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What is the Background of a Study and How to Write It (Examples Included)

Have you ever found yourself struggling to write a background of the study for your research paper? You’re not alone. While the background of a study is an essential element of a research manuscript, it’s also one of the most challenging pieces to write. This is because it requires researchers to provide context and justification for their research, highlight the significance of their study, and situate their work within the existing body of knowledge in the field.  

Despite its challenges, the background of a study is crucial for any research paper. A compelling well-written background of the study can not only promote confidence in the overall quality of your research analysis and findings, but it can also determine whether readers will be interested in knowing more about the rest of the research study.  

In this article, we’ll explore the key elements of the background of a study and provide simple guidelines on how to write one effectively. Whether you’re a seasoned researcher or a graduate student working on your first research manuscript, this post will explain how to write a background for your study that is compelling and informative.  

Table of Contents

What is the background of a study ?  

Typically placed in the beginning of your research paper, the background of a study serves to convey the central argument of your study and its significance clearly and logically to an uninformed audience. The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers understand where the research study is placed in the current body of knowledge in a specific research discipline. It includes the reason for the study, the thesis statement, and a summary of the concept or problem being examined by the researcher. At times, the background of a study can may even examine whether your research supports or contradicts the results of earlier studies or existing knowledge on the subject.  

How is the background of a study different from the introduction?  

It is common to find early career researchers getting confused between the background of a study and the introduction in a research paper. Many incorrectly consider these two vital parts of a research paper the same and use these terms interchangeably. The confusion is understandable, however, it’s important to know that the introduction and the background of the study are distinct elements and serve very different purposes.   

• The basic different between the background of a study and the introduction is kind of information that is shared with the readers . While the introduction provides an overview of the specific research topic and touches upon key parts of the research paper, the background of the study presents a detailed discussion on the existing literature in the field, identifies research gaps, and how the research being done will add to current knowledge.  
• The introduction aims to capture the reader’s attention and interest and to provide a clear and concise summary of the research project. It typically begins with a general statement of the research problem and then narrows down to the specific research question. It may also include an overview of the research design, methodology, and scope. The background of the study outlines the historical, theoretical, and empirical background that led to the research question to highlight its importance. It typically offers an overview of the research field and may include a review of the literature to highlight gaps, controversies, or limitations in the existing knowledge and to justify the need for further research.  
• Both these sections appear at the beginning of a research paper. In some cases the introduction may come before the background of the study , although in most instances the latter is integrated into the introduction itself. The length of the introduction and background of a study can differ based on the journal guidelines and the complexity of a specific research study.  

Learn to convey study relevance, integrate literature reviews, and articulate research gaps in the background section. Get your All Access Pack now!    

To put it simply, the background of the study provides context for the study by explaining how your research fills a research gap in existing knowledge in the field and how it will add to it. The introduction section explains how the research fills this gap by stating the research topic, the objectives of the research and the findings – it sets the context for the rest of the paper.   

Where is the background of a study placed in a research paper?  

T he background of a study is typically placed in the introduction section of a research paper and is positioned after the statement of the problem. Researchers should try and present the background of the study in clear logical structure by dividing it into several sections, such as introduction, literature review, and research gap. This will make it easier for the reader to understand the research problem and the motivation for the study.  

So, when should you write the background of your study ? It’s recommended that researchers write this section after they have conducted a thorough literature review and identified the research problem, research question, and objectives. This way, they can effectively situate their study within the existing body of knowledge in the field and provide a clear rationale for their research.  

Creating an effective background of a study structure  

Given that the purpose of writing the background of your study is to make readers understand the reasons for conducting the research, it is important to create an outline and basic framework to work within. This will make it easier to write the background of the study and will ensure that it is comprehensive and compelling for readers.  

While creating a background of the study structure for research papers, it is crucial to have a clear understanding of the essential elements that should be included. Make sure you incorporate the following elements in the background of the study section :   

• Present a general overview of the research topic, its significance, and main aims; this may be like establishing the “importance of the topic” in the introduction.   
• Discuss the existing level of research done on the research topic or on related topics in the field to set context for your research. Be concise and mention only the relevant part of studies, ideally in chronological order to reflect the progress being made.  
• Highlight disputes in the field as well as claims made by scientists, organizations, or key policymakers that need to be investigated. This forms the foundation of your research methodology and solidifies the aims of your study.   
• Describe if and how the methods and techniques used in the research study are different from those used in previous research on similar topics.   

By including these critical elements in the background of your study , you can provide your readers with a comprehensive understanding of your research and its context.  

How to write a background of the study in research papers ?  

Now that you know the essential elements to include, it’s time to discuss how to write the background of the study in a concise and interesting way that engages audiences. The best way to do this is to build a clear narrative around the central theme of your research so that readers can grasp the concept and identify the gaps that the study will address. While the length and detail presented in the background of a study could vary depending on the complexity and novelty of the research topic, it is imperative to avoid wordiness. For research that is interdisciplinary, mentioning how the disciplines are connected and highlighting specific aspects to be studied helps readers understand the research better.   

While there are different styles of writing the background of a study , it always helps to have a clear plan in place. Let us look at how to write a background of study for research papers.    

• Identify the research problem: Begin the background by defining the research topic, and highlighting the main issue or question that the research aims to address. The research problem should be clear, specific, and relevant to the field of study. It should be framed using simple, easy to understand language and must be meaningful to intended audiences.  
• Craft an impactful statement of the research objectives: While writing the background of the study it is critical to highlight the research objectives and specific goals that the study aims to achieve. The research objectives should be closely related to the research problem and must be aligned with the overall purpose of the study.  
• Conduct a review of available literature: When writing the background of the research , provide a summary of relevant literature in the field and related research that has been conducted around the topic. Remember to record the search terms used and keep track of articles that you read so that sources can be cited accurately. Ensure that the literature you include is sourced from credible sources.  
• Address existing controversies and assumptions: It is a good idea to acknowledge and clarify existing claims and controversies regarding the subject of your research. For example, if your research topic involves an issue that has been widely discussed due to ethical or politically considerations, it is best to address them when writing the background of the study .  
• Present the relevance of the study: It is also important to provide a justification for the research. This is where the researcher explains why the study is important and what contributions it will make to existing knowledge on the subject. Highlighting key concepts and theories and explaining terms and ideas that may feel unfamiliar to readers makes the background of the study content more impactful.  
• Proofread to eliminate errors in language, structure, and data shared: Once the first draft is done, it is a good idea to read and re-read the draft a few times to weed out possible grammatical errors or inaccuracies in the information provided. In fact, experts suggest that it is helpful to have your supervisor or peers read and edit the background of the study . Their feedback can help ensure that even inadvertent errors are not overlooked.  

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How to avoid mistakes in writing the background of a study  

While figuring out how to write the background of a study , it is also important to know the most common mistakes authors make so you can steer clear of these in your research paper.   

• Write the background of a study in a formal academic tone while keeping the language clear and simple. Check for the excessive use of jargon and technical terminology that could confuse your readers.   
• Avoid including unrelated concepts that could distract from the subject of research. Instead, focus your discussion around the key aspects of your study by highlighting gaps in existing literature and knowledge and the novelty and necessity of your study.   
• Provide relevant, reliable evidence to support your claims and citing sources correctly; be sure to follow a consistent referencing format and style throughout the paper.   
• Ensure that the details presented in the background of the study are captured chronologically and organized into sub-sections for easy reading and comprehension.  
• Check the journal guidelines for the recommended length for this section so that you include all the important details in a concise manner. 

By keeping these tips in mind, you can create a clear, concise, and compelling background of the study for your research paper. Take this example of a background of the study on the impact of social media on mental health.  

Social media has become a ubiquitous aspect of modern life, with people of all ages, genders, and backgrounds using platforms such as Facebook, Instagram, and Twitter to connect with others, share information, and stay updated on news and events. While social media has many potential benefits, including increased social connectivity and access to information, there is growing concern about its impact on mental health.   Research has suggested that social media use is associated with a range of negative mental health outcomes, including increased rates of anxiety, depression, and loneliness. This is thought to be due, in part, to the social comparison processes that occur on social media, whereby users compare their lives to the idealized versions of others that are presented online.   Despite these concerns, there is also evidence to suggest that social media can have positive effects on mental health. For example, social media can provide a sense of social support and community, which can be beneficial for individuals who are socially isolated or marginalized.   Given the potential benefits and risks of social media use for mental health, it is important to gain a better understanding of the mechanisms underlying these effects. This study aims to investigate the relationship between social media use and mental health outcomes, with a particular focus on the role of social comparison processes. By doing so, we hope to shed light on the potential risks and benefits of social media use for mental health, and to provide insights that can inform interventions and policies aimed at promoting healthy social media use.  

To conclude, the background of a study is a crucial component of a research manuscript and must be planned, structured, and presented in a way that attracts reader attention, compels them to read the manuscript, creates an impact on the minds of readers and sets the stage for future discussions. 

A well-written background of the study not only provides researchers with a clear direction on conducting their research, but it also enables readers to understand and appreciate the relevance of the research work being done.   

Frequently Asked Questions (FAQs) on background of the study

Q: How does the background of the study help the reader understand the research better?

The background of the study plays a crucial role in helping readers understand the research better by providing the necessary context, framing the research problem, and establishing its significance. It helps readers:

• understand the larger framework, historical development, and existing knowledge related to a research topic
• identify gaps, limitations, or unresolved issues in the existing literature or knowledge
• outline potential contributions, practical implications, or theoretical advancements that the research aims to achieve
• and learn the specific context and limitations of the research project

Q: Does the background of the study need citation?

Yes, the background of the study in a research paper should include citations to support and acknowledge the sources of information and ideas presented. When you provide information or make statements in the background section that are based on previous studies, theories, or established knowledge, it is important to cite the relevant sources. This establishes credibility, enables verification, and demonstrates the depth of literature review you’ve done.

Q: What is the difference between background of the study and problem statement?

The background of the study provides context and establishes the research’s foundation while the problem statement clearly states the problem being addressed and the research questions or objectives.

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How to Write an Effective Background of the Study: A Comprehensive Guide

Table of Contents

The background of the study in a research paper offers a clear context, highlighting why the research is essential and the problem it aims to address.

As a researcher, this foundational section is essential for you to chart the course of your study, Moreover, it allows readers to understand the importance and path of your research.

Whether in academic communities or to the general public, a well-articulated background aids in communicating the essence of the research effectively.

While it may seem straightforward, crafting an effective background requires a blend of clarity, precision, and relevance. Therefore, this article aims to be your guide, offering insights into:

• Understanding the concept of the background of the study.
• Learning how to craft a compelling background effectively.
• Identifying and sidestepping common pitfalls in writing the background.
• Exploring practical examples that bring the theory to life.
• Enhancing both your writing and reading of academic papers.

Keeping these compelling insights in mind, let's delve deeper into the details of the empirical background of the study, exploring its definition, distinctions, and the art of writing it effectively.

What is the background of the study?

The background of the study is placed at the beginning of a research paper. It provides the context, circumstances, and history that led to the research problem or topic being explored.

It offers readers a snapshot of the existing knowledge on the topic and the reasons that spurred your current research.

When crafting the background of your study, consider the following questions.

• What's the context of your research?
• Which previous research will you refer to?
• Are there any knowledge gaps in the existing relevant literature?
• How will you justify the need for your current research?
• Have you concisely presented the research question or problem?

In a typical research paper structure, after presenting the background, the introduction section follows. The introduction delves deeper into the specific objectives of the research and often outlines the structure or main points that the paper will cover.

Together, they create a cohesive starting point, ensuring readers are well-equipped to understand the subsequent sections of the research paper.

While the background of the study and the introduction section of the research manuscript may seem similar and sometimes even overlap, each serves a unique purpose in the research narrative.

Difference between background and introduction

A well-written background of the study and introduction are preliminary sections of a research paper and serve distinct purposes.

Here’s a detailed tabular comparison between the two of them.

What is the relevance of the background of the study?

It is necessary for you to provide your readers with the background of your research. Without this, readers may grapple with questions such as: Why was this specific research topic chosen? What led to this decision? Why is this study relevant? Is it worth their time?

Such uncertainties can deter them from fully engaging with your study, leading to the rejection of your research paper. Additionally, this can diminish its impact in the academic community, and reduce its potential for real-world application or policy influence .

To address these concerns and offer clarity, the background section plays a pivotal role in research papers.

The background of the study in research is important as it:

• Provides context: It offers readers a clear picture of the existing knowledge, helping them understand where the current research fits in.
• Highlights relevance: By detailing the reasons for the research, it underscores the study's significance and its potential impact.
• Guides the narrative: The background shapes the narrative flow of the paper, ensuring a logical progression from what's known to what the research aims to uncover.
• Enhances engagement: A well-crafted background piques the reader's interest, encouraging them to delve deeper into the research paper.
• Aids in comprehension: By setting the scenario, it aids readers in better grasping the research objectives, methodologies, and findings.

How to write the background of the study in a research paper?

The journey of presenting a compelling argument begins with the background study. This section holds the power to either captivate or lose the reader's interest.

An effectively written background not only provides context but also sets the tone for the entire research paper. It's the bridge that connects a broad topic to a specific research question, guiding readers through the logic behind the study.

But how does one craft a background of the study that resonates, informs, and engages?

Here, we’ll discuss how to write an impactful background study, ensuring your research stands out and captures the attention it deserves.

Identify the research problem

The first step is to start pinpointing the specific issue or gap you're addressing. This should be a significant and relevant problem in your field.

A well-defined problem is specific, relevant, and significant to your field. It should resonate with both experts and readers.

Here’s more on how to write an effective research problem .

Provide context

Here, you need to provide a broader perspective, illustrating how your research aligns with or contributes to the overarching context or the wider field of study. A comprehensive context is grounded in facts, offers multiple perspectives, and is relatable.

In addition to stating facts, you should weave a story that connects key concepts from the past, present, and potential future research. For instance, consider the following approach.

• Offer a brief history of the topic, highlighting major milestones or turning points that have shaped the current landscape.
• Discuss contemporary developments or current trends that provide relevant information to your research problem. This could include technological advancements, policy changes, or shifts in societal attitudes.
• Highlight the views of different stakeholders. For a topic like sustainable agriculture, this could mean discussing the perspectives of farmers, environmentalists, policymakers, and consumers.
• If relevant, compare and contrast global trends with local conditions and circumstances. This can offer readers a more holistic understanding of the topic.

Literature review

For this step, you’ll deep dive into the existing literature on the same topic. It's where you explore what scholars, researchers, and experts have already discovered or discussed about your topic.

Conducting a thorough literature review isn't just a recap of past works. To elevate its efficacy, it's essential to analyze the methods, outcomes, and intricacies of prior research work, demonstrating a thorough engagement with the existing body of knowledge.

• Instead of merely listing past research study, delve into their methodologies, findings, and limitations. Highlight groundbreaking studies and those that had contrasting results.
• Try to identify patterns. Look for recurring themes or trends in the literature. Are there common conclusions or contentious points?
• The next step would be to connect the dots. Show how different pieces of research relate to each other. This can help in understanding the evolution of thought on the topic.

By showcasing what's already known, you can better highlight the background of the study in research.

Highlight the research gap

This step involves identifying the unexplored areas or unanswered questions in the existing literature. Your research seeks to address these gaps, providing new insights or answers.

A clear research gap shows you've thoroughly engaged with existing literature and found an area that needs further exploration.

How can you efficiently highlight the research gap?

• Find the overlooked areas. Point out topics or angles that haven't been adequately addressed.
• Highlight questions that have emerged due to recent developments or changing circumstances.
• Identify areas where insights from other fields might be beneficial but haven't been explored yet.

State your objectives

Here, it’s all about laying out your game plan — What do you hope to achieve with your research? You need to mention a clear objective that’s specific, actionable, and directly tied to the research gap.

How to state your objectives?

• List the primary questions guiding your research.
• If applicable, state any hypotheses or predictions you aim to test.
• Specify what you hope to achieve, whether it's new insights, solutions, or methodologies.

Discuss the significance

This step describes your 'why'. Why is your research important? What broader implications does it have?

The significance of “why” should be both theoretical (adding to the existing literature) and practical (having real-world implications).

How do we effectively discuss the significance?

• Discuss how your research adds to the existing body of knowledge.
• Highlight how your findings could be applied in real-world scenarios, from policy changes to on-ground practices.
• Point out how your research could pave the way for further studies or open up new areas of exploration.

