

The rationale for this approach is as follows. We hypothesized that a complex system of factors may shape older adults’ experiences with diabetes self-management and technology use over time, and that a scientific approach to capture dynamic complexity in these experiences may offer insights into future interventions. A complex system is a set of interconnected elements that interact with each other to produce emergent effects or collective behaviors that is distinct from the behavior of any of the subcomponents in isolation [ 24 ]. These effects persist over time and adapt to changing circumstances [ 32 ]. In the setting of technology use, the system could include factors such as physical symptoms or clinical outcomes, lifestyle and behavioral aspects of disease management, wellbeing and psychosocial changes, as well as individual preferences, social and environmental forces, and healthcare resources. Systems science offers methods that can model the structure and complex dynamics of systems (here, those affecting CGM use), while simultaneously looking for direct mechanisms between variables and important points of intervention [ 33 , 34 ]. Dynamic complexity is an emergent behavior of complex systems, and refers to situations in which effects over time are not easily explained through simple cause and effect, but rather represent the influences from multiple interacting factors that may be non-linear, occur over variable durations of times, and trigger powerful feedback loops to reinforce or counteract earlier changes within the web of interconnections [ 25 , 26 , 35 ].

We specifically aimed to generate a conceptual model of the larger hypothesized system of factors that interact to shape CGM use trajectories (and individuals’ embedded experiences) over time. The model can serve as a way to visualize key pathways where effective technology use and self-management break down, elucidate the problematic outcome trajectories and the constraints of real-life care and support systems, and identify opportunities for change that are aligned with individuals’ experienced system structure.

We thus explored how a participatory (i.e., method that engages stakeholders such as patients and caregivers) system dynamics method called GMB could be leveraged to understand the factors, feedback loops, and system changes that most affect CGM use over time [ 33 , 34 ]. GMB is a participatory approach to System Dynamics in which diverse stakeholders can exchange their perceptions and experiences to collectively consider the causes of a dynamically complex problem [ 30 , 35 , 36 , 37 ]. To our knowledge, no GMB studies have yet been published that adapt these methods to specifically engage older adults in improving clinical care by developing a better understanding of broad forces affecting their interactions with evidence-based medical technologies and clinical outcomes.

Materials and methods

We sought to apply GMB methods to collect data from older adults living with Type 1 diabetes and their caregivers, with considerations to accommodate logistical constraints (e.g., welcoming participants bringing heterogeneous clinical, personal, and professional backgrounds, and limiting the study duration to no more than three hours initially). With this approach, we aimed to bring a systems thinking framework and system dynamics techniques to represent and model the complex processes and outcomes of older adults initiating and using CGM over time and to uncover factors relating to sustained and effective use in daily life. As part of this study, we therefore also explored how systems thinking could be taught to older adult research participants.

Study design

We developed a facilitation guide and applied it within a series of small ( n  = 3–8), parallel GMB workshops to understand perspectives of older adults with Type 1 diabetes initiating and using CGM over time. The study included two main components: a series of three-hour, in-person, small-group GMB workshops and an optional follow-up series of one-on-one virtual validation interviews. Upon completion of the study, all participants received a $100 (USD) incentive for their time and effort. All study procedures were approved by the Institutional Review Board at the University of North Carolina at Chapel Hill (IRB Study # 21-2331). Participants provided written informed consent prior to participating in the study.

Study participants

Eligibility criteria.

For the in-person workshops, patient participants were eligible if: they had a diagnosis of Type 1 diabetes documented in the electronic medical record, were ≥ 65 years of age at the time of recruitment, used an insulin regimen of pump or multiple daily injections, were able to manage their diabetes independently or with the help of a caregiver, had a Hemoglobin A1c level measured within the past year of ≤ 10.0%, and comprehended written and spoken English. Patients were eligible to participate regardless of CGM use. Participants were ineligible if they had a significant medical or psychiatric condition that may have prohibited completion of the workshop, a clinical diagnosis of dementia, or were not fully vaccinated against COVID-19 at the time of recruitment (decreasing risk of transmission within in-person sessions during the pandemic). All potential patient participants were invited to bring a caregiver with them to the research study. Caregiver participants were eligible for the study if they were invited by participants living with diabetes and serve a ‘caregiver’ role in the sense that they provide daily or regular care or support with regards to specific aspects of care or daily management for an older adult (≥ 65 years) with Type 1 diabetes.

Recruitment

Study recruitment spanned November 10, 2022–December 13, 2022. Patients were recruited from a single outpatient diabetes clinic at an academic medical center. For the in-person workshops, potentially eligible participants were identified via the electronic health record system and contacted via email and telephone outreach. All interested participants were ultimately contacted by telephone following a standardized recruitment script in which participants were provided information about the study and invited to optionally bring a caregiver to the workshop. CGM use status and vaccination status were determined by chart review and confirmed verbally. Participants were scheduled for a workshop on a rolling basis and provided with a series of confirmation and reminder emails.

All participants of the in-person workshop were invited to participate in optional validation interviews; they indicated their preference in writing at the close of the workshop and provided an email address for further contact/questions. There were no incentives offered for the optional validation interview.

Group model building procedures

Each in-person workshop followed a uniform structure including: (1) completion of a brief questionnaire; (2) the GMB session, including both didactic components and structured activities; and (3) a brief focus group discussion. The workshop lasted three hours, with our agenda shown in Table  2 .

Each workshop was facilitated by two people, which always included the project lead/first author supported by a second facilitator (co-author or research assistant). The workshop was held in a moderate-sized conference room in an outpatient clinical care site with a designated meeting space for clinical research. The room included a large table, up to 12 chairs, a projector with HDMI connector cables, a screen at the front of the room, and ample wall space for posting study materials adhered with blue painters tape (i.e., wall-safe adhesive). Each study participant was provided with an assigned seat and a clipboard that contained the consent form and HIPAA authorization, the questionnaire, and the workshop packet. Each participant’s seat at the table was marked with a name tag, and there were multiple black and colored pens, two individual whiteboards and colored markers, and two small pads of sticky notes. Due to COVID-19 and the need to maintain masking, no beverages or food were provided, although participants were encouraged to take breaks to eat and drink as needed.

The workshop opening included open-ended prompts for introductions, an icebreaker, and sufficient time for study participants to interact and build rapport before we began the structured aspects of the workshop so that participants would feel comfortable sharing their views and brainstorming in a group. Following brief introductions of the research team, participants were asked sequentially to introduce themselves and describe, to the extent they were comfortable, their relationship to Type 1 diabetes. The introduction prompt was selected to allow for a range of possible responses, which may include narratives surrounding diagnosis, experiences with changing treatment regimens or self-management, attitudes towards Type 1 diabetes, and experiences as caregivers. A separate, informal icebreaker was chosen for each workshop, including, “What is your favorite Thanksgiving food?” and “What is your favorite ice cream flavor?” Although icebreakers are not a requisite aspect of GMB, providing participants with an open structure to tell their stories and allowing time for reactions from other participants early in the workshop aimed to facilitate group bonding to support the rest of the workshop activities.

Didactic component

Each workshop opened with a short presentation by the facilitator that included an overview of the rationale for the study, the goals for data collection, and a series of “ground rules.” The ground rules encouraged participants to share their thoughts freely (and to listen to others respectfully), to draw upon their experiences as the ‘experts’ in the room, and to take breaks as needed. An iceberg metaphor was used to introduce the concept of systems thinking [ 38 ], in which isolated events were framed as the ‘tip’ of the iceberg, while related and concerning trends (e.g., root causes,) and problematic aspects of underlying system structure and mental models were reflected as the part of the iceberg that was below the waterline (Fig.  1 ). Of note, the “Iceberg Model” represents a commonly used image to teach systems thinking by linking events to patterns of system behavior to underlying system structures and mental models [ 39 ].

figure 1

Didactic study components used to present systems thinking. Panel A shows the general framework, while Panel B shows its extension to understand Type 1 diabetes self-management experiences ( B ). The goal is to work down the iceberg to understand why events/outcomes are happening, and then to use this understanding to identify changes from the bottom up (i.e., in goals, values, and system structure) capable of improving outcomes and trends. Note: the ‘Iceberg Model’ [ 38 ] is a widely used approach for teaching introductory systems thinking [ 39 ]. The iceberg image is work by Uwe Kils. http://www.ecoscope.com/iceberg/ . Creative Commons CC BY-SA 3.0

The iceberg metaphor was then extended from a general framework to apply to Type 1 diabetes self-management (Fig.  1 B), in which participants were invited to help the research team understand the experiences that happen “below the waterline” as it relates to initiating and using CGM.