Summarize your points

A concise summary acts as a bridge, smoothly transitioning readers from the background to the main body of the paper. This step is a brief recap, ensuring that readers have grasped the foundational concepts.

How to summarize your study?

• Revisit the key points discussed, from the research problem to its significance.
• Prepare the reader for the subsequent sections, ensuring they understand the research's direction.

Include examples for better understanding

Research and come up with real-world or hypothetical examples to clarify complex concepts or to illustrate the practical applications of your research. Relevant examples make abstract ideas tangible, aiding comprehension.

How to include an effective example of the background of the study?

• Use past events or scenarios to explain concepts.
• Craft potential scenarios to demonstrate the implications of your findings.
• Use comparisons to simplify complex ideas, making them more relatable.

Crafting a compelling background of the study in research is about striking the right balance between providing essential context, showcasing your comprehensive understanding of the existing literature, and highlighting the unique value of your research .

While writing the background of the study, keep your readers at the forefront of your mind. Every piece of information, every example, and every objective should be geared toward helping them understand and appreciate your research.

How to avoid mistakes in the background of the study in research?

To write a well-crafted background of the study, you should be aware of the following potential research pitfalls .

• Stay away from ambiguity. Always assume that your reader might not be familiar with intricate details about your topic.
• Avoid discussing unrelated themes. Stick to what's directly relevant to your research problem.
• Ensure your background is well-organized. Information should flow logically, making it easy for readers to follow.
• While it's vital to provide context, avoid overwhelming the reader with excessive details that might not be directly relevant to your research problem.
• Ensure you've covered the most significant and relevant studies i` n your field. Overlooking key pieces of literature can make your background seem incomplete.
• Aim for a balanced presentation of facts, and avoid showing overt bias or presenting only one side of an argument.
• While academic paper often involves specialized terms, ensure they're adequately explained or use simpler alternatives when possible.
• Every claim or piece of information taken from existing literature should be appropriately cited. Failing to do so can lead to issues of plagiarism.
• Avoid making the background too lengthy. While thoroughness is appreciated, it should not come at the expense of losing the reader's interest. Maybe prefer to keep it to one-two paragraphs long.
• Especially in rapidly evolving fields, it's crucial to ensure that your literature review section is up-to-date and includes the latest research.

Example of an effective background of the study

Let's consider a topic: "The Impact of Online Learning on Student Performance." The ideal background of the study section for this topic would be as follows.

In the last decade, the rise of the internet has revolutionized many sectors, including education. Online learning platforms, once a supplementary educational tool, have now become a primary mode of instruction for many institutions worldwide. With the recent global events, such as the COVID-19 pandemic, there has been a rapid shift from traditional classroom learning to online modes, making it imperative to understand its effects on student performance.

Previous studies have explored various facets of online learning, from its accessibility to its flexibility. However, there is a growing need to assess its direct impact on student outcomes. While some educators advocate for its benefits, citing the convenience and vast resources available, others express concerns about potential drawbacks, such as reduced student engagement and the challenges of self-discipline.

This research aims to delve deeper into this debate, evaluating the true impact of online learning on student performance.

Why is this example considered as an effective background section of a research paper?

This background section example effectively sets the context by highlighting the rise of online learning and its increased relevance due to recent global events. It references prior research on the topic, indicating a foundation built on existing knowledge.

By presenting both the potential advantages and concerns of online learning, it establishes a balanced view, leading to the clear purpose of the study: to evaluate the true impact of online learning on student performance.

As we've explored, writing an effective background of the study in research requires clarity, precision, and a keen understanding of both the broader landscape and the specific details of your topic.

From identifying the research problem, providing context, reviewing existing literature to highlighting research gaps and stating objectives, each step is pivotal in shaping the narrative of your research. And while there are best practices to follow, it's equally crucial to be aware of the pitfalls to avoid.

Remember, writing or refining the background of your study is essential to engage your readers, familiarize them with the research context, and set the ground for the insights your research project will unveil.

Drawing from all the important details, insights and guidance shared, you're now in a strong position to craft a background of the study that not only informs but also engages and resonates with your readers.

Now that you've a clear understanding of what the background of the study aims to achieve, the natural progression is to delve into the next crucial component — write an effective introduction section of a research paper. Read here .

Frequently Asked Questions

The background of the study should include a clear context for the research, references to relevant previous studies, identification of knowledge gaps, justification for the current research, a concise overview of the research problem or question, and an indication of the study's significance or potential impact.

The background of the study is written to provide readers with a clear understanding of the context, significance, and rationale behind the research. It offers a snapshot of existing knowledge on the topic, highlights the relevance of the study, and sets the stage for the research questions and objectives. It ensures that readers can grasp the importance of the research and its place within the broader field of study.

The background of the study is a section in a research paper that provides context, circumstances, and history leading to the research problem or topic being explored. It presents existing knowledge on the topic and outlines the reasons that spurred the current research, helping readers understand the research's foundation and its significance in the broader academic landscape.

The number of paragraphs in the background of the study can vary based on the complexity of the topic and the depth of the context required. Typically, it might range from 3 to 5 paragraphs, but in more detailed or complex research papers, it could be longer. The key is to ensure that all relevant information is presented clearly and concisely, without unnecessary repetition.

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Why ‘context’ is important for research

Context is something we’ve been thinking a lot about at ScienceOpen recently. It comes from the Latin ‘ con ’ and ‘ texere ’ (to form ‘ contextus ’), which means ‘weave together’. The implications for science are fairly obvious: modern research is about weaving together different strands of information, thought, and data to place your results into the context of existing research. This is the reason why we have introductory and discussion sections at the intra-article level.

But what about context at a higher level?

Context can defined as: “ The circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood .” Simple follow on questions might be then, what is the context of a research article? How do we define that context? How do we build on that to do science more efficiently? The whole point for the existence of research articles is that they can be understood by as broad an audience as possible so that their re-use is maximised.

There are many things that impinge upon the context of research. Paywalls, secretive and exclusive peer review, lack of discovery, lack of inter-operability, lack of accessibility. The list is practically endless, and a general by-product of a failure for traditional scholarly publishing models to embrace a Web-based era.

While a lot of excellent new research platforms now feature slick discovery tools and features, we feel that this falls short of what is really needed for optimal research re-use in the digital age.

Discovery is the pathway to context. Context of an article is all about how research fits into increasingly complex domains, and using structured networks to decipher its value. With the power of the internet at our disposal, putting research in context should be of key importance in a world where there is ever more research being published that is impossible to manually filter.

Tracking the genealogy of research

Citations are perhaps what we might consider to be academic context. These form the structured networks or genealogies of an idea in their rawest sense. Through citations we gain a small amount of understanding into how research is being re-used by other researchers, and also the gateway to understanding what it is those citations are telling us.

At ScienceOpen, we show all articles and article records that cite a particular research article, and also provide links to similar articles on our platform. These are drawn at the moment from almost 12 million article records, so can potentially form huge networks of information.

In addition we show which articles are most similar based on keywords, and also which open access articles are citing a particular work. You can explore each of these in more depth, and begin to track research networks! So it’s like enhanced discovery, but with a smattering of cherries on top.

Generating context through engagement

One of the great things about context is that it is flexible and can be defined by user engagement. Take peer review for example. This is a way of adding context to a paper, by drawing on external expertise and perspective to enhance the content of a research article. Peer evaluation of this sort is crucial for defining the context of a paper, and should not be hidden away out of sight and use. As we use public post-publication peer review at ScienceOpen, the full discussion and process of research is transparent.

Other ways of generating simple context are through sharing and recommendations of articles. The more this is done, the more you can understand which articles are of wider interest.

Social context

The rise of altmetrics can be seen as the broadening how we think about context. Altmetrics are a pathway to understanding how articles have been discussed, mentioned or shared in online sources including mainstream news outlets, blogs, and a variety of social networks.

On every single article record (almost 12 million at the moment), we show Altmetric scores. You can also sort searches by Altmetric, which provides additional context for which articles are generating the most societal discussion online. This is great if you want to track social media trends in a particular field, and again is all about placing research objects into a broader context.

So these are just some of the ways in which we put research in context, and we do it on a massive scale. Let us know in the comments what you think ‘research in context’ is all about, and why you think it’s important!

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Research Context

Research context refers to the setting or environment in which scientific investigations take place. It includes factors such as the purpose of the study, population being studied, and ethical considerations.

Related terms

Ethics : Ethics refers to moral principles that guide researchers' behavior during studies involving human participants or animals.

Population : In research, population refers to all members who share certain characteristics and from which a sample may be drawn.

Experimental Design : Experimental design refers to how researchers plan and conduct their experiments while considering important factors such as control groups, random assignment, and variables.

" Research Context " appears in:

Practice questions ( 1 ).

• What is an operational definition in a research context?

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What is the Background of the Study and How to Write It

What is the Background of the Study in Research? 

The background of the study is the first section of a research paper and gives context surrounding the research topic. The background explains to the reader where your research journey started, why you got interested in the topic, and how you developed the research question that you will later specify. That means that you first establish the context of the research you did with a general overview of the field or topic and then present the key issues that drove your decision to study the specific problem you chose.

Once the reader understands where you are coming from and why there was indeed a need for the research you are going to present in the following—because there was a gap in the current research, or because there is an obvious problem with a currently used process or technology—you can proceed with the formulation of your research question and summarize how you are going to address it in the rest of your manuscript.

Why is the Background of the Study Important?

No matter how surprising and important the findings of your study are, if you do not provide the reader with the necessary background information and context, they will not be able to understand your reasons for studying the specific problem you chose and why you think your study is relevant. And more importantly, an editor who does not share your enthusiasm for your work (because you did not fill them in on all the important details) will very probably not even consider your manuscript worthy of their and the reviewers’ time and will immediately send it back to you.

To avoid such desk rejections , you need to make sure you pique the reader’s interest and help them understand the contribution of your work to the specific field you study, the more general research community, or the public. Introducing the study background is crucial to setting the scene for your readers.

Table of Contents:

• What is “Background Information” in a Research Paper?
• What Should the Background of a Research Paper Include?
• Where Does the Background Section Go in Your Paper?

Background of the Study Structure

Before writing your study background, it is essential to understand what to include. The following elements should all be included in the background and are presented in greater detail in the next section:

• A general overview of the topic and why it is important (overlaps with establishing the “importance of the topic” in the Introduction)
• The current state of the research on the topic or on related topics in the field
• Controversies about current knowledge or specific past studies that undergird your research methodology
• Any claims or assumptions that have been made by researchers, institutions, or politicians that might need to be clarified
• Methods and techniques used in the study or from which your study deviated in some way

Presenting the Study Background

As you begin introducing your background, you first need to provide a general overview and include the main issues concerning the topic. Depending on whether you do “basic” (with the aim of providing further knowledge) or “applied” research (to establish new techniques, processes, or products), this is either a literature review that summarizes all relevant earlier studies in the field or a description of the process (e.g., vote counting) or practice (e.g., diagnosis of a specific disease) that you think is problematic or lacking and needs a solution.

Example s of a general overview

If you study the function of a Drosophila gene, for example, you can explain to the reader why and for whom the study of fly genetics is relevant, what is already known and established, and where you see gaps in the existing literature. If you investigated how the way universities have transitioned into online teaching since the beginning of the Covid-19 pandemic has affected students’ learning progress, then you need to present a summary of what changes have happened around the world, what the effects of those changes have been so far, and where you see problems that need to be addressed. Note that you need to provide sources for every statement and every claim you make here, to establish a solid foundation of knowledge for your own study. 

Describing the current state of knowledge

When the reader understands the main issue(s), you need to fill them in more specifically on the current state of the field (in basic research) or the process/practice/product use you describe (in practical/applied research). Cite all relevant studies that have already reported on the Drosophila gene you are interested in, have failed to reveal certain functions of it, or have suggested that it might be involved in more processes than we know so far. Or list the reports from the education ministries of the countries you are interested in and highlight the data that shows the need for research into the effects of the Corona-19 pandemic on teaching and learning.

Discussing controversies, claims, and assumptions

Are there controversies regarding your topic of interest that need to be mentioned and/or addressed? For example, if your research topic involves an issue that is politically hot, you can acknowledge this here. Have any earlier claims or assumptions been made, by other researchers, institutions, or politicians, that you think need to be clarified?

Mentioning methodologies and approaches

While putting together these details, you also need to mention methodologies : What methods/techniques have been used so far to study what you studied and why are you going to either use the same or a different approach? Are any of the methods included in the literature review flawed in such a way that your study takes specific measures to correct or update? While you shouldn’t spend too much time here justifying your methods (this can be summarized briefly in the rationale of the study at the end of the Introduction and later in the Discussion section), you can engage with the crucial methods applied in previous studies here first.

When you have established the background of the study of your research paper in such a logical way, then the reader should have had no problem following you from the more general information you introduced first to the specific details you added later. You can now easily lead over to the relevance of your research, explain how your work fits into the bigger picture, and specify the aims and objectives of your study. This latter part is usually considered the “ statement of the problem ” of your study. Without a solid research paper background, this statement will come out of nowhere for the reader and very probably raise more questions than you were planning to answer.   

Where does the study background section go in a paper?

Unless you write a research proposal or some kind of report that has a specific “Background” chapter, the background of your study is the first part of your introduction section . This is where you put your work in context and provide all the relevant information the reader needs to follow your rationale. Make sure your background has a logical structure and naturally leads into the statement of the problem at the very end of the introduction so that you bring everything together for the reader to judge the relevance of your work and the validity of your approach before they dig deeper into the details of your study in the methods section .

Consider Receiving Professional Editing Services

Now that you know how to write a background section for a research paper, you might be interested in our AI Text Editor at Wordvice AI. And be sure to receive professional editing services , including academic editing and proofreading , before submitting your manuscript to journals. On the Wordvice academic resources website, you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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Report reader checklist : context.

Information about the context of a study is usually included at the beginning and end of a report. At the beginning of a report, this context should be provided to describe past research and theory and then explain the focus of the study. At the end of a report, a contextual discussion can relate the findings of the study back to past research and suggest next steps to further understand the topic. When context is missing from a report, you may not understand how the study relates to or informs a general understanding of the topic. For example, you might not have a clear understanding of the background on the topic, how the study advances knowledge on the topic and how the results of the study can be applied. The following are important things to identify as you look for context in research reports:

The report provides a background on the topic, including relevant definitions, the context within which the study is conducted and a rationale for why the study was conducted.

It is important for you to understand why the study was conducted, what the study attempted to accomplish, why the research is important to the field and how the results could be used.

It is also important for you to know how this study fits into the larger body of research that has been conducted on this topic. If the study is the first study of its kind, that should be mentioned. Otherwise, look to see if the authors include references to studies previously published on the topic. This helps you understand how the study adds to or clarifies understanding of the topic. [ see examples ] b. The report explains the history of the study and/or theoretical frameworks, if appropriate.

Some studies in the field of online teaching and learning are repeated periodically. When this is the case, a report should include information, such as past participant rates, revisions to survey instruments or research protocols, or other changes to the study methodology since the previous findings were released.

Study reports may also include theoretical frameworks . Theories (or theoretical frameworks) associate a study with other studies done on a topic. These studies combined together (often called a body of research) can increase overall understanding of a topic and help you see where a study falls within a line of research. You should be able to understand the basic theoretical framework of a report without prior knowledge. If the study is based in theory, the theory can be described and it should be clear how the theory relates to the main focus of the study. [ see examples ] c. The report includes the research aims or goals addressed by the study.

Research aims or goals are often included toward the beginning of the report after past research and theoretical frameworks have been explained. Research aims or goals (sometimes framed as questions) should be clearly identified in a report and can be written in a way that helps you understand what the study is investigating.

[ see examples ] d. The report offers suggestions for further research.

a. The report describes the larger purpose or need for the study.

• See pages 11-14 for an example of a summary of previous research and clear definitions.
• See page 3 for a description of this study’s purpose with citations.

b. The report explains the history of the study and/or theoretical frameworks, if appropriate.

• Pages 5-7 highlight key findings as compared to previous year’s reports. Page 6 features major themes over 12 years of conducting this report.

c. The report includes the research aims or goals addressed by the study.

• Page 3 features a preface that provides key details about the history of the report and the need for the twelfth edition.

d. The report offers suggestions for further research.

• See pages 9-10 for several recommendations for future research. These recommendations are provided in a way that is digestible for diverse readers.

Checklist areas

Download a one-page version of the checklist .