The facilitator provided an example of how the systems thinking framework would be applied, which focused on two hypothetical older adult characters in a relatable but distinct example, with the goals of making the method feel practical but not locking thinking into only what is presented in the example. In the example, the characters were friends who set the same New Year’s Resolution to walk 10,000 steps per day and had different outcomes over the following six months. The example was used to introduce two key GMB concepts, including drawing, discussing, and analyzing graphs of system behavior over time, as well as reference modes, which are depictions of real-world patterns of behavior over time that can be “referred to” (i.e., explained) as part of systems thinking exercises [ 31 ].

The example and didactic language are presented in full in Appendix A : Primer to Systems Thinking and Systems Mapping.

Reference modes

Following the example of systems thinking, the rest of the workshop focused on CGM use in older adults. We selected four reference modes, or real-world patterns of behavior over time, to reflect common trajectories of optimal and suboptimal CGM use over the first six months following initiation of therapy (i.e., consistently high use, moderate use increasing to high use, continually declining use, and intermittent use/oscillation). We aimed to present sufficiently different reference modes to capture the breadth of common real-world use patterns, while avoiding excessive or redundant trends that may contribute to participant fatigue (i.e., we strived to illustrate the smallest set of distinct reference mode shapes that would elucidate the breadth of qualitatively distinct feedback structures). Each reference mode was presented as a hypothetical older adult character ”persona,” which were used to introduce a 6-month behavior-over-time graph of CGM use (see Fig.  2 ). We defined CGM use (i.e., Y-axis) as both wearing the CGM and using the readings to make decisions for Type 1 diabetes management, such as ingesting carbohydrates or dosing insulin. Throughout the study, the research team referred to the reference modes by the name of the corresponding older adult character – with the goal of understanding each common behavior-over-time profile. For each reference mode, we strove to draw out stories about key feedback loops operating at different points of time as described in Fig.  2 (e.g., the reinforcing loop that might drive use up or down; balancing loops that slow change – either limiting improvement or counteracting undesired drops in use).

figure 2

Reference modes provided during the group model building workshop. The reference modes were presented as named characters representing older adults living with Type 1 diabetes who began using CGM as part of their diabetes management. The graphs show the probability of CGM use, defined as both wearing the monitor and using glucose information for diabetes management, over the first six months after CGM is introduced. Four reference modes were selected, including one to represent consistently high use (Stanley), moderate use increasing to high use (Patricia), continually declining use (John), and intermittent use (Wendy)

Behavior-over-time graphs

Following presentation of the reference modes, the workshop transitioned to drawing and group discussion activities. Behavior-over-time graphs were presented as graphs that focus on patterns of change over time, rather than on an isolated event or outcome, to help people and researchers think about how and why these changes are happening (Table  1 ). The facilitator introduced the concept of related trends, including guidelines for drawing and annotating trends, and suggested trend topics. Guidelines for brainstorming related trends included: (1) there are no right or wrong answers; (2) trends typically represented nouns or something that can increase or decrease over time unambiguously; (3) there is no need for a formal scale or measurement (i.e., it could be numbers/a specified range or a more qualitative range – low to high); and (4) trends can be consequences or causes of CGM use over time. The facilitator presented an example of how to draw a graph over time, carefully labeling the X-axis as “Time,” noting the start time and end time. The Y-axis was labeled with a variable name and scale, and the understood trend(s) was (were) drawn on the graph and annotated (i.e., reasons the trend changed at specific points in time were noted).

Over the course of the pilot study, we experimented with a range of approaches to encourage the drawing of behavior-over-time graphs. In the first four workshops, study participants were invited to use their Workbook Packet or personal whiteboards to draw their own CGM use patterns and related trends. In the latter five workshops, participants were asked to use their Workshop Packet to identify which reference mode best reflected their own CGM use pattern. Former-users were asked to select the graph which represented their experience, while never-users were asked to select their imagined experience. Participants were then asked to identify and draw three emotions and three benefits or challenges that changed over their first six or more months of using CGM. A sample workshop packet from the latter five workshops can be found in Appendix B : Group Model Building Workshop Packet.

In any workshop, the facilitator answered questions, clarified tasks, and encouraged participants to ask for help if they experienced confusion. If participants were unable to draw themselves, members of the research team offered to listen to their stories and draw behavior-over-time graphs on their behalf. Following drawing exercises, the facilitator led a discussion in which each participant was asked to share and ”annotate” (or explain) their drawings through storytelling and to react to other participant’s drawings and stories.

Collective annotation of the reference modes

To collect data for causal loop diagrams to model the system structure underlying common CGM use patterns, we applied a facilitated GMB process based on published scripted group exercises [ 30 , 40 ].

For each reference mode, the facilitator posed a series of open-ended questions meant to uncover key variables, causal linkages, and feedback loops explaining change over time to be represented within the causal loop diagram. Feedback loops are closed chains of causal connections, which can be reinforcing (i.e. when change in a variable triggers a series of changes or ”ripple effects” that ultimately loop back to drive further change in earlier variables) or balancing (i.e. when change in a variable causes a series of changes that ultimately loop back to counteract the effects of the earlier change) [ 41 , 42 , 43 , 44 ]. While reinforcing feedback loops can cause exponential growth or decline, balancing loops seek equilibrium within systems; feedback loops may have variable time delays [ 42 , 44 ]. None of these dynamics is innately good or bad; it depends on desired trends. Our probing questions related to the shape of studied reference modes are shown in Fig.  2 . The reference modes were displayed on 24-inch x 36-inch laminated posters around the room, and participants’ ideas were scribed onto small sticky notes and used to annotate the diagram. The facilitator highlighted the feedback thinking for all four reference modes. At the point where a feedback chain became closed, the research team checked with the entire group to see if the chain was correct and complete. Throughout, participants were encouraged to brainstorm together and react to ideas across the group.

Throughout data collection, the research team periodically assessed saturation of themes proposed during the collective annotation of the reference mode. Saturation was defined as the point when no new or original themes emerged. Recruitment ended when saturation was achieved and confirmed through one final meeting in which no new themes emerged.

Other data collection

Focus group discussion.

Participants were engaged in a brief focus group discussion at the end of the workshop to provide a final opportunity for sharing thoughts about CGM use in older adults. The focus group discussion was guided by the following four questions: (1) We just talked through four examples here today. Can you think of a story of CGM use over time (i.e., a new reference mode) that we haven’t talked about? (2) With all of this in mind, what do you think are the top three things that we should know, study, or change to help older adults have positive experiences using CGM? (3) When you think about the things you do to take care of your diabetes every day, what are the ways that CGM can help the most? (4) What are expectations and goals that caregivers, doctors, and other members of the care team could have that would be supportive for older adults when they use CGM?

Feedback on the research study

At the close of the workshop, participants were asked to use their Workshop Packets to provide feedback on the GMB session to understand how the group model building methodology was perceived among older adults with Type 1 diabetes. Participants were asked to rate their comfort level sharing all their experiences and thoughts (Likert scale; 1–5) and offered an opportunity to share in writing anything additional with the research team that they did not feel comfortable sharing with the group. Participants were also provided space to indicate what they liked and did not like about the workshop. Finally, they were provided with a brief ‘primer’ on systems thinking for optional take-home reading, which reinforced didactic content from the workshop and included additional information about causal loop diagrams (see Appendix A : Primer to Systems Thinking and Systems Mapping.).

Workshop packets were collected and scanned. Photography was used to capture individual and group drawings that occurred outside of the packets, as well as the collective annotations of the reference modes. Workshops were audio-recorded and transcribed. All data were de-identified for analysis.

Causal loop diagramming

Given overlap in variables generated through GMB across the four reference modes, the research team consolidated and merged data from each reference mode into one collective causal loop diagram depicting the factors, experiences, outcomes, and events that may interact to drive optimal versus suboptimal CGM use patterns over time (Table  1 ; [ 45 ]). As our goal was understanding lived experiences relating to CGM use, we established a system boundary as factors intrinsic to a patient, in a patients’ life (home, social, etc.), or their clinical care environments shaping their CGM use. We designed a core structure to capture factors relating to uptake of CGM and ongoing use of CGM, as well as a subset of ‘endogenous’ drivers of CGM use — factors that affect use and are affected by use (i.e., they are contained within feedback loops that also contain CGM use). Often causal linkages emerged across narratives, but operate in different directions (with directions of initial change determining consequences, and ultimately driving increases or decreases in CGM use). The nature of the relationships between variables was indicated by marking polarity on arrows; an “S” indicates the factors move in the same direction (an increase/decrease in the first variable triggers an increase/decrease in the second) whereas an “O” indicates the variables move in opposite directions (an increase/decrease in the first factor triggers a decrease/increase in the second). In cases where participants’ direct language was deemed to be the most accurate representation of a sentiment or concept in the map, in vivo factors were used.

Validation of the diagram

A key component of stakeholder-engaged systems science involves iterative refinement and updating of system models with new or changing information [ 32 ]. As explicit diagramming was not a part of the in-person workshop, we elected to validate our diagram in follow-up interviews. Participants of the in-person workshop were offered the opportunity to review final causal loop diagram components and offer their feedback through an individual, virtual follow-up interview. The objective of this validation scheme was to ensure that diagrams retained fidelity to the raw data and lived experiences of study participants. Because the full causal loop diagram included many variables and feedback loops, different components of the map were validated in detail with different participants. Validation interviews were 30-minutes and followed a standardized script including a brief overview of the objectives of the research study, a narrative overview of main findings, a viewing of the full causal loop diagram, and a “step-by-step” walk-through of the overall diagram structure and one detailed segment of it (i.e., a subset of loops). Feedback was structured around the following questions: (1) What are your reactions to the full system map (causal loop diagram)? (2) What part of the focused diagram resonates most? (3) What pieces of the focused diagram are the most important in determining CGM use over time? (4) What is missing from the focused diagram that feels as or more important? This may include making changes such as adding factors, removing factors, or drawing new connections between factors. Participant feedback was scribed. Validation interviews were performed as dyadic interviews when caregivers were also present. The causal loop diagram was revised iteratively over the course of conducting interviews.

Ethics approval and Informed Consent : Ethical approval for this study was obtained from the Institutional Review Board at the University of North Carolina at Chapel Hill (IRB Study # 21-2331). Participants provided written informed consent prior to participating in the study.

We adapted GMB methods, a participatory approach to system dynamics, to model experiences and trajectories of CGM use among older adults with Type 1 diabetes. We completed nine in-person GMB workshops with older adult patients and their caregivers and generated an integrated causal loop diagram. An in-depth description of study participants and the resulting causal loop diagram are provided elsewhere [ 45 ].

Herein, we illustrate data collected through each component of the GMB process, as well as other evaluation measures including recruitment outcomes and feedback on the study from participants as a form of process evaluation.

Recruitment and attendance

Nine workshops were held between November 15, 2022 and December 15, 2022. Each workshop had between two and six participants. A total of 33 older adults and caregivers participated, of which four were caregivers and the rest were individuals living with Type 1 diabetes. The mean age of the sample was 73.3 ± 4.3 year, with a range of 66–85 years. 55% identified as women, 82% identified as non-Hispanic white, and 12% were non-CGM users.

During recruitment, the main reasons cited for lack of interest or inability to participate included competing medical or surgical appointments, conflicts relating to the winter holidays, and non-local temporary or permanent residence. There were two major challenges for recruitment of an adequately diverse sample. First, the majority of eligible participants for the study within the medical center were non-Hispanic White race and ethnicity. The imbalanced recruitment pool was reflected in a study sample that was majority White and non-Hispanic. Second, there was a small number of eligible participants who were not CGM users, resulting in the majority of study participants being active CGM users.

Attendance of the workshop by recruited individuals was relatively high. A total of four participants did not show for their scheduled workshop. One of those participants was rescheduled for a subsequent workshop, two were unable to be rescheduled, and one was not successfully re-contacted.

Individual drawing activities proved to be the most challenging aspect of the workshop for study participants, particularly when the drawing prompts were left open-ended. Approaches that facilitated older adults drawing included providing an example of a drawing, pairing a participant with a facilitator to draw on their behalf and in response to their storytelling, and providing more specific prompts, such as asking for graphs of named emotions associated with using CGM or benefits yielded over the first six month. Figure  3 depicts a sample of drawings of emotions and benefits from study participants, including both users and non-users of CGM.

figure 3

Selected illustrative examples of raw data generated as part of the in-person group model building workshop. Panel A shows a subset of individual behavior-over-time graphs drawn by older adults living with Type 1 diabetes and their caregivers. Panel B shows the collective annotations of the “moderate use increasing to high use” reference mode

When not all study participants felt comfortable drawing, we found that those who did tended to lead storytelling, which revealed complex dynamics, catalyzing rich group discussions that were captured in the study transcripts and coded for inclusion in the final causal loop diagram. These discussions would draw other participants into the discussion, and also contributed to a significant amount of group bonding, particularly when participants found resonance in their drawings or stories.

Workshop participants were successfully engaged through a series of discussion questions to elicit information necessary for causal loop diagramming. Figure  3 B shows an example of the raw data produced by collective annotation of the reference modes. Although some themes were constant across groups, different groups generated unique collective ideas, resulting in rich heterogeneity in the annotations across groups. Saturation was achieved in the themes of group annotations by the eighth workshop and confirmed through completion of the ninth workshop.

Study feedback

Participants expressed that they felt comfortable sharing their thoughts and experiences in the group format, where the mean comfort level was rated as 4.97 on a scale of 1–5 (5 is the highest; n  = 26 respondents), citing open discussion, a safe and/or welcoming environment, and a clear explanation of how the data will be used as the main reasons for comfort. Several participants expressed value in creating dedicated space for older adults to discuss age-specific aspects of Type 1 diabetes management: “There is great value in listening to the not so good outcomes as we old ducks struggle with the technology…. How best to supply thoughtful, personal support to overcome hesitancy and get folks to actually embrace the technology [Workshop 1, Participant 2].” One participant additionally shared with the research team, “Having the experiences of others was most helpful. Being diabetic sometimes makes you feel alone. Getting to share with your age group was therapeutic [Workshop 3, Participant 4].” Other participant feedback on the GMB workshop is summarized in Table  3 , in which participants indicated they liked sharing information and learning from others, finding peer support through shared experiences, the structure and pace of the workshop including small group sessions, age-specific discussions, the systems thinking framework, and the opportunity to contribute to research. Key aspects of feedback for future sessions focused on the duration of the session (where some participants indicated they would like a longer session and other indicated preference for a shorter session) and the lack of food or beverages provided by the research team.

The four focus group questions provided an opportunity to collect any last feedback or perspectives from study participants, but in general, did not reveal new themes or dynamics underlying CGM use that had not been identified through the preceding GMB activities. Participants expressed the sentiment that they had already shared their material they felt to be important relating to CGM use and non-use in older adults.