What are theoretical frameworks?

What are qualitative research methodologies, what are quantitative research methodologies, what are mixed methodologies, what is validity, what is the difference between a population and a sample, what is generalizability in research, what is data visualization, what is conflict of interest, contact info.

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Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

• Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
• Provide context: Provide a brief overview of the research area and highlight its significance in the field.
• Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
• Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
• Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
• Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
• Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
• Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

• Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
• Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
• I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
• Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
• Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
• State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
• Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

• Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
• Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
• Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
• Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
• State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
• Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

• Provide a clear understanding of the research problem and its context.
• Identify the gap in knowledge that the study intends to fill.
• Establish the significance of the research problem and its potential contribution to the field.
• Highlight the key concepts, theories, and research findings related to the problem.
• Provide a rationale for the research questions or hypotheses and the research design.
• Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

• Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
• Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
• Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
• Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
• Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

• Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
• Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
• Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
• Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
• Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

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What is the Background of a Study and How Should it be Written?

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Table of Contents

The background of a study is one of the most important components of a research paper. The quality of the background determines whether the reader will be interested in the rest of the study. Thus, to ensure that the audience is invested in reading the entire research paper, it is important to write an appealing and effective background. So, what constitutes the background of a study, and how must it be written?

What is the background of a study?

The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the research and its importance in a clear and logical manner. At times, the background may even explore whether the study builds on or refutes findings from previous studies. Any relevant information that the readers need to know before delving into the paper should be made available to them in the background.

How is a background different from the introduction?

The introduction of your research paper is presented before the background. Let’s find out what factors differentiate the background from the introduction.

• The introduction only contains preliminary data about the research topic and does not state the purpose of the study. On the contrary, the background clarifies the importance of the study in detail.
• The introduction provides an overview of the research topic from a broader perspective, while the background provides a detailed understanding of the topic.
• The introduction should end with the mention of the research questions, aims, and objectives of the study. In contrast, the background follows no such format and only provides essential context to the study.

How should one write the background of a research paper?

The length and detail presented in the background varies for different research papers, depending on the complexity and novelty of the research topic. At times, a simple background suffices, even if the study is complex. Before writing and adding details in the background, take a note of these additional points:

• Start with a strong beginning: Begin the background by defining the research topic and then identify the target audience.
• Cover key components: Explain all theories, concepts, terms, and ideas that may feel unfamiliar to the target audience thoroughly.
• Take note of important prerequisites: Go through the relevant literature in detail. Take notes while reading and cite the sources.
• Maintain a balance: Make sure that the background is focused on important details, but also appeals to a broader audience.
• Include historical data: Current issues largely originate from historical events or findings. If the research borrows information from a historical context, add relevant data in the background.
• Explain novelty: If the research study or methodology is unique or novel, provide an explanation that helps to understand the research better.
• Increase engagement: To make the background engaging, build a story around the central theme of the research

Avoid these mistakes while writing the background:

• Ambiguity: Don’t be ambiguous. While writing, assume that the reader does not understand any intricate detail about your research.
• Unrelated themes: Steer clear from topics that are not related to the key aspects of your research topic.
• Poor organization: Do not place information without a structure. Make sure that the background reads in a chronological manner and organize the sub-sections so that it flows well.

Writing the background for a research paper should not be a daunting task. But directions to go about it can always help. At Elsevier Author Services we provide essential insights on how to write a high quality, appealing, and logically structured paper for publication, beginning with a robust background. For further queries, contact our experts now!

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Study Context: Ensuring Validity, Reliability, and Credibility Research Paper

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
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Introduction

Context of research, data collection, data analyses, reliability and validity of research.

In essence, the context of research is probably the most crucial aspects of the entire study. It defines factors involving the social, political, and economic environment in which the problem arises. Also, it includes the factors that inspire the designing, proposing, and conducting the actual research in the field. It could identify what other researchers have done as well as the underlying gaps in the literature which the envisioned research will close.

Also, the context will include the originality of approach and the future aspects of research that cannot be incorporated in the current research. Further, this discussion will focus on the variables that will form the basis of envisioned research. Lastly, the paper will elaborate on how to ensure the validity, reliability, and credibility of the research.

Environment Evoking Research Problem

There are political, economic, and social aspects which have evoked the problem being solved by the envisioned research. These form the environmental context within which the mistreatment, oppression, and exploitation of migrant worker occur.

First, the political autonomy of Malaysia is the main factor that has led to the oppression of migrant workers. In this regard, it is realized that the government of Malaysia has enacted laws that encourage the development of industries in congested areas of Malaysia. In these areas, the workers are rendered powerless because they can hardly access good leadership to protect their rights (Hilsdon & Giridharan, 2010).

While the government neglects the welfare of the migrant workers, underground systems emerge to connect the workers and the industries. The brokerage system takes advantage of the migrant, luring them to work under great oppression. The country lacks NGOs which could assist in offering this leadership by directing the government on best practices of humanity and rights.

The social environment is another aspect that purports the emergence of this problem (Sharma, 2010; See, 2010). In this case, it is evident that the native citizens of Malaysia consider the migrants as complete foreigners rather than fellow human beings (Sharma, 2010). As a result, the owners of industries exploit them by paying extremely low wages (See, 2010). Probably, the social environment is the major aspect that evokes this oppression.

Regarding the economic context, the companies in congested areas where the government allows industrialization to seek cheap labor. Most of these industries aim at reducing operational cost, which includes payment of wages. As a result, the economic strategies of these companies tempt them to employ the migrants who offer cheap labor. Additionally, the owners take advantage of the government’s ignorance to oppress the workers.

Also, the macroeconomic systems of Malaysia have not set the wage limits for the companies leading to mistreatment of these migrant workers. These are the aspects of environmental context which result to the problem addressed in this research.

Interests Inspiring Research

There are various aspects which inspire the development of this envisioned research that focuses on the treatment of migrant workers in Malaysia. In this regard, one of the most important aspects which inspire the research is the gap that exists in the literature. The current research has focused profoundly on the aspects such as the geography of migration, sexuality, Filipinos migrants in Malaysia, and ethnicity, among other issues.

However, they have not explored to evaluate the extent to which the church has helped in eliminating and reducing the oppression of migrants. They have not also determined how the churches could intervene in the struggle against the oppression of the migrants.

This gap has been the major driving force behind conducting the envisioned research. In other words, the research is meant to close that gap by identifying ways in which the church could offer transformational leadership to change the current status of migrants.

Secondly, the ideologies of purporting humanity, advocating for equality, and harnessing unity are roles of all human beings regardless of their religions, geographical origins, and ethnic group. All people are entitled to care for the welfare of their colleagues irrespective of the nationality.

The research is based on the premises that the migrant workers should be assisted to live a good life in Malaysia although they come from other countries. As a result, investigating how the church could help in offering the right leadership is one of the efforts directed towards purporting humanity.

Originality of Approach

In essence, the approach used in conducting the envisioned research is essentially unique in various ways. First, it is evident that the research focuses on an area that has not been studied by another researcher. Particularly, previous research studies have not researched on the intervention, position, and role of the church in solving the oppression of migrants in Malaysia.

Additionally, the research is based on a combination of various research method and approaches. For example, the research involves the use of two research methods, including exploratory and constructive methods. Exploratory research aims at investigating other issues revolving around the problem. This investigation will determine whether there are other factors conjoined to the main problem statement.

On the other hand, constructive research method aims at developing solutions for the identified problem. The combination of two separate methods is an authentic approach to the envisioned research. Additionally, the research incorporates qualitative and quantitative research methods. This method is commonly known as the pragmatic method of research (Schram, 2006).

Another aspect of research that purports authenticity is the use of a flexible questionnaire. This is not a commonplace type of questionnaire that is used in research studies. It is an authentic idea developed to ensure that the questionnaires can include new themes as opposed to the fixed ones.

Lastly, the sampling method is an original idea developed by combining two methods of sampling, which include purposive and random sampling. The combination develops another authentic sampling method known as the random sampling method. The authenticity of the research is based on combining various methods that could give reliable results.

Future Research

There are issues that cannot be incorporated in this research owing to the cost, scope, and relevance of research study. As a result, they will be handled in future research that could be rooted in these ideas. For example, the research will be concentrating on the inputs of churches and other NGOs to eliminate oppression.

However, it is evident that the importance of the church is to provide the right leadership that could assist in fighting for the rights of the migrant workers. These rights can be granted by the government, considering that it is the supreme organ in the system. As a result, future research could make inquiries on how the government could be involved fully. This will be the final stage of eliminating oppression.

Mixed method research is time-consuming due to the integration of the two methodologies of research, including quantitative and qualitative methods (Trochim & Donnelly, 2010; Shank, 2006). This implies that the method of data collection should be reliable, effective, and satisfactory. In this light, data will be collected by two methods ensuring that the respondents are reached within considerable time.

Use of Questionnaires

A questionnaire refers to a set of questions that a respondent should answer to provide the relevant information concerning the research study (Wirth & Padilla, 2010). Because the study involves unique methods, flexible questionnaires will be used to allow manipulation of questions according to the emerging issues (Trochim & Donnelly, 2010). Also, this will allow the concurrent analysis to take place along with the collection of data.

Also, the questionnaires will include both closed and opened questions. Closed questions will provide predefined choices so that the respondents choose from them. These questions guide the respondents while answering questions to ensure that they do not provide irrelevant information (Patton, 2002).

Also, it saves a lot of time for the respondents. However, respondents need to express profound information about the subject even if it is not inquired (Chan & Danao, 2010). In this case, the questionnaires will include open questions which allow the provision of additional information. The additional information could inspire the development of new themes during the study (Trochim & Donnelly, 2010).

Email survey

In this online survey, questions will be composed through email messages reflecting the components of the questionnaires. The researcher will find contacts for the relevant leaders and send the survey. Phone calls will be used to notify the respondents about the survey to ensure that many respondents access their emails to undertake the survey.

Interviewing

Due to the involvement of crucial participants such as government officials, administration of questionnaires is very informal. As a result, it is important to conduct interviews that could help in obtaining information from such people. Although most of the interviews are physical, some situations will require the application of technological devices such as phone calls, emails, and text messages to save time.

The use of technology will assist in saving time for the research since the sample population could be considerably large (Shank, 2006). The application of this method allows follow up questions that help in obtaining more information from the respondents. The combination of questionnaires and interview will lead to a holistic qualitative methodology that will fulfill the research purpose and solve the problem.

Quantitative Analysis

This study must incorporate quantitative and qualitative analysis because the data will have two aspects. This analysis will involve the computation of mean, mode, and variance of quantitative data. The mean will provide an overview of the respondent’s general view of the problem. The mode will assist in analyzing the number of responses with the highest number of responses to determine the intensity of each view (Plowright, 2011).

Lastly, the variance will aim at investigating the distribution of the views by the respondents. Also, the variance will provide the basis of computing the standard deviation that shows the deviation of view from the general overview as stipulated by the mean.

Moreover, quantitative analysis will involve the computation of the “p” test using ANOVA to determine the rejection of null hypotheses (Liu, 2009). In this case, the null hypothesis will be rejected when the “p” values are less than 0.05.

Qualitative Analysis

Qualitative analysis will be conducted in the following steps.

• Reading the responses carefully and coding them.
• Determination of the relevant themes and making a thematic summary.
• Interpreting the findings by analyzing the impacts of the responses.
• Triangulation of sources.
• Making conclusions and recommendations while compiling a draft report.
• Seeking the validation of feedback.
• Communicating findings to the relevant authorities for implementation.

The variables of the envisioned research will include various factors. Regarding the qualitative ones, the research will include variables such as gender, nationality, and ethnicity. In this case, the oppression of migrant workers is mostly evoked by these factors. For example, the native employees discriminate between the migrants and native job seekers.

Also, the ethnicity of the migrants is of utmost importance because the natives focus on it profoundly when mistreating the migrants. Gender is an additional variable which affects the treatment of migrants.

This variable has been very relevant to the research studies concerning migrants in Malaysia. Male and female migrants are treated differently in the industries making it crucial to investigate how the churches could intervene in helping the oppressed gender.

There are various measures that will ensure the reliability and validity of the entire research. These measures ensure that the final results can be used in making relevant conclusions regarding the topic of study. These measures are applied during sampling, data collection, and analysis.

During sampling, the sample population will be collected using the random purposive method. This implies that it will incorporate the ideology of randomization and purposefulness (Stein & Kohlmann, 2010). Randomization will help in ensuring that the sample population is not collected in a manner that obtains biased results (Shank, 2006).

On the other hand, purposefulness ensures that the sample population includes the people who only have relevant information concerning the oppression of migrant workers (Grbich, 2007). Also, the population will be stratified such that it has three stratifications, including migrants, church leaders, and government officials. This will ensure a diversity of opinions in the data obtained (Stein & Kohlmann, 2010).

During data collection, data collectors will use flexible questionnaires, ensuring that the inquiries can allow for manipulation of question. The manipulation of questions allows the inclusion of new themes and removal of irrelevant themes during the study.

This implies that most of the issues conjoined to the problem are considered (Shibata & Kihura, 2010). The questionnaire will have clear questions so that the query could lead to the same answer even if the question is posed severally (Patton, 2002).

Analysis of data will also be conducted in a manner that purports relate-ability and credibility of the research. In this case, the analysts will examine data during and after collection. This is known as parallel analysis. It ensures that the questionnaire is updated to include the relevant themes and exclude the irrelevant ones according to the opinions emerging from respondents (Sheḳedi, 2008).

It is evident that the research problem arises due to some issues regarding the social, political, and economic aspects. These aspects form the broad context of the entire research because the problem is the sole basis of research. Additionally, the discussion has shown that the research has considered the reliability, credibility, and validity of the results.

Chan, S., & Danao, L. (2010). Are nurses prepared to curb the tobacco epidemic in China? A questionnaire survey of schools of nursing. International Journal of Nursing Studies , 45 (5), 706-713.doi:10.1016/j.ijnurstu.2006.12.008

Grbich, C. (2007). Qualitative data analysis: an introduction . London: SAGE Publications.

Liu, H. (2009). Software performance and scalability a quantitative approach . Hoboken, N.J.: John Wiley & Sons.

Patton, M. (2002). Qualitative research & evaluation methods (3 rd ed.). Thousand Oaks, CA: Sage.

Plowright, D. (2011). Using mixed methods: frameworks for an integrated methodology . London: SAGE.

Schram, T. (2006). Conceptualizing and proposing qualitative research . Upper Saddle River, NJ: Pearson Merrill Prentice Hall.

See, C. (2010). Counseling in Malaysia: History, current status, and future trends. Journal of Counseling and Development, 88 (1), 18–22.

Sharma, R. (2010). Preventing corruption through spiritual leadership in organizations. Organization and Management, 1 (139), 135–151.

Shank, G. (2006). Qualitative research: A personal skills approach . Upper Saddle River, NJ: Pearson Merrill Prentice Hall.

Sheḳedi, A. (2008). Multiple case narrative a qualitative approach to studying multiple populations . Amsterdam: John Benjamins Publishers.

Shibata, Y., &Kihura, K. (2010). Questionnaire survey of physical properties of urea preparations. Nishi Nihon Hifuka, 70 (6), 634-638. doi:10.2336/nishinihonhifu.70.634

Stein, H., & Kohlmann, B. (2010). Comparison of two sampling methods for biomonitoring using aquatic macroinvertebrates in the Dos Novillos River, Costa Rica. Ecological Engineering, 34 (4), 267-275. doi:10.1016/j.ecoleng.2007.06.010

Trochim, W., & Donnelly, J. (2010). The research methods knowledge base . Mason, OH: Cengage Learning.

Wirth, M., & Padilla, R. (2010). College student success: A qualitative modeling approach. Community College Journal of Research and Practice , 32 (9), 688-711.doi:10.1080/10668920701380942

Hilsdon, A., & Giridharan, B. (2010). Racialised Sexualities: The Case of Filipina Migrant Workers In East Malaysia. Gender, Place & Culture , 15 (6), 611-628. doi: 10.1080/09663690802518529.

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World Leaders in Research-Based User Experience

Context methods: study guide.

July 31, 2023 2023-07-31

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Context methods (such as field and diary studies) provide insights about a users’ real-life environment and behaviors and shed light on how products are used in a natural context. 

Here’s a list of NN/g’s most useful introductory articles and videos about context methods (field studies and diary studies), as well as some related topics. Within each section, the resources are in recommended reading order.

In This Article:

Context methods: an overview, diary studies, field studies, contextual inquiry.