Validation interviews

27 of the 33 in-person study participants indicated that they were interested in providing feedback on the diagram. Following completion of the causal loop diagram, eight virtual validation interviews were conducted between March and April 2022. Each interview focused on validating a specific component of the diagram, including the core structure (i.e. the central drivers that impacted CGM use among older adults) and the key feedback loops. Revisions made in validation were minor (i.e., modification of variable names and addition of missing components) and included (1) connecting reactions from caregivers to a perceived sense of intrusiveness, (2) an additional variable to indicate that alarm fatigue would be driven by frequency of alarms, and (3) clarifying that improved HbA1c is associated with a sense of prolonging life alongside preventing complications, both of which promote future CGM use.

We applied GMB, a participatory approach to system dynamics modeling, to collect data from older adults with Type 1 diabetes and their caregivers through group workshops and individual validation interviews to learn about their experiences using technology as part of glucose monitoring regimens. Compared to standard approaches such as surveys, interviews, or focus groups, this systems thinking approach is able to capture the complexity of multiple, interconnected variables that are relevant to an older adult’s experience using CGM, including the feedback loops capable of dramatically impacting long-term CGM use by reinforcing or counteracting earlier changes – sometimes for better and other times for worse. Though the systems thinking approach and methodologies may not be familiar to the readership of clinical journals, there is a growing interest in how systems science methods can be applied to population health and clinical research [ 33 , 34 ]. Given the richness of our findings and the value the approach added above and beyond more typical approaches (e.g., interview, survey, focus group), we aim to disseminate our approach to introducing and using systems thinking and systems science to diagram the diverse, interrelated factors affecting sustained use of evidence-based technologies in older adult populations.

To our knowledge, few studies to date have extended participatory systems science methods to engage older adult patients. Thus, we consider our study to be a substantial contribution to the literature in that it demonstrates feasibility, acceptability, and value of this methodology to engage older adult patients and stakeholders in research relevant to their health and well-being. This is thus a highly novel study in the field of qualitative systems science approaches as it relates to increasing the diversity of patient stakeholders who are included; older adults have historically been excluded from many clinical research projects. It is unclear why there are so few other studies to employ this method among older adults. One possibility is that the cognitive complexity of GMB may not be seen as feasible among older adults. A recent editorial in the Journal of the American Medical Association stated that “structural, or institutional, ageism is not only one of the most potent forms of bias that exists today, but also one of the least acknowledged ” [ 46 ]. The authors proceed to cite a report from the World Health Organization that underscores the long-standing history of institutional ageism and the ways in which ageism has become normalized across many domains both in and out of healthcare [ 47 ]. A key finding of our study, where we explored best practices to teach systems thinking to older adult research participants, was that the didactic component of the study was well-received, and participants expressed positive feedback for the systems thinking framework, with more than 80% indicating interest in further engagement (i.e., providing feedback on the causal loop diagrams produced). By contrast, the most challenging aspect of the study involved strategies to encourage drawing of behavior-over-time graphs to describe personal experiences with CGM. Future studies that apply GMB with older adult stakeholders will continue to shed insight on how the methodology can be made most accessible and best leveraged to elevate older adult voices as part of the clinical literature that informs future interventions for care and self-management.

Several aspects of the GMB study proved to be effective. The single-session, three-hour long workshop was associated with efficient recruitment and high attendance rates. Although we explored GMB in varied group sizes, we found that data collection was optimized at a group size of between four and six participants, as this size allowed for sufficient exchange of ideas but provided enough time for each participant to speak. Presenting an example of the systems thinking approach as part of the didactic component helped to solidify the framework and prepare participants for the activities to come. We intentionally presented an example focused around a potentially stigmatizing lifestyle change—increasing physical activity—to implicitly reinforce the value of GMB for diving deeper into clinical outcomes or trajectories that may be similarly stigmatized in clinical settings. We hoped that by showing the complexity underlying individual health-related decision-making, that participants would feel comfortable exploring the deeper trends that led to “less than ideal” CGM monitoring. We also drew and provided reference modes for the study, rather than asking study participants to brainstorm and co-create them. In the context of our research question, as well as the time-constraints and need to avoid participant fatigue, we found this approach to be effective. Further, personalizing the reference modes through named older adult characters helped to bridge the graphs to storytelling as part of the collective annotation.

A powerful aspect of the in-person workshops involved the ways in which participants and groups bonded over the duration of the study. We found that bonding occurred regardless of differences in demographics or clinical histories and was largely driven by resonance in day-to-day experiences in managing Type 1 diabetes or age-specific changes in older adulthood. The value of this peer support was reflected in study feedback. From a study design perspective, allowing time and space for long-form introductions prior to the structured presentations and activities was critical. Participants were generally eager to exchange tips or resources for Type 1 diabetes management and effective CGM use, although the research team always stressed the importance of talking with a healthcare provider about any clinical changes to their healthcare plan. Several participants expressed interest in continuing to meet with other participants in their workshops to continue conversations and the exchange of information and experiences.

There were several aspects of the study that were more challenging. One of the major limitations was the ability to recruit an adequately diverse sample to reflect the heterogeneity of the population of older adults living with Type 1 diabetes. The limited number of participants from underrepresented racial and ethnic groups (e.g. those who identified as Black and/or Hispanic) and non-CGM users likely reflects a combination of selection bias related to recruiting from a single academic medical center, as well as a degree of survivor bias in which individuals with diabetes who did not have access to high-quality treatment or the ability to self-manage effectively earlier in disease duration were not represented; in cohort studies, excess mortality associated with Type 1 diabetes has been shown to disproportionately affect African American individuals compared to their White counterparts [ 5 ]. Given the in-person component of the study, the majority of study participants were also local to the city in which the academic medical center was located. Further, the selection bias associated with the COVID-19 vaccination requirement likely represents a complex medical and social bias, the effect of which on the study findings is difficult to characterize. Future work to include more diverse participants within group sessions is critically needed to ensure that conceptual models and diagrams are valid for this population.

There are other limitations to external generalizability of the findings as our study did not include older adults with cognitive, visual, or hearing impairments, or HbA1c greater than 10%. Future work is needed to engage populations with additional health challenges that may complicate diabetes management, as well as the subpopulations for whom hypo- and hyperglycemia represent major clinical issues. Because GMB focuses on working together to describe stories, having a group that shares the same language is also a requirement, further narrowing our sample and thus the overall representativeness of this study.

Future GMB studies for older adults may consider the following points of learning. First, data collection may be enriched with regards to underrepresented views, including one or more group study sessions dedicated to capturing the experiences of such individuals, without dilution or bias from influence of subgroups that tend to be over-represented [ 48 ]. In our study, a small proportion of older adults living with Type 1 diabetes elected to bring a caregiver with them to the study. Future studies which aim to integrate diverse stakeholder perspectives, such as those from caregivers, may consider recruiting these stakeholders independently for participation in a focused workshop of caregiver participants only. Although we included a brief focus group discussion as part of our workshop, we found that participant responses were very brief or largely redundant with data collected through the drawing and annotation exercises; thus, this section of the study did not significantly expound upon or enrich data. We believe this reflects, in large part, the open-ended nature of the preceding GMB activities and vigorous group discussion. Finally, in review of the transcripts of the study, we found that participant narratives yielded significant contextual information, personal narrative, or other ‘foreground’ for clinical questions that are well-suited for inductive qualitative analysis techniques. Our research team aims to explore how the results from system dynamics analyses and varied qualitative analyses [ 49 ] can be triangulated to provide a complementary, comprehensive view of the lived experiences of older adults with Type 1 diabetes, as well as their complex experiences with technology.

There are several questions remaining. It remains unclear how best providers can be integrated into GMB, and if the potential for power dynamics between patient and provider stakeholders may influence the quality of GMB in mixed groups. Recruiting patient and provider stakeholders across institutions or holding provider-specific workshops may avoid this problem, although validation between groups may prove more challenging for the latter. It is also unknown how the causal loop diagrams may change if older adult participants are directly involved in the diagraming process, and how the diagrams would change between sessions. Studies that include older adult participants in direct diagraming may need to explore various study formats to ensure that burden remains low, and participants do not experience significant fatigue. Given that the number of feedback loops in our causal loop diagram exceeded 100, it is likely that generating comprehensive diagrams with participants directly may involve more than one workshop, thus increasing the burden of the research study and potentially hampering recruitment outcomes. Alternatively. research teams might explore the potential of having workshops build on earlier diagrams, adding content believed to be as or more important as what was included in prior sessions.

It has been argued that systems thinking and systems science methods, such as system dynamics, remain underutilized for complex health problems [ 33 , 34 , 50 , 51 ]. For the study objective, which demands an awareness of more dynamic complexity than traditional research methods [ 34 ], GMB provided a novel approach among older adults to comprehensively investigate and describe multiple, interrelated factors that determine the uptake and use of medical technologies, as well as their complex interactions over time. The holistic view of experiences as the behavior of a complex system offers the opportunity to not only describe, but start to untangle the mechanisms that shape older adults’ experiences with technology and how it fits into broader chronic disease self-management [ 42 , 52 ]. In related work, our team used the causal loop diagrams to identify outcome sets which represent ‘suboptimal CGM responses’ that signal the need for additional resources, education, or support, as well as the system structure of the factors that interact to produce each response; we will use this problem definition as the basis for efforts to develop new strategies to address and prevent suboptimal trajectories associated with CGM use in older adults with Type 1 diabetes [ 45 ]. We are also enthusiastic to extend the GMB methodology, and integrate lessons learned in the present study, to continue to engage older adults as primary stakeholders in research to promote the access to and use of medical technology for longevity and healthy aging across a range of clinical contexts.

Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author, ARK. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Abbreviations

Continuous Glucose Monitoring

Group Model Building

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Acknowledgements

We are grateful to the participants of the CGM Older Adult Stakeholder Mapping Workshop research study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Damilola Ayinde, Kabir Dewan, Maya Loga, and Sharita Thomas provided research assistance during the workshops.

ARK is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The project was supported by UL1TR002489 from the Clinical and Translational Science Award program of the Division of Research Resources, National Institutes of Health and the Diabetes Research Connection.

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A.R.K., L.A.Y, and K.H.L contributed to the conception and design of the research study. A.R.K and C.S. contributed to the acquisition of and analysis of the data. A.R.K. drafted the initial manuscript text and prepared the figures. All authors contributed to interpreting the data and revising the manuscript for important intellectual content. All authors approved the manuscript.

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Kahkoska, A.R., Smith, C., Young, L.A. et al. Use of systems thinking and adapted group model building methods to understand patterns of technology use among older adults with type 1 diabetes: a preliminary process evaluation. BMC Med Res Methodol 24 , 126 (2024). https://doi.org/10.1186/s12874-024-02252-z

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Navigating the future of Alzheimer’s care in Ireland - a service model for disease-modifying therapies in small and medium-sized healthcare systems

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A new class of antibody-based drug therapy with the potential for disease modification is now available for Alzheimer’s disease (AD). However, the complexity of drug eligibility, administration, cost, and safety of such disease modifying therapies (DMTs) necessitates adopting new treatment and care pathways. A working group was convened in Ireland to consider the implications of, and health system readiness for, DMTs for AD, and to describe a service model for the detection, diagnosis, and management of early AD in the Irish context, providing a template for similar small-medium sized healthcare systems.

A series of facilitated workshops with a multidisciplinary working group, including Patient and Public Involvement (PPI) members, were undertaken. This informed a series of recommendations for the implementation of new DMTs using an evidence-based conceptual framework for health system readiness based on [1] material resources and structures and [2] human and institutional relationships, values, and norms.

We describe a hub-and-spoke model, which utilises the existing dementia care ecosystem as outlined in Ireland’s Model of Care for Dementia, with Regional Specialist Memory Services (RSMS) acting as central hubs and Memory Assessment and Support Services (MASS) functioning as spokes for less central areas. We provide criteria for DMT referral, eligibility, administration, and ongoing monitoring.

Conclusions

Healthcare systems worldwide are acknowledging the need for advanced clinical pathways for AD, driven by better diagnostics and the emergence of DMTs. Despite facing significant challenges in integrating DMTs into existing care models, the potential for overcoming challenges exists through increased funding, resources, and the development of a structured national treatment network, as proposed in Ireland’s Model of Care for Dementia. This approach offers a replicable blueprint for other healthcare systems with similar scale and complexity.

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Alzheimer’s disease (AD), the most prevalent type of dementia, constitutes approximately 70% of all dementia cases globally among people aged 60 and above. Projections suggest that the current 35–40 million individuals affected by AD worldwide will rise to at least 100 million by 2050, with substantial implications for individuals, their families, and healthcare expenditure [ 1 ]. Age stands out as the primary risk factor for AD, indicating a heightened vulnerability due to the aging population, and representing a significant gap in medical care [ 2 ].

Until 2021, there was no licenced treatment to delay or slow the progression of the neurodegeneration that characterises AD [ 3 ]. However, over the past decade, evidence supporting the potential for dementia prevention through risk reduction has increased [ 4 ], alongside a growing pipeline of medications with the potential for disease modification [ 5 ], targeting the very earliest stages of AD, including mild cognitive impairment (MCI) and the early-stage dementia. Of the current ongoing trials of 126 agents for AD worldwide, about 80% are classified as ‘disease modifying therapties’ (DMTs), as opposed to symptomatic therapies for cognitive enhancement or management of neuropsychiatric symptoms [ 5 ]. Current potential DMTs for AD include human monoclonal antibody-based agents targeting beta amyloid.

Recently, regulatory approval was obtained in both the United States of America (USA) and Japan for two anti-amyloid monoclonal antibodies showing potential DMT properties: aducanumab and lecanemab. Aducanumab underwent review by the European Medicines Agency, but did not secure approval for use in Europe. Conversely, lecanemab is currently undergoing regulatory review in Europe. Additionally, recent data have revealed promising results for another DMT candidate, donanemab, which is progressing towards approval. Notably, all anti-amyloid monoclonal antibody DMTs currently approved or on the approval pathway require biomarker confirmation of the diagnosis of AD through demonstrating ‘β-amyloid positivity’, using either PET-ligand neuroimaging, or amyloid-tau ratio cut offs from cerebrospinal fluid, obtained via lumbar puncture [ 3 ]. Plasma-derived levels of tau/β-amyloid are not yet approved for clinical use. The early evidence of these monoclonal antibody treatments is associated with potentially high-risk side effects such as brain oedema or microhaemorrhages (i.e., amyloid-associated imaging abnormalities; ARIA) which require serial monitoring with brain MRIs and close clinical follow-up during treatment [ 6 ]. This represents a departure in the current approach to AD, warranting the urgent need to ascertain care pathways for these new drugs. Although these medications have yet to receive licencing approval outside of the USA and Japan, a policy analysis on how such DMTs could be incorporated into existing services is also needed.

Ireland as a case study

In Ireland, a MCI prevalence estimate of 6% of adults over age 60 years is accepted [ 3 ], suggesting that about 57,000 of people in this age group may have MCI, although the MCI may not always be due to an underlying neurodegenerative disorder. Nonetheless, this number, together with those with early-stage AD dementia, represents a significant number of people who may benefit from approaches to prevent or delay the onset of dementia. Considering the specific inclusion criteria for the current DMTs, it is estimated that up to 20,000 people might qualify. However, when patients present to memory clinics with subjective memory complaints (SMC), or MCI, they are often discharged back to primary care without further support or intervention. Thus, it is imperative to consider systematic approaches to managing early-stage cognitive decline, since evidence suggests early interventions are associated with larger clinical benefits [ 7 ], and foster potential for access to DMT. Details of how the estimates for potentially eligible patients in Ireland were arrived at are outlined in Supplementary Material.