Many  UX-research methods  involve asking users to pretend they’re in a realistic but hypothetical situation. For example, in a usability test, participants may be given the task to buy a new car. While we hope that users will behave as if they really were making this purchase, there might be some important contextual details we’d miss out on with this method. 

Field and diary studies use very different approaches. They involve observing users’ behaviors in their real-life context. Participants are not asked to do anything special, except perhaps answer a few questions. 

In a  diary study , participants document their experiences (thoughts, feelings, and behaviors) over a set period of time (a few days, a few weeks, or longer). A  field study  is conducted in the user’s environment (e.g., home or office). Researchers follow each participant around and observe the participant’s normal daily behaviors and activities.

Returning to the car-buying example, a field study may involve observing participants in their homes while they research models of cars and dealerships. A diary study may have each participant log car-shopping activities such as visiting a dealership or discussing options with a partner.  

Field and diary studies are particularly useful during the  discovery  phase of a design project when we’re trying to build up our understanding of our users and opportunities to improve their experiences. They’re also commonly used to help  develop customer journey maps .

The terms “field study” and “contextual inquiry” are often used interchangeably. Typically, a contextual inquiry involves more interviewing and conversation with the participant.

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Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

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Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." [2] Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Qualitative Research Approaches

Ethnography

Ethnography as a research design originates in social and cultural anthropology and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc, through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded theory

Grounded Theory is the "generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior." [5] Unlike quantitative research, which is deductive and tests or verifies an existing theory, grounded theory research is inductive and, therefore, lends itself to research aimed at social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is the "study of the meaning of phenomena or the study of the particular.” [5] At first glance, it might seem that Grounded Theory and Phenomenology are pretty similar, but the differences can be seen upon careful examination. At its core, phenomenology looks to investigate experiences from the individual's perspective. [2] Phenomenology is essentially looking into the "lived experiences" of the participants and aims to examine how and why participants behaved a certain way from their perspective. Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources. In contrast, Phenomenology focuses on describing and explaining an event or phenomenon from the perspective of those who have experienced it.

Narrative research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called a "thick" or "rich" description and is a strength of qualitative research. Narrative research is rife with the possibilities of "thick" description as this approach weaves together a sequence of events, usually from just one or two individuals, hoping to create a cohesive story or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be "opportunities for innovation." [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. [4] It is valuable for qualitative and quantitative researchers to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontologies and epistemologies. Ontology is defined as the "assumptions about the nature of reality,” whereas epistemology is defined as the "assumptions about the nature of knowledge" that inform researchers' work. [2] It is essential to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a complete understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, researchers must understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist versus postpositivist

To further understand qualitative research, we must discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social and natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in their research. It stems from positivist ontology, that there is an objective reality that exists that is wholly independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained, but could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world,” and therefore, postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are also constructivist, meaning they think there is no objective external reality that exists but instead that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. "Constructivism contends that individuals' views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality.” [6]  constructivist thought focuses on how "reality" is not a fixed certainty and how experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike positivist views, that there is not necessarily an "objective"reality we all experience. This is the ‘relativist’ ontological view that reality and our world are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have. It can even change the role of the researchers. [2] For example, is the researcher an "objective" observer, such as in positivist quantitative work? Or is the researcher an active participant in the research, as in postpositivist qualitative work? Understanding the philosophical base of the study undertaken allows researchers to fully understand the implications of their work and their role within the research and reflect on their positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale for being the most informative.
• Criterion sampling selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques, including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic, and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant-observers to share the experiences of the subject or non-participants or detached observers.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed, which may then be coded manually or using computer-assisted qualitative data analysis software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results could also be in the form of themes and theory or model development.

Dissemination

The healthcare team can use two reporting standards to standardize and facilitate the dissemination of qualitative research outcomes. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research. [13]

Applications

Many times, a research question will start with qualitative research. The qualitative research will help generate the research hypothesis, which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data to better understand what the numbers truly mean and their implications. The qualitative techniques can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research, researchers can explore poorly studied subjects with quantitative methods. These include opinions, individual actions, and social science research.

An excellent qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure no omissions of part of the target population. A proper collection method should be selected that will help obtain the desired information without overly limiting the collected data because, often, the information sought is not well categorized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of why teens start to smoke and factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered "cool," and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current nonsmokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the survey results to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the primary factor that keeps teens from starting to smoke, and peer pressure was the primary factor that contributed to teens starting smoking. The researcher can go back to qualitative research methods to dive deeper into these for more information. The researcher wants to focus on keeping teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly in the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure to smoke. The researcher finds a local park where many local teenagers hang out and sees that the smokers tend to hang out in a shady, overgrown area of the park. The researcher notes that smoking teenagers buy their cigarettes from a local convenience store adjacent to the park, where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk populations their perceptions of the changes and what factors are still at play, and quantitative research that includes teen smoking rates in the community and the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or combined with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation not only to help generate hypotheses that can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are. Qualitative research allows researchers to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many ways, including the evaluation criteria. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. Credibility, transferability, dependability, and confirmability are the correlating concepts in qualitative research. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept on the left and the qualitative concept on the right:

• Internal validity: Credibility
• External validity: Transferability
• Reliability: Dependability
• Objectivity: Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid, qualitative researchers should ensure that their work has credibility. 

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple data collection methods to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable if we interviewed the magician, backstage hand, and the person who "vanished." In qualitative research, triangulation can include telephone surveys, in-person surveys, focus groups, and interviews and surveying an adequate cross-section of the target demographic.
• Peer examination: A peer can review results to ensure the data is consistent with the findings.

A "thick" or "rich" description can be used to evaluate the transferability of qualitative research, whereas an indicator such as an audit trail might help evaluate the dependability and confirmability.

• Thick or rich description:  This is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was conducted. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original information records should also be kept (eg, surveys, notes, recordings).

One issue of concern that qualitative researchers should consider is observation bias. Here are a few examples:

• Hawthorne effect: The effect is the change in participant behavior when they know they are being observed. Suppose a researcher wanted to identify factors that contribute to employee theft and tell the employees they will watch them to see what factors affect employee theft. In that case, one would suspect employee behavior would change when they know they are being protected.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens unconsciously for the participant, so it is essential to eliminate or limit the transmission of the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in contrived scenarios with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative or quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research is not an island apart from quantitative research but an integral part of research methods to understand the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is essential for all healthcare team members as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research. Much of the qualitative research data acquisition is completed by numerous team members, including social workers, scientists, nurses, etc. Within each area of the medical field, there is copious ongoing qualitative research, including physician-patient interactions, nursing-patient interactions, patient-environment interactions, healthcare team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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• Systematic review
• Open access
• Published: 07 August 2024

Models and frameworks for assessing the implementation of clinical practice guidelines: a systematic review

• Nicole Freitas de Mello   ORCID: orcid.org/0000-0002-5228-6691 1 , 2 ,
• Sarah Nascimento Silva   ORCID: orcid.org/0000-0002-1087-9819 3 ,
• Dalila Fernandes Gomes   ORCID: orcid.org/0000-0002-2864-0806 1 , 2 ,
• Juliana da Motta Girardi   ORCID: orcid.org/0000-0002-7547-7722 4 &
• Jorge Otávio Maia Barreto   ORCID: orcid.org/0000-0002-7648-0472 2 , 4  

Implementation Science volume  19 , Article number:  59 ( 2024 ) Cite this article

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The implementation of clinical practice guidelines (CPGs) is a cyclical process in which the evaluation stage can facilitate continuous improvement. Implementation science has utilized theoretical approaches, such as models and frameworks, to understand and address this process. This article aims to provide a comprehensive overview of the models and frameworks used to assess the implementation of CPGs.

A systematic review was conducted following the Cochrane methodology, with adaptations to the "selection process" due to the unique nature of this review. The findings were reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Electronic databases were searched from their inception until May 15, 2023. A predetermined strategy and manual searches were conducted to identify relevant documents from health institutions worldwide. Eligible studies presented models and frameworks for assessing the implementation of CPGs. Information on the characteristics of the documents, the context in which the models were used (specific objectives, level of use, type of health service, target group), and the characteristics of each model or framework (name, domain evaluated, and model limitations) were extracted. The domains of the models were analyzed according to the key constructs: strategies, context, outcomes, fidelity, adaptation, sustainability, process, and intervention. A subgroup analysis was performed grouping models and frameworks according to their levels of use (clinical, organizational, and policy) and type of health service (community, ambulatorial, hospital, institutional). The JBI’s critical appraisal tools were utilized by two independent researchers to assess the trustworthiness, relevance, and results of the included studies.

Database searches yielded 14,395 studies, of which 80 full texts were reviewed. Eight studies were included in the data analysis and four methodological guidelines were additionally included from the manual search. The risk of bias in the studies was considered non-critical for the results of this systematic review. A total of ten models/frameworks for assessing the implementation of CPGs were found. The level of use was mainly policy, the most common type of health service was institutional, and the major target group was professionals directly involved in clinical practice. The evaluated domains differed between the models and there were also differences in their conceptualization. All the models addressed the domain "Context", especially at the micro level (8/12), followed by the multilevel (7/12). The domains "Outcome" (9/12), "Intervention" (8/12), "Strategies" (7/12), and "Process" (5/12) were frequently addressed, while "Sustainability" was found only in one study, and "Fidelity/Adaptation" was not observed.

Conclusions

The use of models and frameworks for assessing the implementation of CPGs is still incipient. This systematic review may help stakeholders choose or adapt the most appropriate model or framework to assess CPGs implementation based on their specific health context.

Trial registration

PROSPERO (International Prospective Register of Systematic Reviews) registration number: CRD42022335884. Registered on June 7, 2022.

Peer Review reports

Contributions to the literature

Although the number of theoretical approaches has grown in recent years, there are still important gaps to be explored in the use of models and frameworks to assess the implementation of clinical practice guidelines (CPGs). This systematic review aims to contribute knowledge to overcome these gaps.

Despite the great advances in implementation science, evaluating the implementation of CPGs remains a challenge, and models and frameworks could support improvements in this field.

This study demonstrates that the available models and frameworks do not cover all characteristics and domains necessary for a complete evaluation of CPGs implementation.

The presented findings contribute to the field of implementation science, encouraging debate on choices and adaptations of models and frameworks for implementation research and evaluation.

Substantial investments have been made in clinical research and development in recent decades, increasing the medical knowledge base and the availability of health technologies [ 1 ]. The use of clinical practice guidelines (CPGs) has increased worldwide to guide best health practices and to maximize healthcare investments. A CPG can be defined as "any formal statements systematically developed to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances" [ 2 ] and has the potential to improve patient care by promoting interventions of proven benefit and discouraging ineffective interventions. Furthermore, they can promote efficiency in resource allocation and provide support for managers and health professionals in decision-making [ 3 , 4 ].

However, having a quality CPG does not guarantee that the expected health benefits will be obtained. In fact, putting these devices to use still presents a challenge for most health services across distinct levels of government. In addition to the development of guidelines with high methodological rigor, those recommendations need to be available to their users; these recommendations involve the diffusion and dissemination stages, and they need to be used in clinical practice (implemented), which usually requires behavioral changes and appropriate resources and infrastructure. All these stages involve an iterative and complex process called implementation, which is defined as the process of putting new practices within a setting into use [ 5 , 6 ].

Implementation is a cyclical process, and the evaluation is one of its key stages, which allows continuous improvement of CPGs development and implementation strategies. It consists of verifying whether clinical practice is being performed as recommended (process evaluation or formative evaluation) and whether the expected results and impact are being reached (summative evaluation) [ 7 , 8 , 9 ]. Although the importance of the implementation evaluation stage has been recognized, research on how these guidelines are implemented is scarce [ 10 ]. This paper focused on the process of assessing CPGs implementation.

To understand and improve this complex process, implementation science provides a systematic set of principles and methods to integrate research findings and other evidence-based practices into routine practice and improve the quality and effectiveness of health services and care [ 11 ]. The field of implementation science uses theoretical approaches that have varying degrees of specificity based on the current state of knowledge and are structured based on theories, models, and frameworks [ 5 , 12 , 13 ]. A "Model" is defined as "a simplified depiction of a more complex world with relatively precise assumptions about cause and effect", and a "framework" is defined as "a broad set of constructs that organize concepts and data descriptively without specifying causal relationships" [ 9 ]. Although these concepts are distinct, in this paper, their use will be interchangeable, as they are typically like checklists of factors relevant to various aspects of implementation.

There are a variety of theoretical approaches available in implementation science [ 5 , 14 ], which can make choosing the most appropriate challenging [ 5 ]. Some models and frameworks have been categorized as "evaluation models" by providing a structure for evaluating implementation endeavors [ 15 ], even though theoretical approaches from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 13 ]. Two frameworks that can specify implementation aspects that should be evaluated as part of intervention studies are RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [ 16 ] and PRECEDE-PROCEED (Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development) [ 17 ]. Although the number of theoretical approaches has grown in recent years, the use of models and frameworks to evaluate the implementation of guidelines still seems to be a challenge.

This article aims to provide a complete map of the models and frameworks applied to assess the implementation of CPGs. The aim is also to subside debate and choices on models and frameworks for the research and evaluation of the implementation processes of CPGs and thus to facilitate the continued development of the field of implementation as well as to contribute to healthcare policy and practice.

A systematic review was conducted following the Cochrane methodology [ 18 ], with adaptations to the "selection process" due to the unique nature of this review (details can be found in the respective section). The review protocol was registered in PROSPERO (registration number: CRD42022335884) on June 7, 2022. This report adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 19 ] and a completed checklist is provided in Additional File 1.

Eligibility criteria

The SDMO approach (Types of Studies, Types of Data, Types of Methods, Outcomes) [ 20 ] was utilized in this systematic review, outlined as follows:

Types of studies

All types of studies were considered for inclusion, as the assessment of CPG implementation can benefit from a diverse range of study designs, including randomized clinical trials/experimental studies, scale/tool development, systematic reviews, opinion pieces, qualitative studies, peer-reviewed articles, books, reports, and unpublished theses.

Studies were categorized based on their methodological designs, which guided the synthesis, risk of bias assessment, and presentation of results.

Study protocols and conference abstracts were excluded due to insufficient information for this review.

Types of data

Studies that evaluated the implementation of CPGs either independently or as part of a multifaceted intervention.

Guidelines for evaluating CPG implementation.

Inclusion of CPGs related to any context, clinical area, intervention, and patient characteristics.

No restrictions were placed on publication date or language.

Exclusion criteria

General guidelines were excluded, as this review focused on 'models for evaluating clinical practice guidelines implementation' rather than the guidelines themselves.

Studies that focused solely on implementation determinants as barriers and enablers were excluded, as this review aimed to explore comprehensive models/frameworks.

Studies evaluating programs and policies were excluded.

Studies that only assessed implementation strategies (isolated actions) rather than the implementation process itself were excluded.

Studies that focused solely on the impact or results of implementation (summative evaluation) were excluded.

Types of methods

Not applicable.

All potential models or frameworks for assessing the implementation of CPG (evaluation models/frameworks), as well as their characteristics: name; specific objectives; levels of use (clinical, organizational, and policy); health system (public, private, or both); type of health service (community, ambulatorial, hospital, institutional, homecare); domains or outcomes evaluated; type of recommendation evaluated; context; limitations of the model.

Model was defined as "a deliberated simplification of a phenomenon on a specific aspect" [ 21 ].

Framework was defined as "structure, overview outline, system, or plan consisting of various descriptive categories" [ 21 ].

Models or frameworks used solely for the CPG development, dissemination, or implementation phase.

Models/frameworks used solely for assessment processes other than implementation, such as for the development or dissemination phase.

Data sources and literature search

The systematic search was conducted on July 31, 2022 (and updated on May 15, 2023) in the following electronic databases: MEDLINE/PubMed, Centre for Reviews and Dissemination (CRD), the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Epistemonikos, Global Health, Health Systems Evidence, PDQ-Evidence, PsycINFO, Rx for Change (Canadian Agency for Drugs and Technologies in Health, CADTH), Scopus, Web of Science and Virtual Health Library (VHL). The Google Scholar database was used for the manual selection of studies (first 10 pages).