Memory clinics, initially established as tertiary referral medication-management clinics, were introduced to the National Health Service (NHS) in the United Kingdom in the 1990’s, when cognitive-enhancing medications for AD were first licensed. However, memory clinics are now specialist centres that diagnose and treat memory disorders, including dementia. Until recently, Ireland had only 25 such memory clinics, across 13 counties, but many operated less than weekly and were cohorted medical clinics, without a full range of disciplines. Currently, however, driven by Ireland Health Service Executive’s National Dementia Office (NDO), there are plans to expand diagnostic capability significantly over the next five years. This will add new services, as outlined in the recently launched national ‘Model of Care for Dementia in Ireland’ [ 8 ].

This new Irish model of diagnosis and care for AD, and other forms of dementia, was informed by the European research project “ACT on Dementia” [ 9 ]. The model describes a three-tiered national service, increasing the number of existing local Memory Assessment and Support Services (MASS;level 2), which are supported by primary-care led diagnostic services for low complexity cases (level 1), and by new Regional Specialist Memory Clinics (RSMC; level 3) for higher complexity cases. The RSMC focusses on complex diagnoses, while the MASS provides a full range of services, including brain health and post-diagnostic management and support. Under this new model, neurology, psychiatry of later life and older person’s medicine (medical gerontology) provide integrated services for diagnosis and post-diagnostic care.

The Model of Care for Dementia [ 8 ] includes one MASS per local population of 150,000 people (i.e., three Community Health Networks), and a minimum of five RSMCs nationally, with at least two of these based outside Dublin. The first phase of this expansion has taken place, with ten new MASS clinics and two new RSMCs, across the country, funded in 2021 and 2022. Prior to this, existing memory clinics had limited capacity to diagnose, administer and monitor complex new therapies such as DMTs, particularly regarding the anticipated increased need for biomarker ascertainment and safety monitoring including radiological surveillance. Thus, careful consideration regarding the requirement to deliver a new DMT service are outlined below, addressing minimal service requirements.

Local service audit of the prevalence of patient anti-amyloid treatment eligibility

Since the prevalence estimates of potential DMT recipients are based on numerous assumptions, we present audit data, including CSF biomarker data collected from 184 patients between 2017 and 2023, from a local MASS in Tallaght University Hospital, Ireland. Of these patients, 39.6% (73/184) were positive for AD biomarkers (low AB-42 and high P-Tau), 25% (46/184) were negative (both AB-42 and P-Tau in normal range), and 35.3% (65/184) were indeterminate (i.e., one of low AB-42 or high P-Tau) [ 10 ]. Retrospective case note review was available for 70 CSF-positive patients with AD. Of these, 40 (57%) met potential eligibility criteria for aducanumab therapy by ‘Appropriate Use Criteria’ guidelines [ 11 ]. Thus, we can conclude that over half the patients with positive AD biomarkers presenting with prodromal to early-stage AD to a MASS, may be suitable for DMTs based on current treatment indications [ 12 ]. We note that patients receiving anti-coagulation therapy generally do not undergo lumbar punctures and thus would not be offered anti-amyloid DMTs.

In 2022, the NDO in Ireland convened an Expert Reference Group (ERG) on preparedness for the imminent licensing of new DMTs for AD. Here, we report on the findings of the group’s workshop-style discussions in the form of a blueprint for the implementation of DMTs for prodromal (i.e., MCI) and early-stage dementia due to AD in Ireland. This blueprint extends existing and more detailed guidance on the infrastructure required for the administration of DMTs for AD [ 11 , 13 ] Footnote 1

Approach and framework

Facilitated workshops were convened by Ireland’s NDO, following a qualitative research model. The workshops were attended by an 11-member multidisciplinary ERG. The aim of the workshops was to scope the current capability and capacity of MASS/RSMC in Ireland, to project demand for DMTs based on Ireland’s population and the current prevalence of AD, and to define system readiness for the introduction of the DMTs. Additionally, potential challenges to developing such a service were identified. The discussions were informed by a new conceptual framework of health system readiness described by Palagyi et al., 2019 [ 14 ]. Originally devised to assess system preparedness for emerging infectious diseases, the framework, consisting of six core constructs, also has utility for the proposed DMT service. Four of the constructs focus on material resources and structures (i.e., system ‘hardware’), including (i) Surveillance, (ii) Infrastructure and medical supplies, (iii) Workforce, and (iv) Communication mechanisms; and two constructs focus on human and institutional relationships, values and norms (i.e. system ‘software’), including (i) Governance, and (ii) Trust.

The ERG consisted of geographically dispersed individuals experienced in various facets of AD care in Ireland, including an academic geriatric psychiatrist, a specialist trainee geriatrician, two consultant geriatricians who are memory clinic leads, a consultant cognitive neurologist, a GP with special interest in cognitive health, two health economists, a memory clinic specialist nurse practitioner, and a representative from the HSE and third sector partner, the Alzheimer Society of Ireland, representing people with lived experience of AD and their care partners. Five workshops were held serially, either face-to-face or remotely and were facilitated by a chairperson.

Data collection and analysis

Workshops were recorded and field notes obtained for subsequent narrative and descriptive analysis. Modelling of projected demand for DMTs was informed by current national and international prevalence data of prodromal (MCI) and early-stage dementia due to AD, AD biomarker positivity, and other factors relevant for DMT eligibility.

Purpose of an early diagnosis and DMT intervention service for AD

The ERG agreed that a new DMT service for AD should be fully embedded in existing or proposed RSMC units. As such, it would be an additional layer of service integrated into the pathway for diagnosis, initial care planning, and post-diagnostic interventions. Patients would retain close ties with their local MASS to access brain health support, and the full range of post-diagnostic interventions, in parallel with the provision of DMTs. Table  1 outlines the specific purposes of the DMT service.

Material resources and structures (‘system hardware’)

Surveillance to detect early disease.

Early detection and monitoring of progression of cognitive decline in the earliest stages (prodromal AD or early-stage dementia due to AD) should be a key element of DMT preparedness, supported by evidence of the potential effectiveness of disease modifying approaches prior to moderate- or advanced-stage dementia. This requires early detection at the clinical level, but also greater public awareness and health literacy for timely help seeking.

Early clinical detection

Ideally, blood-based biomarkers, available in primary care, would be available to enable early detection. Since such biomarkers are as yet not widely available in clinical settings, raising awareness amongst the public and primary care providers needs to take place to foster early clinical detection. Importantly, initiatives to introduce widespread cognitive screening for older people in the UK were not supported [ 15 ] due to the risk of over-diagnosis and the lack of meaningful interventions in the early stages of AD. With the advent of DMTs, such screening initiatives may need to be revisited.

Public awareness and health literacy needs

To enable successful implementation of the new DMTs, it is important to harness political willpower by presenting DMTs as a public health investment, rather than a cost, with a clear narrative around economic savings that could accrue from delaying conversion to or progression of dementia. Economic data specific to Ireland is currently not available. However, a model developed in the USA to assess the impact of DMTs on AD highlights substantial benefits [ 16 ]. It predicts that a 5-year delay in the onset of AD, leading to a 25% reduction in its prevalence by 2050, could result in cumulative savings of over $3 trillion between 2022 and 2050 [ 16 ].

Research at a national scale that captures public perception, expectations, and concerns about DMTs is also required, and any public health literacy campaign should be designed in collaboration with stakeholders, including PPI contributors, capitalising on existing resources (e.g., Ireland’s ‘Understand Together’ dementia campaign). Finally, public health messaging should highlight the importance of an early diagnosis, while managing expectations regarding the efficacy and narrow eligibility criteria for DMTs.