Additionally, hand searches were performed on the lists of references included in the systematic reviews and citations of the included studies, as well as on the websites of institutions working on CPGs development and implementation: Guidelines International Networks (GIN), National Institute for Health and Care Excellence (NICE; United Kingdom), World Health Organization (WHO), Centers for Disease Control and Prevention (CDC; USA), Institute of Medicine (IOM; USA), Australian Department of Health and Aged Care (ADH), Healthcare Improvement Scotland (SIGN), National Health and Medical Research Council (NHMRC; Australia), Queensland Health, The Joanna Briggs Institute (JBI), Ministry of Health and Social Policy of Spain, Ministry of Health of Brazil and Capes Theses and Dissertations Catalog.

The search strategy combined terms related to "clinical practice guidelines" (practice guidelines, practice guidelines as topic, clinical protocols), "implementation", "assessment" (assessment, evaluation), and "models, framework". The free term "monitoring" was not used because it was regularly related to clinical monitoring and not to implementation monitoring. The search strategies adapted for the electronic databases are presented in an additional file (see Additional file 2).

Study selection process

The results of the literature search from scientific databases, excluding the CRD database, were imported into Mendeley Reference Management software to remove duplicates. They were then transferred to the Rayyan platform ( https://rayyan.qcri.org ) [ 22 ] for the screening process. Initially, studies related to the "assessment of implementation of the CPG" were selected. The titles were first screened independently by two pairs of reviewers (first selection: four reviewers, NM, JB, SS, and JG; update: a pair of reviewers, NM and DG). The title screening was broad, including all potentially relevant studies on CPG and the implementation process. Following that, the abstracts were independently screened by the same group of reviewers. The abstract screening was more focused, specifically selecting studies that addressed CPG and the evaluation of the implementation process. In the next step, full-text articles were reviewed independently by a pair of reviewers (NM, DG) to identify those that explicitly presented "models" or "frameworks" for assessing the implementation of the CPG. Disagreements regarding the eligibility of studies were resolved through discussion and consensus, and by a third reviewer (JB) when necessary. One reviewer (NM) conducted manual searches, and the inclusion of documents was discussed with the other reviewers.

Risk of bias assessment of studies

The selected studies were independently classified and evaluated according to their methodological designs by two investigators (NM and JG). This review employed JBI’s critical appraisal tools to assess the trustworthiness, relevance and results of the included studies [ 23 ] and these tools are presented in additional files (see Additional file 3 and Additional file 4). Disagreements were resolved by consensus or consultation with the other reviewers. Methodological guidelines and noncomparative and before–after studies were not evaluated because JBI does not have specific tools for assessing these types of documents. Although the studies were assessed for quality, they were not excluded on this basis.

Data extraction

The data was independently extracted by two reviewers (NM, DG) using a Microsoft Excel spreadsheet. Discrepancies were discussed and resolved by consensus. The following information was extracted:

Document characteristics : author; year of publication; title; study design; instrument of evaluation; country; guideline context;

Usage context of the models : specific objectives; level of use (clinical, organizational, and policy); type of health service (community, ambulatorial, hospital, institutional); target group (guideline developers, clinicians; health professionals; health-policy decision-makers; health-care organizations; service managers);

Model and framework characteristics : name, domain evaluated, and model limitations.

The set of information to be extracted, shown in the systematic review protocol, was adjusted to improve the organization of the analysis.

The "level of use" refers to the scope of the model used. "Clinical" was considered when the evaluation focused on individual practices, "organizational" when practices were within a health service institution, and "policy" when the evaluation was more systemic and covered different health services or institutions.

The "type of health service" indicated the category of health service where the model/framework was used (or can be used) to assess the implementation of the CPG, related to the complexity of healthcare. "Community" is related to primary health care; "ambulatorial" is related to secondary health care; "hospital" is related to tertiary health care; and "institutional" represented models/frameworks not specific to a particular type of health service.

The "target group" included stakeholders related to the use of the model/framework for evaluating the implementation of the CPG, such as clinicians, health professionals, guideline developers, health policy-makers, health organizations, and service managers.

The category "health system" (public, private, or both) mentioned in the systematic review protocol was not found in the literature obtained and was removed as an extraction variable. Similarly, the variables "type of recommendation evaluated" and "context" were grouped because the same information was included in the "guideline context" section of the study.

Some selected documents presented models or frameworks recognized by the scientific field, including some that were validated. However, some studies adapted the model to this context. Therefore, the domain analysis covered all models or frameworks domains evaluated by (or suggested for evaluation by) the document analyzed.

Data analysis and synthesis

The results were tabulated using narrative synthesis with an aggregative approach, without meta-analysis, aiming to summarize the documents descriptively for the organization, description, interpretation and explanation of the study findings [ 24 , 25 ].

The model/framework domains evaluated in each document were studied according to Nilsen et al.’s constructs: "strategies", "context", "outcomes", "fidelity", "adaptation" and "sustainability". For this study, "strategies" were described as structured and planned initiatives used to enhance the implementation of clinical practice [ 26 ].

The definition of "context" varies in the literature. Despite that, this review considered it as the set of circumstances or factors surrounding a particular implementation effort, such as organizational support, financial resources, social relations and support, leadership, and organizational culture [ 26 , 27 ]. The domain "context" was subdivided according to the level of health care into "micro" (individual perspective), "meso" (organizational perspective), "macro" (systemic perspective), and "multiple" (when there is an issue involving more than one level of health care).

The "outcomes" domain was related to the results of the implementation process (unlike clinical outcomes) and was stratified according to the following constructs: acceptability, appropriateness, feasibility, adoption, cost, and penetration. All these concepts align with the definitions of Proctor et al. (2011), although we decided to separate "fidelity" and "sustainability" as independent domains similar to Nilsen [ 26 , 28 ].

"Fidelity" and "adaptation" were considered the same domain, as they are complementary pieces of the same issue. In this study, implementation fidelity refers to how closely guidelines are followed as intended by their developers or designers. On the other hand, adaptation involves making changes to the content or delivery of a guideline to better fit the needs of a specific context. The "sustainability" domain was defined as evaluations about the continuation or permanence over time of the CPG implementation.

Additionally, the domain "process" was utilized to address issues related to the implementation process itself, rather than focusing solely on the outcomes of the implementation process, as done by Wang et al. [ 14 ]. Furthermore, the "intervention" domain was introduced to distinguish aspects related to the CPG characteristics that can impact its implementation, such as the complexity of the recommendation.

A subgroup analysis was performed with models and frameworks categorized based on their levels of use (clinical, organizational, and policy) and the type of health service (community, ambulatorial, hospital, institutional) associated with the CPG. The goal is to assist stakeholders (politicians, clinicians, researchers, or others) in selecting the most suitable model for evaluating CPG implementation based on their specific health context.

Search results

Database searches yielded 26,011 studies, of which 107 full texts were reviewed. During the full-text review, 99 articles were excluded: 41 studies did not mention a model or framework for assessing the implementation of the CPG, 31 studies evaluated only implementation strategies (isolated actions) rather than the implementation process itself, and 27 articles were not related to the implementation assessment. Therefore, eight studies were included in the data analysis. The updated search did not reveal additional relevant studies. The main reason for study exclusion was that they did not use models or frameworks to assess CPG implementation. Additionally, four methodological guidelines were included from the manual search (Fig.  1 ).

PRISMA diagram. Acronyms: ADH—Australian Department of Health, CINAHL—Cumulative Index to Nursing and Allied Health Literature, CDC—Centers for Disease Control and Prevention, CRD—Centre for Reviews and Dissemination, GIN—Guidelines International Networks, HSE—Health Systems Evidence, IOM—Institute of Medicine, JBI—The Joanna Briggs Institute, MHB—Ministry of Health of Brazil, NICE—National Institute for Health and Care Excellence, NHMRC—National Health and Medical Research Council, MSPS – Ministerio de Sanidad Y Política Social (Spain), SIGN—Scottish Intercollegiate Guidelines Network, VHL – Virtual Health Library, WHO—World Health Organization. Legend: Reason A –The study evaluated only implementation strategies (isolated actions) rather than the implementation process itself. Reason B – The study did not mention a model or framework for assessing the implementation of the intervention. Reason C – The study was not related to the implementation assessment. Adapted from Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 . For more information, visit:

According to the JBI’s critical appraisal tools, the overall assessment of the studies indicates their acceptance for the systematic review.

The cross-sectional studies lacked clear information regarding "confounding factors" or "strategies to address confounding factors". This was understandable given the nature of the study, where such details are not typically included. However, the reviewers did not find this lack of information to be critical, allowing the studies to be included in the review. The results of this methodological quality assessment can be found in an additional file (see Additional file 5).

In the qualitative studies, there was some ambiguity regarding the questions: "Is there a statement locating the researcher culturally or theoretically?" and "Is the influence of the researcher on the research, and vice versa, addressed?". However, the reviewers decided to include the studies and deemed the methodological quality sufficient for the analysis in this article, based on the other information analyzed. The results of this methodological quality assessment can be found in an additional file (see Additional file 6).

Documents characteristics (Table  1 )

The documents were directed to several continents: Australia/Oceania (4/12) [ 31 , 33 , 36 , 37 ], North America (4/12 [ 30 , 32 , 38 , 39 ], Europe (2/12 [ 29 , 35 ] and Asia (2/12) [ 34 , 40 ]. The types of documents were classified as cross-sectional studies (4/12) [ 29 , 32 , 34 , 38 ], methodological guidelines (4/12) [ 33 , 35 , 36 , 37 ], mixed methods studies (3/12) [ 30 , 31 , 39 ] or noncomparative studies (1/12) [ 40 ]. In terms of the instrument of evaluation, most of the documents used a survey/questionnaire (6/12) [ 29 , 30 , 31 , 32 , 34 , 38 ], while three (3/12) used qualitative instruments (interviews, group discussions) [ 30 , 31 , 39 ], one used a checklist [ 37 ], one used an audit [ 33 ] and three (3/12) did not define a specific instrument to measure [ 35 , 36 , 40 ].

Considering the clinical areas covered, most studies evaluated the implementation of nonspecific (general) clinical areas [ 29 , 33 , 35 , 36 , 37 , 40 ]. However, some studies focused on specific clinical contexts, such as mental health [ 32 , 38 ], oncology [ 39 ], fall prevention [ 31 ], spinal cord injury [ 30 ], and sexually transmitted infections [ 34 ].

Usage context of the models (Table  1 )

Specific objectives.

All the studies highlighted the purpose of guiding the process of evaluating the implementation of CPGs, even if they evaluated CPGs from generic or different clinical areas.

Levels of use

The most common level of use of the models/frameworks identified to assess the implementation of CPGs was policy (6/12) [ 33 , 35 , 36 , 37 , 39 , 40 ]. In this level, the model is used in a systematic way to evaluate all the processes involved in CPGs implementation and is primarily related to methodological guidelines. This was followed by the organizational level of use (5/12) [ 30 , 31 , 32 , 38 , 39 ], where the model is used to evaluate the implementation of CPGs in a specific institution, considering its specific environment. Finally, the clinical level of use (2/12) [ 29 , 34 ] focuses on individual practice and the factors that can influence the implementation of CPGs by professionals.

Type of health service

Institutional services were predominant (5/12) [ 33 , 35 , 36 , 37 , 40 ] and included methodological guidelines and a study of model development and validation. Hospitals were the second most common type of health service (4/12) [ 29 , 30 , 31 , 34 ], followed by ambulatorial (2/12) [ 32 , 34 ] and community health services (1/12) [ 32 ]. Two studies did not specify which type of health service the assessment addressed [ 38 , 39 ].

Target group

The focus of the target group was professionals directly involved in clinical practice (6/12) [ 29 , 31 , 32 , 34 , 38 , 40 ], namely, health professionals and clinicians. Other less related stakeholders included guideline developers (2/12) [ 39 , 40 ], health policy decision makers (1/12) [ 39 ], and healthcare organizations (1/12) [ 39 ]. The target group was not defined in the methodological guidelines, although all the mentioned stakeholders could be related to these documents.

Model and framework characteristics

Models and frameworks for assessing the implementation of cpgs.

The Consolidated Framework for Implementation Research (CFIR) [ 31 , 38 ] and the Promoting Action on Research Implementation in Health Systems (PARiHS) framework [ 29 , 30 ] were the most commonly employed frameworks within the selected documents. The other models mentioned were: Goal commitment and implementation of practice guidelines framework [ 32 ]; Guideline to identify key indicators [ 35 ]; Guideline implementation checklist [ 37 ]; Guideline implementation evaluation tool [ 40 ]; JBI Implementation Framework [ 33 ]; Reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework [ 34 ]; The Guideline Implementability Framework [ 39 ] and an unnamed model [ 36 ].

Domains evaluated

The number of domains evaluated (or suggested for evaluation) by the documents varied between three and five, with the majority focusing on three domains. All the models addressed the domain "context", with a particular emphasis on the micro level of the health care context (8/12) [ 29 , 31 , 34 , 35 , 36 , 37 , 38 , 39 ], followed by the multilevel (7/12) [ 29 , 31 , 32 , 33 , 38 , 39 , 40 ], meso level (4/12) [ 30 , 35 , 39 , 40 ] and macro level (2/12) [ 37 , 39 ]. The "Outcome" domain was evaluated in nine models. Within this domain, the most frequently evaluated subdomain was "adoption" (6/12) [ 29 , 32 , 34 , 35 , 36 , 37 ], followed by "acceptability" (4/12) [ 30 , 32 , 35 , 39 ], "appropriateness" (3/12) [ 32 , 34 , 36 ], "feasibility" (3/12) [ 29 , 32 , 36 ], "cost" (1/12) [ 35 ] and "penetration" (1/12) [ 34 ]. Regarding the other domains, "Intervention" (8/12) [ 29 , 31 , 34 , 35 , 36 , 38 , 39 , 40 ], "Strategies" (7/12) [ 29 , 30 , 33 , 35 , 36 , 37 , 40 ] and "Process" (5/12) [ 29 , 31 , 32 , 33 , 38 ] were frequently addressed in the models, while "Sustainability" (1/12) [ 34 ] was only found in one model, and "Fidelity/Adaptation" was not observed. The domains presented by the models and frameworks and evaluated in the documents are shown in Table  2 .

Limitations of the models

Only two documents mentioned limitations in the use of the model or frameworks. These two studies reported limitations in the use of CFIR: "is complex and cumbersome and requires tailoring of the key variables to the specific context", and "this framework should be supplemented with other important factors and local features to achieve a sound basis for the planning and realization of an ongoing project" [ 31 , 38 ]. Limitations in the use of other models or frameworks are not reported.

Subgroup analysis

Following the subgroup analysis (Table  3 ), five different models/frameworks were utilized at the policy level by institutional health services. These included the Guideline Implementation Evaluation Tool [ 40 ], the NHMRC tool (model name not defined) [ 36 ], the JBI Implementation Framework + GRiP [ 33 ], Guideline to identify key indicators [ 35 ], and the Guideline implementation checklist [ 37 ]. Additionally, the "Guideline Implementability Framework" [ 39 ] was implemented at the policy level without restrictions based on the type of health service. Regarding the organizational level, the models used varied depending on the type of service. The "Goal commitment and implementation of practice guidelines framework" [ 32 ] was applied in community and ambulatory health services, while "PARiHS" [ 29 , 30 ] and "CFIR" [ 31 , 38 ] were utilized in hospitals. In contexts where the type of health service was not defined, "CFIR" [ 31 , 38 ] and "The Guideline Implementability Framework" [ 39 ] were employed. Lastly, at the clinical level, "RE-AIM" [ 34 ] was utilized in ambulatory and hospital services, and PARiHS [ 29 , 30 ] was specifically used in hospital services.

Key findings

This systematic review identified 10 models/ frameworks used to assess the implementation of CPGs in various health system contexts. These documents shared similar objectives in utilizing models and frameworks for assessment. The primary level of use was policy, the most common type of health service was institutional, and the main target group of the documents was professionals directly involved in clinical practice. The models and frameworks presented varied analytical domains, with sometimes divergent concepts used in these domains. This study is innovative in its emphasis on the evaluation stage of CPG implementation and in summarizing aspects and domains aimed at the practical application of these models.

The small number of documents contrasts with studies that present an extensive range of models and frameworks available in implementation science. The findings suggest that the use of models and frameworks to evaluate the implementation of CPGs is still in its early stages. Among the selected documents, there was a predominance of cross-sectional studies and methodological guidelines, which strongly influenced how the implementation evaluation was conducted. This was primarily done through surveys/questionnaires, qualitative methods (interviews, group discussions), and non-specific measurement instruments. Regarding the subject areas evaluated, most studies focused on a general clinical area, while others explored different clinical areas. This suggests that the evaluation of CPG implementation has been carried out in various contexts.