Infrastructure and provision for an early diagnosis and intervention service for AD

The EGR debated the utility of a distributed versus centralised service model for DMT delivery. One commonly used model in Irish healthcare is a ‘hub-and-spoke’ model (e.g., in hyper-acute stroke care), which can provide a practical compromise, as shown in Fig.  1 . This entails one or more RSMC acting as a central hub, ideally in different regions of the country, and local MASS acting as spokes across the country. Rolling out DMT provision in one or two highly resourced pilot sites in the first instance, rather than starting with a fully distributed service, would ascertain eligibility rates and DMTs uptake, and develop experience around the administration and monitoring of DMTs. In the meantime, existing MASS services (spokes) will see relatively small numbers of people potentially eligible for DMTs. Thus, local referral pathways will be needed from these services to a nearby RSMC, or a MASS that has elected to provide DMTs in the first wave, so that the person can have detailed eligibility assessment and access to a DMT.

figure 1

Schematic of infrastructure and provision for an early diagnosis and intervention service for Alzheimer’s’ Disease (AD) in Ireland

Minimal service capacity to deliver DMT

The minimal service capacity requirements to deliver DMTs are detailed in Table  1 and include: [ 1 ] capacity to ascertain biomarker status for AD; [ 2 ] medication administration infrastructure (e.g., infusion facilities); [ 3 ] sufficient MRI neuroimaging capacity for both diagnosis and monitoring; and [ 4 ] links to brain health pathways and post-diagnostic support.

Patient referral criteria to the DMT service

It was agreed that the service providing access to anti-amyloid DMTs would identify its own pool of eligible patients within its MASS/RSMC remit, as well as accepting referrals from other MASS, diverting a patient directly to the MASS/RSMC or performing an initial assessment/full diagnosis and disclosure prior to referral. Suggested patient referral criteria to the DMT service are listed in Table  1 . Patients would not be accepted into the service if they had non-degenerative cognitive impairment due to another identified cause at the point of referral (e.g., depression, alcohol, or drug misuse, Vitamin B12 deficiency, thyroid disease, and others). If cognitive complaints persisted after effective treatment of a primary illness, then referral could be considered.

Components of the DMT intervention pathway

The DMT arm of a memory service would have three main components: [ 1 ] assessment and diagnosis; [ 2 ] intervention administration; and [ 3 ] ongoing monitoring and care . These components would run parallel to a brain health clinic model offered by local MASS services and are also outlined in Table  1 , along with a list of eligibility criteria for DMT, which align broadly with the inclusion criteria of the relevant clinical trials of these same DMT [ 3 ]. It is noted that at the point of referral to local MASS, it is expected that a basic medical and cognitive work-up will have been completed in primary care to rule out reversible causes for cognitive complaints (e.g., alcohol, or drug misuse, vitamin B12 deficiency, thyroid disease, and others) and to establish that the patient is in the prodromal or mild dementia stage. Once a referral has been accepted, a detailed assessment including biomarkers would be undertaken, as summarized in Table  1 under the first component of the service, ‘ Assessment domains . This will ascertain diagnostic sub-type and prognosis of early-stage cognitive decline. The assessment would include clinical, lifestyle, behavioural, functional, and cognitive assessments as well as biomarker detection. Key biomarkers, as recommended under the International Working Group (IWG-2) criteria for AD [ 6 ] are briefly outlined in Table  1 . Specifically, these criteria define the clinical phenotypes of AD (typical or atypical), integrating pathophysiological biomarker consistent with the presence of AD into the diagnostic process [ 17 ]. The use of biomarkers in the diagnosis of AD in the prodromal stage has altered the characterization of AD from being a syndrome-based diagnosis to a biologically-based diagnosis. A biomarker-based approach will support more personalized therapeutic approaches to the prevention of aging-related brain disorders, taking individual biological, genetic and cognitive profiles into account [ 18 ].

The outcome of the assessment will enable patients to be assigned to one of three risk-based ‘streams’: [ 1 ] begin the DMT care pathway; [ 2 ] be refered to local MASS for brain health pathway or relevant care for non-AD neurodegenerative disorders; and/or [ 3 ] be referred for a research study. Details of an approach to brain health management along with the new DMTs has been outlined elsewhere [ 13 ]. Under the ‘ intervention administration ’ domain, guidelines for rationalization of prescribed medications, managing comorbidity, and pharmacological and non-pharmacological interventions are outlined, and under the ‘ ongoing monitoring and care ’ domain, guidelines for periodic MRI monitoring and possible cessation of therapy are listed. Coordination of care is an important, yet challenging, issue. Ideally, an ‘early diagnosis navigator’ is needed to ensure all patients receive optimal care and foster a pathway back to the main clinic should they be diverted down an alternative path, such as research, brain health, or DMT.

Workforce: roles and education

Roles and expertise required.

The availability of frontline healthcare workers in sufficient numbers and with appropriate training and expertise to administer and monitor DMTs is a key feature. Whilst the specialist assessments and initial interventions will be undertaken by members of the core MASS/RSMC team, they will link in with a range of services and providers, both internal and external, as per the Model of Care for Dementia in Ireland. The MASS/RSMC team should meet regularly in person or virtually for multidisciplinary team (MDT) meetings. Finally,, there are additional MDT roles specifically for DMT provision, which exceed the role of the MDT as outlined in the Model of Care for Dementia (see Table  2 ).

Education and training of workforce

Delivery of an accurate diagnosis, identification of suitable treatment candidates, and monitoring of ongoing DMTs will require a standardised training program. Clinicians from multiple specialities (older person’s medicine, psychiatry of later life, neurology, general practice) involved in the work-up and diagnosis of dementia will require training delivered via the Royal College of Physicians Ireland, Irish College of General Practitioners, and the Irish College of Psychiatry. This would involve the application of AD biomarkers and therapeutic indications for prescribing DMTs. Radiologists will require training in the interpretation of evolving imaging modalities supporting the diagnosis of AD and other dementia subtypes. Training for clinicians on the identification of adverse drug reactions (e.g., ARIA) will be required and delivered as an ongoing iterative process as the field advances.

Communication mechanisms

Considering the hub-and-spoke model across the geography of Ireland, robust, timely, and standardized communication between the hub and spokes is necessary for ensuring patient safety. Additionally, the use of visual representations of the health system structure, such as the one detailed in Fig.  1 , will facilitate decision-making and preparedness among users and policy makers.

Human and institutional relationships, values, and norms (i.e., system ‘software’)

Governance: need for a national ad dmt patient registry.

Governance emphasizes the creation and monitoring of the rules that govern the supply and demand of health services. There should be a national AD DMT patient registry for accurate patient safety monitoring at a national level, and to support service planning. These data can be recorded along with other mandatory data from the developing MASS and RSMC (as an adjunct to the already proposed minimum dataset), but there may be additional monitoring requirements, dictated by Ireland Health Products Regulatory Agency (HPRA). It should be noted, however, that establishing a patient treatment registry is complex and requires significant resources, both human and financial. Challenges to consider in setting up a registry include ensuring the quality of the data, sustainability, governance, financing, and data protection. It is very likely that the pharmaceutical industry will play an important part in the set up and support of a registry, possibly linked to their licensing agreement [ 19 ].

Trust is a fundamental component of health system preparedness, incorporating both interpersonal trust (between patient and provider) and institutional trust (between individuals and the health system or government) [ 20 ]. Furthermore, trust is a prerequisite for health system resilience, particularly as new paradigms of care are being introduced. Health systems that have the trust of the population and political leaders by providing quality services prior to a health urgency have greater resilience [ 21 ].

Current provision of services in Ireland that are ‘DMT ready’ and the projected need

If and when a DMT such as lecanemab gains approval for use Ireland, the additional demand on services would be significant, including additional diagnostic services for biomarker detection (i.e., lumbar puncture, ligand-based PET scans), drug administration (i.e., infusion facilities such as day hospitals), and treatment-related safety monitoring capacity (i.e. serial brain MRIs). Considering these minimum requirements to offer an infusion-based DMT in Ireland, it is likely that it could only be offered by a limited number of centres in Ireland.

Projected neuroimaging requirements

Currently, an MRI scan is the preferred imaging modality in a memory service to assist with early diagnosis and detection of subcortical vascular changes [ 12 ]. Since the amyloid-based DMTs are associated with ARIA, ongoing monitoring for those on treatment would be needed. Based on the estimated prevalence figures above, and an anticipated need for at least three routine monitoring scans per person, this would necessitate 10,140 − 65,640 scheduled MRIs. Footnote 2 Additionally, about 40% of patients on DMTs may require up to three additional MRIs due to the actual development of ARIA or neurological sequelae, equating to 4056-26,656 non-routine MRI scans, so that the total early requirement for additional MRI scans would be 14,196 − 92,296 scans, required over the first two to three years post-licencing. 3 Steady-state MRI demand by 2030 is estimated to be 5,720 − 30,879 scans per annum.