The models were chosen independently of the categories proposed in the literature, with their usage categorized for purposes other than implementation evaluation, as is the case with CFIR and PARiHS. This practice was described by Nilsen et al. who suggested that models and frameworks from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 14 , 15 , 42 , 43 ].

The results highlight the increased use of models and frameworks in evaluation processes at the policy level and institutional environments, followed by the organizational level in hospital settings. This finding contradicts a review that reported the policy level as an area that was not as well studied [ 44 ]. The use of different models at the institutional level is also emphasized in the subgroup analysis. This may suggest that the greater the impact (social, financial/economic, and organizational) of implementing CPGs, the greater the interest and need to establish well-defined and robust processes. In this context, the evaluation stage stands out as crucial, and the investment of resources and efforts to structure this stage becomes even more advantageous [ 10 , 45 ]. Two studies (16,7%) evaluated the implementation of CPGs at the individual level (clinical level). These studies stand out for their potential to analyze variations in clinical practice in greater depth.

In contrast to the level of use and type of health service most strongly indicated in the documents, with systemic approaches, the target group most observed was professionals directly involved in clinical practice. This suggests an emphasis on evaluating individual behaviors. This same emphasis is observed in the analysis of the models, in which there is a predominance of evaluating the micro level of the health context and the "adoption" subdomain, in contrast with the sub-use of domains such as "cost" and "process". Cassetti et al. observed the same phenomenon in their review, in which studies evaluating the implementation of CPGs mainly adopted a behavioral change approach to tackle those issues, without considering the influence of wider social determinants of health [ 10 ]. However, the literature widely reiterates that multiple factors impact the implementation of CPGs, and different actions are required to make them effective [ 6 , 46 , 47 ]. As a result, there is enormous potential for the development and adaptation of models and frameworks aimed at more systemic evaluation processes that consider institutional and organizational aspects.

In analyzing the model domains, most models focused on evaluating only some aspects of implementation (three domains). All models evaluated the "context", highlighting its significant influence on implementation [ 9 , 26 ]. Context is an essential effect modifier for providing research evidence to guide decisions on implementation strategies [ 48 ]. Contextualizing a guideline involves integrating research or other evidence into a specific circumstance [ 49 ]. The analysis of this domain was adjusted to include all possible contextual aspects, even if they were initially allocated to other domains. Some contextual aspects presented by the models vary in comprehensiveness, such as the assessment of the "timing and nature of stakeholder engagement" [ 39 ], which includes individual engagement by healthcare professionals and organizational involvement in CPG implementation. While the importance of context is universally recognized, its conceptualization and interpretation differ across studies and models. This divergence is also evident in other domains, consistent with existing literature [ 14 ]. Efforts to address this conceptual divergence in implementation science are ongoing, but further research and development are needed in this field [ 26 ].

The main subdomain evaluated was "adoption" within the outcome domain. This may be attributed to the ease of accessing information on the adoption of the CPG, whether through computerized system records, patient records, or self-reports from healthcare professionals or patients themselves. The "acceptability" subdomain pertains to the perception among implementation stakeholders that a particular CPG is agreeable, palatable or satisfactory. On the other hand, "appropriateness" encompasses the perceived fit, relevance or compatibility of the CPG for a specific practice setting, provider, or consumer, or its perceived fit to address a particular issue or problem [ 26 ]. Both subdomains are subjective and rely on stakeholders' interpretations and perceptions of the issue being analyzed, making them susceptible to reporting biases. Moreover, obtaining this information requires direct consultation with stakeholders, which can be challenging for some evaluation processes, particularly in institutional contexts.

The evaluation of the subdomains "feasibility" (the extent to which a CPG can be successfully used or carried out within a given agency or setting), "cost" (the cost impact of an implementation effort), and "penetration" (the extent to which an intervention or treatment is integrated within a service setting and its subsystems) [ 26 ] was rarely observed in the documents. This may be related to the greater complexity of obtaining information on these aspects, as they involve cross-cutting and multifactorial issues. In other words, it would be difficult to gather this information during evaluations with health practitioners as the target group. This highlights the need for evaluation processes of CPGs implementation involving multiple stakeholders, even if the evaluation is adjusted for each of these groups.

Although the models do not establish the "intervention" domain, we thought it pertinent in this study to delimit the issues that are intrinsic to CPGs, such as methodological quality or clarity in establishing recommendations. These issues were quite common in the models evaluated but were considered in other domains (e.g., in "context"). Studies have reported the importance of evaluating these issues intrinsic to CPGs [ 47 , 50 ] and their influence on the implementation process [ 51 ].

The models explicitly present the "strategies" domain, and its evaluation was usually included in the assessments. This is likely due to the expansion of scientific and practical studies in implementation science that involve theoretical approaches to the development and application of interventions to improve the implementation of evidence-based practices. However, these interventions themselves are not guaranteed to be effective, as reported in a previous review that showed unclear results indicating that the strategies had affected successful implementation [ 52 ]. Furthermore, model domains end up not covering all the complexity surrounding the strategies and their development and implementation process. For example, the ‘Guideline implementation evaluation tool’ evaluates whether guideline developers have designed and provided auxiliary tools to promote the implementation of guidelines [ 40 ], but this does not mean that these tools would work as expected.

The "process" domain was identified in the CFIR [ 31 , 38 ], JBI/GRiP [ 33 ], and PARiHS [ 29 ] frameworks. While it may be included in other domains of analysis, its distinct separation is crucial for defining operational issues when assessing the implementation process, such as determining if and how the use of the mentioned CPG was evaluated [ 3 ]. Despite its presence in multiple models, there is still limited detail in the evaluation guidelines, which makes it difficult to operationalize the concept. Further research is needed to better define the "process" domain and its connections and boundaries with other domains.

The domain of "sustainability" was only observed in the RE-AIM framework, which is categorized as an evaluation framework [ 34 ]. In its acronym, the letter M stands for "maintenance" and corresponds to the assessment of whether the user maintains use, typically longer than 6 months. The presence of this domain highlights the need for continuous evaluation of CPGs implementation in the short, medium, and long term. Although the RE-AIM framework includes this domain, it was not used in the questionnaire developed in the study. One probable reason is that the evaluation of CPGs implementation is still conducted on a one-off basis and not as a continuous improvement process. Considering that changes in clinical practices are inherent over time, evaluating and monitoring changes throughout the duration of the CPG could be an important strategy for ensuring its implementation. This is an emerging field that requires additional investment and research.

The "Fidelity/Adaptation" domain was not observed in the models. These emerging concepts involve the extent to which a CPG is being conducted exactly as planned or whether it is undergoing adjustments and adaptations. Whether or not there is fidelity or adaptation in the implementation of CPGs does not presuppose greater or lesser effectiveness; after all, some adaptations may be necessary to implement general CPGs in specific contexts. The absence of this domain in all the models and frameworks may suggest that they are not relevant aspects for evaluating implementation or that there is a lack of knowledge of these complex concepts. This may suggest difficulty in expressing concepts in specific evaluative questions. However, further studies are warranted to determine the comprehensiveness of these concepts.

It is important to note the customization of the domains of analysis, with some domains presented in the models not being evaluated in the studies, while others were complementarily included. This can be seen in Jeong et al. [ 34 ], where the "intervention" domain in the evaluation with the RE-AIM framework reinforced the aim of theoretical approaches such as guiding the process and not determining norms. Despite this, few limitations were reported for the models, suggesting that the use of models in these studies reflects the application of these models to defined contexts without a deep critical analysis of their domains.

Limitations

This review has several limitations. First, only a few studies and methodological guidelines that explicitly present models and frameworks for assessing the implementation of CPGs have been found. This means that few alternative models could be analyzed and presented in this review. Second, this review adopted multiple analytical categories (e.g., level of use, health service, target group, and domains evaluated), whose terminology has varied enormously in the studies and documents selected, especially for the "domains evaluated" category. This difficulty in harmonizing the taxonomy used in the area has already been reported [ 26 ] and has significant potential to confuse. For this reason, studies and initiatives are needed to align understandings between concepts and, as far as possible, standardize them. Third, in some studies/documents, the information extracted was not clear about the analytical category. This required an in-depth interpretative process of the studies, which was conducted in pairs to avoid inappropriate interpretations.

Implications

This study contributes to the literature and clinical practice management by describing models and frameworks specifically used to assess the implementation of CPGs based on their level of use, type of health service, target group related to the CPG, and the evaluated domains. While there are existing reviews on the theories, frameworks, and models used in implementation science, this review addresses aspects not previously covered in the literature. This valuable information can assist stakeholders (such as politicians, clinicians, researchers, etc.) in selecting or adapting the most appropriate model to assess CPG implementation based on their health context. Furthermore, this study is expected to guide future research on developing or adapting models to assess the implementation of CPGs in various contexts.

The use of models and frameworks to evaluate the implementation remains a challenge. Studies should clearly state the level of model use, the type of health service evaluated, and the target group. The domains evaluated in these models may need adaptation to specific contexts. Nevertheless, utilizing models to assess CPGs implementation is crucial as they can guide a more thorough and systematic evaluation process, aiding in the continuous improvement of CPGs implementation. The findings of this systematic review offer valuable insights for stakeholders in selecting or adjusting models and frameworks for CPGs evaluation, supporting future theoretical advancements and research.

Availability of data and materials

Abbreviations.

Australian Department of Health and Aged Care

Canadian Agency for Drugs and Technologies in Health

Centers for Disease Control and

Consolidated Framework for Implementation Research

Cumulative Index to Nursing and Allied Health Literature

Clinical practice guideline

Centre for Reviews and Dissemination

Guidelines International Networks

Getting Research into Practice

Health Systems Evidence

Institute of Medicine

The Joanna Briggs Institute

Ministry of Health of Brazil

Ministerio de Sanidad y Política Social

National Health and Medical Research Council

National Institute for Health and Care Excellence

Promoting action on research implementation in health systems framework

Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

International Prospective Register of Systematic Reviews

Reach, effectiveness, adoption, implementation, and maintenance framework

Healthcare Improvement Scotland

United States of America

Virtual Health Library

World Health Organization

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This study is supported by the Fundação de Apoio à Pesquisa do Distrito Federal (FAPDF). FAPDF Award Term (TOA) nº 44/2024—FAPDF/SUCTI/COOBE (SEI/GDF – Process 00193–00000404/2024–22). The content in this article is solely the responsibility of the authors and does not necessarily represent the official views of the FAPDF.

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NFM and JOMB conceived the idea and the protocol for this study. NFM conducted the literature search. NFM, SNS, JMG and JOMB conducted the data collection with advice and consensus gathering from JOMB. The NFM and JMG assessed the quality of the studies. NFM and DFG conducted the data extraction. NFM performed the analysis and synthesis of the results with advice and consensus gathering from JOMB. NFM drafted the manuscript. JOMB critically revised the first version of the manuscript. All the authors revised and approved the submitted version.

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13012_2024_1389_moesm1_esm.docx.

Additional file 1: PRISMA checklist. Description of data: Completed PRISMA checklist used for reporting the results of this systematic review.

Additional file 2: Literature search. Description of data: The search strategies adapted for the electronic databases.

13012_2024_1389_moesm3_esm.doc.

Additional file 3: JBI’s critical appraisal tools for cross-sectional studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for cross-sectional studies.

13012_2024_1389_MOESM4_ESM.doc

Additional file 4: JBI’s critical appraisal tools for qualitative studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for qualitative studies.

13012_2024_1389_MOESM5_ESM.doc

Additional file 5: Methodological quality assessment results for cross-sectional studies. Description of data: Methodological quality assessment results for cross-sectional studies using JBI’s critical appraisal tools.

13012_2024_1389_MOESM6_ESM.doc

Additional file 6: Methodological quality assessment results for the qualitative studies. Description of data: Methodological quality assessment results for qualitative studies using JBI’s critical appraisal tools.

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Freitas de Mello, N., Nascimento Silva, S., Gomes, D.F. et al. Models and frameworks for assessing the implementation of clinical practice guidelines: a systematic review. Implementation Sci 19 , 59 (2024). https://doi.org/10.1186/s13012-024-01389-1

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The context and development of teachers’ collective reflections on student data.

1. Introduction

2. interpreting student thinking, interpreting student thinking in figured worlds, 3. materials and methods, 3.1. unpacking and understanding students’ mathematical thinking, 3.1.1. cognitive interview reports, 3.1.2. debriefing conversations about the cognitive interviews, 3.2. data analysis, 4.1. first-grade teachers’ collective reflections.

Leah: Interesting. How they don’t really have–we’ve been working on it a lot, kind of drawing a picture of what is happening in the story, if they don’t even really even know where to start. {But} they’re recognizing the numbers, they saw the five and the three. But they’re not getting three tables {and} what that really means, kind of not paying attention to maybe all those details or not really sure how to draw that out. Lucy: I think they’re also just overgeneralizing. They’re so used to us just adding or just subtracting. They just assume all the problems are going to do that. Facilitator: I wondered about the visualization…I wondered about that phrase, whether kids were familiar with that phrase sitting at every table. Leah: These guys and our kindergarten guys haven’t really seen anybody more than two sitting at a table. It’s not like we’re sitting at tables like normal for these past two years. You know what I mean? Delilah: But they should, because we did tons of work with subitizing. They should be able to picture a five on a dice. When you say there’s five kids at that table… Lucy: With five frames they always did. Delilah: Yeah. They should be able to {imagine} that amount and say, oh yeah, five and five, and there’s a five…They should have had that skill down pat. Because we do that fluency with them constantly.

Situating Interpretations into Figured Worlds

4.2. second-grade teachers’ collective reflections.

Evelyn: If we see the word ‘altogether’ is this addition or subtraction, and it’s just like jump starting them selecting their strategy before they start. Madison: Yeah, yeah. Because I think I’ve noticed too, even last year, my kids have always had issues of like when there’s a missing part. If it is 38 plus blank equals {81}, finding that missing part. It’s almost like if it’s not in order 32 plus 15 equals…or subtraction order, my guys get confused by that. Of what to do with it having that missing part. Evelyn: They don’t like it when they get the sum first in the question. That’s the end goal. Madison: Yeah. That way. Right. It’ll say, 72 equals 32 plus what. It’s just if the signs are mixed up or a part is missing it throws them off… Because on the assessment, they have to end up doing that at the end of the module. But I probably need to practice that more. Allison: Or just go back to those number bonds with part-part-whole, where we have the whole {and} we have the part. How do we solve it? Evelyn: They’re so used to doing repetitive, rote, 40 problems in a row that are the exact same kind of problem. But they’re not really thinking about it, it’s just– Allison: Yeah. Like a repetition thing. Evelyn: Yeah. But they’re not really thinking about “what is the author asking you to solve here?” Allison: Yeah. I also know that they like to rush. Madison: Yes. Evelyn: Yeah, yeah. Allison: They think everything’s a race. Evelyn: They do. And I find that a lot of the computer programs that we have, it’s like they get that instant gratification when they move on. It’s like a video game, moving on to the next level.

5. Discussion

5.1. collective interpretations as collective sensemaking, 5.2. teachers’ figured worlds—challenges with computer-based curriculum, 5.3. limitations, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Prough, S.; Webster, A.; Gibbons, L.K. The Context and Development of Teachers’ Collective Reflections on Student Data. Educ. Sci. 2024 , 14 , 859. https://doi.org/10.3390/educsci14080859

Prough S, Webster A, Gibbons LK. The Context and Development of Teachers’ Collective Reflections on Student Data. Education Sciences . 2024; 14(8):859. https://doi.org/10.3390/educsci14080859

Prough, Sam, Amber Webster, and Lynsey K. Gibbons. 2024. "The Context and Development of Teachers’ Collective Reflections on Student Data" Education Sciences 14, no. 8: 859. https://doi.org/10.3390/educsci14080859

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A transcendental phenomenological study of factors that contribute to first-generation minority college students' motivation in bachelor's degree attainment.