Challenges to delivery of DMT for Alzheimer’s in Ireland

Table  3 summaries several areas that may pose both structural and ethical challenges to these new treatments for AD in Ireland.

Using a conceptual model of health system preparedness, we have presented a proposal for the delivery of anti-amyloid DMTs for AD, integrated within a hub-and-spoke model as part of the newly launched Model of Care for Dementia in Ireland. We acknowledge that the implementation of any healthcare model requires both system ‘hardware’ (tangible components such as infrastructure and workforce) and system ‘software’ (intangible components such as human values and power dynamics) [ 22 ]. As such, our current systems would likely face challenges in implementing DMT services without active efforts towards building individual and institutional trust and obtaining additional resources. Therefore, we anticipate that the full evolution to a national network of MASS with supporting RSMC, will take approximately three to five years to achieve and is subject to funding.

Moreover, while DMTs for AD offer the potential to revolutionize the management of the disease, it’s crucial to approach their development and implementation with careful consideration of both their benefits and limitations. Potential benefits include slowing of disease progression, improved quality of life, and delayed institutionalisation, along with associated economic benefits. However, these potential benefits need to be weighed against limitations such as moderate effectiveness of the drugs, potentially serious side effects, and ethical considerations such as treatment accessibility and affordability, and ascertaining what is meaningful to patients and their families [ 23 ].

Actions for the future

It will be important for healthcare systems to remain abreast of developments in order to offer those affected by AD the latest and most effective therapies. Part of this effort involves playing a role in drug discovery and evaluation. Recently, the Irish Health Research Board’s Clinical Trials Network funded a 5-year dementia clinical trials’ infrastructure development program, Dementia Trials Ireland, to grow Ireland’s capacity to conduct dementia trials [ 24 ]. In addition, over the past five years, there has been an additional focus on risk factor modification through lifestyle changes for prevention of dementia [ 4 ]. This approach is critical, and Ireland needs to keep pace with the rest of the world in addressing this issue. Finally, it is important to highlight inherent structural risks to the implementation of the proposed model. The future delivery of DMTs is complicated by long-standing capacity constraints within the Irish healthcare system, insufficient universal primary care coverage, and growing waiting lists [ 25 ]. As structural and policy reform continues as manifested by the publishing of National Dementia Strategy (2014) the establishment of the NDO (2017), and the launch of the Model of Care for Dementia (2023), this must be underpinned by targeted spending.

Limitations

The analysis was based on a consensus exercise including mostly healthcare professionals supported by consultation with healthcare recipients. Ideally, people with lived experience of AD and their care partners should be included in the main consensus process. Additionally, our data on the numbers of potential DMT recipients are broad estimates. To date, we lack robust epidemiological data on biomarker-positive individuals who may be eligible for the drugs. Additionally, the system readiness model we used is limited by the lack of consideration of funding, which is critical to future developments and implementation of DMTs. Moreover, risk-prediction modelling is not considered in the model, and although it is an active area of research, it has not been clinically implemented on a wide scale.

Other limitations include the dynamic nature of healthcare policies and the need to continuously adapt the system to consider changes in healthcare polices, funding structures and regulatory frameworks. Changes in population demographics also need to be considered, along with technological and IT infrastructure demands. Finally, other models besides the hub-and-spoke model may have merit and could provide alternative approaches to addressing this impending change in dementia care.

Healthcare systems around the world are recognising the urgent need for next-generation clinical care pathways for AD, prompted by enhanced diagnostics and the emergence of DMTs. Concerns about the ability of existing delivery models to introduce such therapies efficiently and equitably have been highlighted in several European countries [ 26 ]. Echoing European colleagues, Ireland’s healthcare system faces challenges to fully incorporate the prescription of DMTs into routine clinical pathways. However, these challenges may be overcome with additional resources, financial investment, and evolution to a structured, national treatment network, as envisaged by the recently launched ‘Model of Care for Dementia in Ireland’ [ 8 ]. We suggest that the blueprint outlined in this paper, developed in conjunction with facilitated workshops, including PPI representatives, is replicable for other healthcare systems of comparable size and scope in Europe and further afield.

Data availability

All data generated or analysed during this study are included in this published article.

At the time of writing, whilst Biogen was granted an accelerated approval for their monoclonal antibody (aducanumab) by the US food and Drug Administration (FDA), the European Medicines Agency (EMA) rejected the marketing authorisation application based on results from the two-phase III clinical trials.

As detailed earlier, we anticipate 3,380 to 21,880 people with prevalent AD-MCI or mild AD may proceed to DMT after licencing. As at least 3 monitoring scans are required per person, this equates to 10,140 − 65,640 additional “routine” MRIs.

In addition, 40% of these are expected to develop ARIAs (from trial data), where up to 3 additional MRIs would be required (depending on speed of resolution), equating to 4,056 − 26,656 non-routine MRI scans required over a specified period.

Once the existing pool of “prevalent” people is treated, steady-state MRI demand will be due to incident AD-MCI and mild AD. Based on the projections for an increased population with these two conditions, and also increased uptake of DMTs within this eligible pool, we estimate 1,362 to 7,350 people proceeding to DMT (see workings in footnote 2). This equates to 4,086 − 22,050 routine MRIs (3 per person), and 1634-8,820 non-routine MRIs for ARIAs (40% occurrence of ARIAs, and 3 scans per person for ARIA monitoring). Thus, the total annual MRI requirement by 2030 is 5720-30,879.

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Acknowledgements

The authors wish to thank the members of the Working Group for Disease Modifying Treatments for Dementia convened by the Irish Health Service Executive’s National Dementia Office, who contributed to discussions that informed this paper, including the authors, and Professor Brian Lawlor, Dr Justin Kinsella, Mr Pat McLoughlin, Ms Anne Horgan, and Dr Tim Dukelow, and members of the Patient and Public Involvement panel of Alzheimer Society Ireland.

This work was unfunded but supported administratively by the HSE’s National Dementia Office, Ireland.

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Iracema Leroi, Helena Dolphin, Sean Kennelly, Irina Kinchin, Sean O’Dowd, Dominic Trepel & Suzanne Timmons

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Iracema Leroi, Sean Kennelly, Irina Kinchin & Dominic Trepel

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Iracema Leroi, Sean Kennelly, Rónán O’Caoimh & Suzanne Timmons

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Helena Dolphin, Sean Kennelly & Sean O’Dowd

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Rachel Dinh

Department of General Practice, School of Medicine, University College Cork, Cork, Ireland

Department of Geriatric Medicine, Mercy University Hospital, Cork, Ireland

Rónán O’Caoimh

Health Service Executive’s National Dementia Office, Dublin, Ireland

Sean O’Dowd

Alzheimer Society Ireland, Dublin, Ireland

Laura O’Philbin

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Contributions

All authors meet all four ICMJE criteria for authorship. IL conceived and planned the project, supported by the NDO and other members of the working group. All authors carried participated in the workshops and contributed to the consensus statements, interpretation and presentation of the results. IL, HD, and RD took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript. None of the other authors declare any conflicts of interest.

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Correspondence to Iracema Leroi .

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Given that this study relied solely on the analysis of publicly available and previously published data, including Dolphin, Fallon [ 10 ], ethical approval was waived.

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IL, SK, and SOD participated in Advisory Groups with Biogen and Roche. The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. None of the other authors declare any conflicts of interest.

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Leroi, I., Dolphin, H., Dinh, R. et al. Navigating the future of Alzheimer’s care in Ireland - a service model for disease-modifying therapies in small and medium-sized healthcare systems. BMC Health Serv Res 24 , 705 (2024). https://doi.org/10.1186/s12913-024-11019-7

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