Garry Anthony Mebane , Liberty University Follow

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Sharon Michael-Chadwell

First-generation college students, First-generation minority college students, Social Cognitive Learning, Self-efficacy, Persistent

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Mebane, Garry Anthony, "A Transcendental Phenomenological Study of Factors That Contribute to First-Generation Minority College Students' Motivation in Bachelor's Degree Attainment" (2024). Doctoral Dissertations and Projects . 5845. https://digitalcommons.liberty.edu/doctoral/5845

The purpose of this transcendental phenomenological study was to describe the lived experiences that motivated and contributed to first-generation minority college student success (FGMCSS) at a university in the southeastern United States. This study involved 12 first-generation minority college students (FGMCS) enrolled in an undergraduate degree program. Bandura's social cognitive theory guided this research, which explains that the human learning process develops through social context. Research questions emerged from understanding the problem and purpose statements. The question guiding this research was: What are the experiences that motivate and contribute to FGMCS success at a university in the southeastern United States? Data was collected using interviews, focus groups, and journaling. Data analysis entailed using epoché, phenomenological reduction, and creative variation techniques to uncover and explore emergent themes. The data analysis revealed six distinct themes: difficulties in adjusting to college life, involvement in various groups and activities, determination to complete a degree, confidence in overcoming obstacles, lack of support, and emotional and resource assistance received, which emphasized the intricate interaction of motivation, assistance, and obstacles encountered by FGMCS. The findings also uncovered several complex factors that contribute to the success of FGMCS, such as the desire for personal and family improvement, even in the face of doubt and lack of support. Additional research is needed to investigate successful tactics and interventions that may help overcome the many obstacles experienced by first-generation minority college students, eventually leading to their successful completion of degrees and academic achievements.

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Scientific understanding in biomedical research

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• Published: 02 August 2024
• Volume 204 , article number  66 , ( 2024 )

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• Somogy Varga   ORCID: orcid.org/0000-0001-9383-7843 1 , 2  

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Motivated by a recent trend that advocates a reassessment of the aim of medical science and clinical practice, this paper investigates the epistemic aims of biomedical research. Drawing on contemporary discussions in epistemology and the philosophy of science, along with a recent study on scurvy, this paper (1) explores the concept of understanding as the aim of scientific inquiry and (2) establishes a framework that will guide the examination of its forms in biomedical research. Using the case of Tuberculosis (TB), (3) it is argued that grasping a mechanistic explanation is crucial for reaching a threshold of understanding at which we may speak of an objectual, biomedical understanding of TB.

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Within just a few years, multiple editorials in prominent medical journals have issued a call to reflect on the aim of clinical medicine and medical science. Focusing on the latter matter, in a 2013 editorial, the editors of The Lancet clarified that they frequently confront not only queries about the rationale behind specific scientific studies but also broader inquiries regarding the overarching aim of medical science, which includes both clinical and medical laboratory research. They contend that the moment has come to rethink our approach to conducting and incentivizing research, and for this, “we need to remind ourselves about the real purpose of research” (The Lancet, 2013 , p. 347; see also Thornton, 2013 ). The authors express concern that a significant portion of the vast sums allocated annually to biomedical research fails to meet its true objectives. This shortfall is attributed not only to deficiencies in research design and methodology but also to a lack of “clinical meaningfulness.” Specifically, they highlight that many research projects pose questions that are not sufficiently aligned with clinical medicine and relevant to the treatment, control, prevention, or prediction of diseases. The authors note that the issues extend beyond merely reducing the potential impact of biomedical research; they suggest a fundamental misunderstanding of the very purpose of biomedical research, implying that such studies may not truly qualify as medical . Footnote 1

This short Lancet piece highlights significant, yet often overlooked, questions concerning the epistemic aim of medical research. This paper will address these questions, positing that medical science is fundamentally engaged in inquiries aimed at achieving what we shall refer to as biomedical understanding . To investigate and clarify what such understanding amounts to, the paper takes inspiration from two sources. On the one hand, it draws on contemporary discussions in philosophy of science and epistemology, which have seen a renewed interest in understanding as a distinct cognitive accomplishment (Grimm, 2021 ; Baumberger et al., 2017 ), as the epistemic aim of scientific inquiry, and the measure of progress (see e.g., Potochnik, 2015 ; De Regt & Dieks, 2005 ; Elgin, 2017 ). Acknowledging that what constitutes proper understanding can depend on the field, as noted by scholars in the field (Strevens, 2010 ; De Regt et al., 2009 ), this paper aims to specifically articulate what understanding entails within medical science. On the other hand, this paper draws on and employs several distinctions from a recent study on scurvy (Varga, 2023 ). However, while that study focused on a noncommunicable condition stemming from severe dietary deficiencies, this paper shifts our focus to Tuberculosis (TB), a multifaceted and emblematic infectious disease often accompanied by stigma (WHO, 2023 ). TB, which is one of the oldest known infectious diseases, is caused by the bacterium Mycobacterium tuberculosis (Kapur et al., 1994 ; Daniel, 2006 ). The bacteria are transmitted when an infected individual coughs, sneezes, or speaks, allowing another person to breathe in the pathogens. Symptoms of TB can include coughing, chest pain, fatigue, fever, and night sweats and although the condition is treatable with antibiotics it remains a major global health concern.

The paper is organized as follows. It (1) explores the idea of understanding as the aim of scientific inquiry and (2) lays down a framework of understanding that will subsequently guide our exploration of its forms in medicine. Using the case of Tuberculosis (TB), (3) it is argued that grasping a mechanistic explanation is crucial for reaching a threshold of understanding at which we may speak of an objectual, biomedical understanding of TB. If evidence can be gathered to support this argument, it would align with the previously mentioned research on a noncommunicable disease (scurvy), suggesting a recurring pattern across various contexts of medical research.

1 The aim of scientific inquiry: constitutive aim and truth

Scientific inquiries can be viewed as extensions of our day-to-day endeavors to gather information albeit executed in a more systematic manner (see e.g., Kelp, 2021 ). They are goal-directed activities, implying that there is some aim that inquiry strives to accomplish. It is quite natural to assume that this description also fits medical science; however, before delving into the question of what constitutes the epistemic aim of medical science, it is crucial to first briefly clarify what medical science refers to. What sets medical science apart and qualifies something as specifically medical science, rather than just science in general?

Medical science, which includes clinical research and laboratory research in medicine, is fundamentally based on the life sciences. Over the past two centuries, it has extensively leveraged discoveries in biology that have identified cellular, genetic, and molecular entities and processes that help explain the development and course of diseases. While some aspects of medical science may not differ essentially from laboratory sciences within contributing disciplines such as biology, biochemistry, and physiology, medical science cannot simply be reduced to the sum of these fields. One reason is that medical research is only deemed properly medical when it has a specific practical orientation—that is, when it is driven by the goal of contributing to clinical medicine, which primarily focuses on the diagnosis, prevention, and treatment of disease. Without this practical focus, research might be more accurately described as biological rather than medical. Take, for example, large-scale laboratory research that aims to chart the functions of specific limbic structures in the brain. Without a practical focus on clinical applications or health outcomes, such research might be more accurately described as neurobiological rather than medical. Of course, this research could potentially yield benefits for clinical medicine in the future, but without a direct and immediate practical orientation, it would not be classified as medical research. Moreover, if we were to classify such research as medical merely based on potential future benefits, the distinction between medical and non-medical research would collapse.

Of course, this practical orientation toward health outcomes is a characteristic that biomedical research shares with related fields such as public health. However, their epistemic aims are directed towards different objectives: biomedical research typically focuses on the biological and physiological aspects of diseases at a molecular or cellular level, aiming to elucidate disease mechanisms and develop new treatments, whereas public health is primarily concerned with improving the health of populations through prevention strategies, health education, surveillance, and improving access to health care. Public health aims encompass a wide array of objectives: ensuring safe environments by controlling hazards in air, water, and food, enhancing host resistance through balanced nutrition and immunization, promoting health-supportive behaviors, and improving equitable access to health and social services (White et al., 2013 ; Munthe, 2008 ). Footnote 2

Although both fields are dedicated to conducting research with the final aim to improve health outcomes, they operate with different priorities and methodologies, each aligned with their distinct epistemic goals. A biomedical researcher might delve into the genetic factors that contribute to the resistance of TB to antibiotics, focusing on molecular and cellular details. In contrast, public health initiatives may not require such knowledge; instead, they often concentrate on understanding societal or other health factors that hinder the implementation of vaccination programs or public campaigns aimed at increasing awareness and prevention of TB.

Having briefly clarified at least some of the aspects that set medical science apart, we can now turn to the question of its aim. As we begin to explore this, it is worth considering how plausible it is to claim that scientific inquiries in medicine are driven by a single aim. This consideration is crucial because the diversity of methods, approaches, and priorities within medical science suggests that its objectives might not be unified under a single overarching aim. In response, it is important to clarify that for the purposes of this paper, we do not assert that medical science is driven by a single aim. Instead, among potentially other aims, our objective is to explore the nature of medical science’s epistemic aim , which also determines what counts as progress at least in this limited sense. Thus, very roughly, if A is the aim of inquiry, then medical science makes progress when A accumulates or increases (for a discussion, see Bird, 2007 ; Varga, 2024 ).

So what is the epistemic aim of scientific inquiry in medicine? According to a plausible suggestion, the aim is simply to discover truths about health and disease and correct past errors (e.g., false beliefs about diseases like scurvy or depression being caused by humoral imbalance) that were based on tradition, cognitive errors, ideologies, or religious dogma. Correspondingly, progress consists in a cumulative acquisition of true beliefs. For example, until the nineteenth century, the prevailing belief was that TB was inherited or caused by environmental factors such as bad air or poor living conditions. But already in 1720, the English physician Benjamin Marten hypothesized that TB and its symptomatic lesions in the lungs are caused by “species of Animalcula or wonderfully minute living Creatures” that can be transmitted “by very frequently conversing so nearly as to draw inpart of the breath he emits from the lungs” (cited in Cambau & Drancourt, 2014 ; Daniel, 2006 ). Supporting this hypothesis, in 1865, the French physician Jean-Antoine Villemin provided experimental evidence that TB could be transmitted. He observed that TB was more prevalent in people living close and in poorly ventilated buildings, and he noted that while TB was common among troops in barracks, it decreased during military campaigns when soldiers were not housed (Daniel et al., 1994 ). Thus, Marten and Villemin unearthed truths regarding TB, rectified previous mistakes, and aided in the ongoing accumulation of accurate beliefs, which constitutes progress.

On its face, the suggestion that the aim of medical research is simply to discover truths is plausible. After all, it is often said that scientific inquiries are in the “truth business” (Pennock, 2019 ; Lipton, 2004 ), and it is difficult to imagine that contemporary medical science would be able to achieve what it does if its claims would not at least roughly correspond to how the world actually is. Nonetheless, the acquisition of true beliefs does not seem sufficient to constitute progress. Footnote 3 Take, for instance, a scenario where Marten and Villemin arrived at the same conclusion through unreliable methods and, coincidentally, the theory they came to accept happened to be true. In that case, Marten and Villemin would have acquired a true belief, but it would not have counted as genuine progress. What would be lacking is suitable justification for holding the relevant belief. In other words, the belief that they would have acquired would not qualify as knowledge .

1.1 Knowledge and understanding

What we learn from these considerations is that progress not only requires that our beliefs and theories be true but that we have attained adequate reasons for forming them. If this is correct, then it seems safe to conclude that the aim of inquiry is not merely truth, but knowledge (achieved by reliable means), which would mean that progress consists in the increase not of true beliefs, but of knowledge. Although this correction marks an improvement, it is necessary to supply some clarifications and caveats.

First, the aim of inquiry cannot simply be the mere accumulation of knowledge. Medical science has an expansive range of questions at its disposal, and it could potentially attain a vast pool of knowledge, but much of this potential knowledge might be trivial or inconsequential, lacking the impact or significance to be deemed progress. Imagine that researchers could come to know everything about some minor and transient symptom (e.g., a slight, transient change in nail coloration or longitudinal nail ridging) observed in a small subset of TB patients that are known not to have bearing on the disease’s diagnosis, progression, or response to treatment. While detailed knowledge of these symptoms might add to the clinical descriptions of TB, the reason this gained knowledge is not considered significant or constitutive of true progress likely stems from its limited impact on key areas of TB research and clinical management. It lacks the potential to advance our understanding of TB (or indeed other medically relevant conditions), uncover new treatment targets, enhance diagnostic methods, or deepen our understanding of disease transmission and resistance mechanisms.

If we accept this line of reasoning, then the aim of inquiry in medicine cannot be simply to amass knowledge, but rather a selective process that prioritizes the acquisition of certain kinds of significant knowledge. Hence, part of the scientific endeavor involves a critical evaluation process to identify which pieces of knowledge are significant and worth pursuing. This selection process is fundamental to progress, ensuring that scientific efforts are directed toward areas of genuine importance and potential impact (Kitcher, 2001 ; Dupré, 2016 ). Identifying and focusing on significant knowledge, therefore, becomes a crucial aspect of the scientific method, guiding researchers in making meaningful advancements rather than merely expanding the repository of human knowledge.

While the aim of inquiry is significant knowledge, the selection process to identify which pieces of knowledge count as significant cannot be extracted from nature and is largely relative to specific interests. As Kitcher ( 2001 , 61) stated regarding scientific inquiry in general, “significant science must be understood in the context of a particular group with particular practical interests and a particular history”. In the context of TB, it is far more plausible to suggest that what constitutes significant knowledge is closely interwoven with practical concerns related to the understanding and treatment of TB.

Having discussed the issue of significance, we are now faced with a final challenge that questions the notion that the goal of inquiry in medical science is best described as the pursuit of knowledge. In recent years, numerous philosophers of science have contended that framing the aim of inquiry in terms of understanding offers significant benefits over viewing progress merely as an accumulation of knowledge. The advantage with comprehending progress in terms of increased understanding is that it avoids the challenges faced by accounts measuring scientific progress in terms of knowledge (see e.g., Elgin, 2007 , 2017 ; De Regt & Dieks, 2005 ; Potochnik, 2017 ). Footnote 4 First, traditional accounts have problems explaining the significance of certain pragmatic virtues (e.g., simplicity) that do not affect the truth of claims, theories, and explanations. In contrast, an account of progress based on the notion of understanding does not face this problem, as these pragmatic virtues clearly affect the ability to understand (Dellsén, 2016 ). Second, traditional accounts of progress as knowledge accumulation have problems explaining abstractions, approximations, and idealizations. For example, in medicine, physiological accounts often offer idealized and simplified descriptions of organs and their functions (Ereshefsky, 2009 ). These provide computational tractability and improve understanding, but they also include aspects that are, strictly taken, inaccurate or false. However, such falsehoods are, as Elgin ( 2017 ) puts it, “felicitous”: although they involve false representations, they also exemplify significant aspects of phenomena in a tractable manner. Several philosophers have argued that science can increase understanding and contribute to progress even if it involves departing from the truth (e.g., Elgin, 2009a , b ; Strevens, 2017 ; Potochnik, 2015 ).

On an account of progress in terms of knowledge, the presence of manifest falsehoods seems incompatible with progress. However, an account of progress in terms of understanding fares better here, since understanding is compatible with a limited number of falsehoods, which are outweighed by practical advantages. Strevens argues that idealized models can provide understanding, but in a somewhat more limited way, showing why some causal factors are difference-makers and others are not (Strevens, 2017 ). Potochnik ( 2017 , 102; 2015 ) holds that while idealizations cannot be true or approximately true, they can be epistemically acceptable. Because such idealizations are rampant in science and they always detract from the truth, truth does not seem to be a good candidate for describing the aim of science. However, given that idealizations can support understanding, it is more adequate to suppose that understanding is what science aims at.

The latter is not susceptible to such worries, because, in contradistinction to knowledge, understanding is only quasi-factive: it can survive false beliefs if they are not absolutely vital to the phenomenon in question. For example, Marten hypothesized that TB was caused by “species of Animalcula or wonderfully minute living Creatures” (Doetsch, 1978 ; Daniel et al., 1994 ). Strictly taken, this is false: TB was not caused by such small creatures, but by the Mycobacterium tuberculosis bacteria, which Marten had no knowledge of. Nevertheless, it is hard to deny that some progress occurred and an increase in the (objectual) understanding of TB had been obtained.

In all, as opposed to truth or knowledge, the epistemic aim of scientific inquiry is best comprehended as understanding. Comprehending progress in terms of increased understanding dovetails more closely with the pragmatic nature of medicine and has the advantage of being resistant to some of the problems that haunt accounts that comprehend progress as knowledge accumulation. If the epistemic goal of inquiry is best framed as seeking understanding, this raises questions about what understanding is in medical research. The following sections will initially delve into theories of understanding, followed by an examination of the specific nature of understanding within the realm of medicine.

2 Forms of understanding

The debates on understanding have focused on three types of understanding: propositional understanding (understanding that something is the case), explanatory understanding (understanding why something is the case), and objectual understanding (understanding a particular topic or subject matter) (see e.g., Kvanvig, 2003 ; Hannon, 2021 ; Grimm, 2021 ). Footnote 5 In what follows, we are going to be focusing on explanatory and objectual understanding, in part because propositional understanding is often largely reducible to propositional knowledge or explanatory understanding. For example, saying “he understands that he needs to come to TB screening” could amount to the attribution of propositional knowledge (“he knows that he needs to come to TB screening”) or to explanatory understanding (“he understands why it is important for him to come to TB screening”). Of course, there are many other examples of how the term “understanding” is used. But many of them are either reducible to claims about knowledge, objectual understanding or explanatory understanding. For example, when we say that a person really understands how x works, then we are attributing to this person some degree of objectual understanding of x.

To illustrate the difference between knowledge and understanding, consider the example of TB. A student of medicine may attend a lecture on infectious diseases and come to know from a reliable source that TB is caused by Mycobacterium tuberculosis. Accepting the testimony from a reliable source and even double checking it in an encyclopedia of infectious diseases, the student gains causal knowledge. But while the student now knows a proposition that picks out the cause of TB, that is not enough for explanatory understanding, which not only requires knowledge of what caused the effect, but also grasping how that cause brings about the effect (Kvanvig, 2003 ; Pritchard, 2010a ), which many take to involves a type of “skill” (see e.g., De Regt, 2017 ). Understanding does not only require the possession of a theory or model, but also the skill or ability to use it to discern the causal relationship involved. One way to comprehend the difference is that unless explanatory understanding about how cause and effect are related is attained, she will be unable to address what-if-things-had-been-different questions or predict the outcomes of potential interventions (Grimm, 2011 ).

For another example, consider an utterly false theory leading to correct results. Charles Locock’s mid-19th century discovery of the anticonvulsant effect of potassium bromide. Locock, a physician working in London, shared the widely accepted theory among his contemporaries of a causal relationship between masturbation, convulsions, and epilepsy (Ban, 2006 ). As bromides were known to reduce the sex drive, Locock reasoned that the ingestion of potassium bromides would control convulsions by reducing the rate of masturbation. His account of the drug’s effectiveness was published in The Lancet in 1857, and subsequent independent studies confirmed potassium bromide’s antiepileptic efficacy, albeit evidently not by reducing masturbation frequency. Through observations and inference to the best explanation, Locock had attained knowledge that potassium bromide reduced convulsions, and such knowledge allowed the introduction of a relatively effective antiepileptic treatment into medical practice.

Still, in an important sense, such causal knowledge does not properly close the inquiry, which would require grasping a correct explanation and attaining understanding of what happens and how cause and effect are related. Locock did not understand why potassium bromide was effective, why it failed to be effective in some people, and so on. This meant that he lacked the ability to improve the efficiency of the intervention, since he was unable to counterfactually anticipate the effects of changes he could have made with respect to the treatment. More precisely, the lack of understanding means that Locock was unable to (i) predict the changes that would occur if the factors cited as explanatory were different and (ii) to draw correct inferences about similar situations under slightly varied conditions.

2.1 Explanatory and objectual understanding

Objectual and explanatory understanding differ in several ways (Kvanvig, 2003 , 2009 ; Hannon, 2019 , 2021 ). Explanatory understanding involves grasping why something is the case (e.g., uncovering the causal mechanisms or reasons behind phenomena) and its scope is less expansive than that of objectual understanding (Hannon, 2021 ). Objectual understanding, usually expressed using the verb “understands”, followed by a noun, as in the phrase “she understands TB”, entails a comprehensive grasp of a particular topic or subject matter, which includes incorporating these causal explanations into a broader context. While explanatory understanding is often necessary, it is not sufficient for objectual understanding, which requires integrating these explanatory insights within a larger framework.

To illustrate the difference, imagine that our student has now acquired knowledge of a vast number of isolated facts about TB, such that her peers would not hesitate to say that she has knowledge about TB. Nonetheless, this would not imply that the student understands TB, which would attribute to the student a more profound penetration of TB, a sort of epistemic acquaintance that is more profound than knowing particular propositions (Kvanvig, 2003 , p. 191; Strevens, 2017 ). Her objectual understanding of TB is gradable and can always become more profound along various dimensions (Bengson, 2017 ).

Often, achieving (full) objectual understanding is the aim of inquiry, and reaching it justifiably concludes the investigation of the topic (Kvanvig, 2013 ). If we think of medical research, objectual understanding seems to better capture the primary aim of inquiry and the conditions under which it can be concluded. To take the example of TB, researchers not only want to understand why it arises or why certain characteristic biochemical reactions occur but also why it leads to the characteristic symptoms, why it has varied effects on individuals, how it relates to other conditions, and so on. Even though single research projects cannot take on such a large task, the ultimate goal seems to go beyond obtaining explanatory understanding of features of TB to systematically understanding TB , which means attaining some level of coherence and completeness in terms of knowledge, as well as in taxonomies and classifications.

A prevalent perspective posits that achieving objectual understanding marks the endpoint of inquiry and legitimately closes the investigation into the subject (Kvanvig, 2013 ; Carter and Gordon 2014). This perspective aligns well with medicine, where an objectual understanding of a condition, rather than just its explanation, is often the ultimate aim. In their pursuit of understanding TB, researchers aim to grasp not just its origins, but also its manifestations, correlations with other conditions, its varied effects on individuals, and the most useful systematic categorization of its characteristic symptoms and signs.

Some argue that objectual understanding is not merely a subset of explanatory understanding, in part because it is possible to achieve objectual understanding of indeterministic systems where explanatory relations do not obtain (Kvanvig, 2009 ). But even if this turns out to be false (see e.g., Khalifa, 2013 , ch. 4), maintaining this distinction conserves the intuition that when we attribute to somebody objectual understanding of a subject matter (as opposed to explanatory understanding), we imply that the agent’s epistemic commitments relevant to the subject matter form a coherent network. Also, the distinction upholds the idea that objectual understanding’s factivity requirement is more lenient, making it less susceptible to peripheral falsehoods compared to explanatory understanding (see e.g., Elgin, 2017 ; Bamberger, Beisbart, & Brun 2017; Kvanvig, 2009 ).

2.2 Grasping explanations and context-dependency

Both explanatory and objectual understanding go beyond mere knowledge by encompassing an additional cognitive achievement, often referred to as a form of “grasping” (e.g., de Regt, 2009 ; Strevens, 2017 ; Grimm, 2014 ; Elgin, 2017 ; for a critique, see Khalifa, 2013 , ch. 3). The objects of grasping are “explanatory and other coherence-making relationships” (Kvanvig, 2003 , p. 192). There is no clear agreement on the precise meaning of “grasping” (Hannon, 2019 ), but for our purposes we might conceptualize it as a form of cognitive control that agents develop through the active engagement of their epistemic agency in delineating conceptual and explanatory links. Importantly, while grasping enables agents to mentally map a relational assembly (Grimm, 2014 ), it is not reducible to the experience of understanding (e.g., an “aha” moment): good explanations do not necessarily trigger a sense of understanding, while inadequate explanations sometimes do (Trout, 2002 ). While philosophers commonly concur that what is being grasped are explanations, aligning with the notion that the primary purpose of scientific explanation is to foster understanding (Lipton, 2001 ), opinions differ on what kind of explanations lead to understanding, such as deductive-nomological explanations (Hempel & Oppenheim, 1948 ), or mechanistic explanations, which explain phenomena by specifying the mechanisms that produce them (Salmon, 1984 ; Machamer et al., 2000 ). Footnote 6

Importantly, what counts as understanding, is – at least in a limited sense – context-sensitive . This can be interpreted in several ways. First, some argue that understanding is context-sensitive in the sense that the criteria for understanding can evolve even within a single scientific discipline (for historical examples, see De Regt, 2017 ; De Regt et al., 2009 ). This is in part because the capacity of an explanation to lead to understanding is partially contingent upon the disciplinary background and knowledge of individuals seeking to understand.

Second, and more importantly for our aims here, some hold that context-sensitivity is linked to the nature and aim of the particular scientific inquiry. For example, Craver ( 2013 ; Kendler et al., 2011 ) contends that mechanistic explanations are inherently contextual and “perspectival”, as they are framed within a specific explanatory framework that is chosen based on explanatory interests. While this point may be limited to mechanistic explanations, there are indications that objectual understanding displays some context-sensitivity across scientific fields. To illustrate this with a medical example, consider the study of cholesterol metabolism in medical science and chemistry. In medical science, a significant level of objectual understanding of cholesterol metabolism arguably encompasses an understanding of how cholesterol levels are regulated (e.g., by diet, genetics) and how they can be modified through interventions or lifestyle changes to reduce the risk of disease. From the perspective of chemistry, objectual understanding of cholesterol metabolism does not necessarily relate to cardiovascular health but instead focuses on explaining the biochemical pathways of cholesterol breakdown and synthesis, elucidating the precise molecular interactions involved. Thus, what constitutes some sufficient level of objectual understanding in medicine might differ from that in chemistry, primarily because the explanatory goals and interests in medicine are intrinsically tied to practical applications and clinical medicine. There is no inherent tension between context-sensitivity and objectual understanding: even if the threshold for sufficient objectual understanding can be consistent across disciplines, the kinds of explanations needed to reach this understanding vary according to the specific context and the explanatory, practical and other goals of each field.

3 Biomedical understanding

While the presented account of understanding does not purport to capture the intricacies of philosophical debates on the topic, it serves as a basis for exploring what it means to possess objectual understanding of a disease within the medical field. This will be referred to as biomedical understanding (see Varga, 2023 , 2024 ). To grasp what biomedical understanding entails, let us revisit the history of TB research.

Before the 19th century, tuberculosis (TB) was thought to result from heredity or environmental causes like bad air. Marten’s initial hypothesis that “minute living creatures” could spread TB was later validated by Villemin, who in 1865 provided experimental evidence of TB’s transmissibility. He linked its higher incidence to crowded, inadequately ventilated environments and noted a decrease in TB cases among soldiers when they were not confined to cramped barracks (Daniel et al., 1994 ; Bynum, 2012 ). Moreover, by removing liquid from tuberculous cavities of individuals who had died of TB and injecting it into healthy animals, Villemin successfully transmitted the disease from humans to rabbits, from cows to rabbits, and from rabbits to rabbits. Throughout his studies, he used the same amount of liquid and animals of similar origin, age, and habitat conditions, such that “everything indeed other than inoculation, were identical” (Villemin 1868/2015 , 257). While not all animals developed symptoms, autopsies three months later revealed that the vast majority developed extensive TB with massive dissemination of tubercles to the organs (Villemin 1868/2015 ; Barnes, 2000 ).

Clearly, Villemin’s findings helped distinguish between variables that had a direct effect on the development of TB and those that were correlated with it (e.g., certain professions, poverty, poor living conditions). However, while Villemin attained an important piece of explanatory understanding, it would be unwarranted to say that he obtained objectual understanding of TB in any noteworthy sense. Given that the explanatory goals and interests in medicine are closely tied to practical applications, such a claim might seem excessive because the explanatory understanding Villemin obtained did not form a coherent network that would have allowed him to consider how possible medical interventions could limit control the progression and spread of TB. After all, Villemin did not understand under what conditions TB developed, how it transmitted, and what the agent of the disease was, except that the tubercle (nodular lesion) contained it.

Let us now look closer at some shortcomings that could have prevented him from attaining objectual understanding of TB in any substantial sense. The first shortcoming stems from an incomplete understanding of the causal agent. Villemin lacked comprehension with respect to two critical aspects of the causal connection: stability and specificity (see Woodward, 2010 ). A causal link between the injected substance and TB is considered stable if the counterfactual dependence remains consistent across various background situations. Villemin’s studies did not provide much evidence with respect to stability, because they did not involve testing under different background circumstances. In addition, specificity refers to the grain level of counterfactual dependencies between the inoculated substance and TB. Because Villemin inoculated the same amount of substance in each case, his studies offered no knowledge about the extent to which the intensity of tuberculization depends on the amount of substance inoculated. Villemin had no way of determining whether the counterfactual dependencies between the inoculated substance and TB are fine-grained, in which case intervention on the inoculated substance would enable more precise control over how TB develops.

Moreover, Villemin’s incomplete understanding of the causal agent prevented him from ruling out the possibility that experimentally induced tuberculosis might follow a different pathway from ordinary TB or could even be a distinct disease altogether. When injecting liquids from organisms that succumbed from TB, one could argue that the effects obtained were not due to TB, but to the injection containing some “cadaveric material.” Although Villemin could show that the number and extent of lesions on the lungs are not correlated with the number and extent of lesions developed at the injection site, he himself noted a crucial limitation: “should we consider the entire chain of phenomena observed in experimental tuberculosis as the result of a traumatism due to inoculation? This is an enigma that we cannot resolve” (Villemin 1868/2015 , 259).

The second shortcoming concerns a lack of knowledge about the relevant mechanism. The causal knowledge Villemin attained did not permit “tracing” the causal process (Steel, 2008 ), which would have assisted grasping coherence-making relationships and comprehending how the elements of TB are configured. This seems to necessitate some degree of explanatory understanding and discerning the mechanism that is responsible for linking cause and effect. A mechanism for phenomenon P consists of parts and processes that are structured in a way such that they are responsible for P (Glennan et al., 2021 ). Explanations in the biomedical sciences are most frequently mechanistic, explaining a disease by identifying the spatiotemporal structure of a mechanism that is responsible for that disease and its symptoms (Thagard, 2005 ; Darrason, 2018 ; Williamson, 2019 ). Villemin’s study establishes a coarse-grained difference-making relationship, but it does not amount to biomedical understanding because it fails to discern the correct mechanism.

We could say that the lack of such a mechanism has crucially impacted Villemin’s ability to gather sufficient evidence for explanatory understanding. There are two possibilities here, depending on which thesis one subscribes to regarding the role of mechanisms in establishing causal claims (for discussions, see Russo & Williamson, 2007 ; Illari, 2011 ; Williamson, 2019 ). According to a strong thesis, establishing a causal relationship requires not only difference-making evidence but also evidence of a mechanism composed by entities (such as proteins) and processes (such as protein expression) that together link cause and effect. If one accepts the strong thesis, then Villemin has not met the criteria for establishing a causal relationship because he had no knowledge of the mechanism. According to a weaker thesis, difference-making can serve as evidence for a causal relationship. However, evidence of a mechanism, combined with difference-making evidence, significantly increases certainty that the observed correlation is not merely spurious and that the effect can be attributed to the experimental intervention rather than to confounding variables.

Having examined these two shortcomings, it appears likely that each has contributed to the failure to attain objectual understanding. However, it is unclear whether any of these factors are essential for achieving objectual understanding. In the sections that follow, we will explore the historical development of tuberculosis research to further investigate this issue.

3.1 Koch and beyond

A significant breakthrough with respect to the first two shortcomings came with Robert Koch’s 1882 discovery of the bacterium Mycobacterium tuberculosis (MTB) as the causative agent of TB (Keshavjee & Farmer, 2012 ). Footnote 7 Koch formalized a set of “postulates” for establishing causation, which required (a) coincidence of bacteria and disease, (b) isolation of bacteria in a pure culture, and (c) induction of disease by inoculation with bacteria from pure culture. As to (a), Koch was able to show that the MTB were always present in TB (but not in normal states), that they preceded tubercle formation, and that their number covaried with TB being progressive or quiescent. As to (b), Koch managed to isolate individual colonies of MTB in pure culture that allowed studying their growth characteristics. As to (c), he inoculated animals with MTB obtained from various origins (induced disease, spontaneous disease, and artificial culture). Koch found that injections led to the formation of tubercles with similar characteristics, and the number of tubercles corresponded to the amount of the inoculum used (Blevins & Bronze, 2010 ).

While Koch’s postulates can be interpreted in various ways (e.g., Broadbent, 2009 ), some have argued that Koch’s experimental distinction of causal from correlational relationships are best captured by the interventionist account of causation (Ross & Woodward, 2016 ). Interventionism posits that causal relationships are those that can be potentially harnessed for manipulation and control: very roughly, if intervening on C reliably leads to changes in E, then C is the cause of E. Woodward ( 2003 ) outlines the necessary and sufficient criteria for establishing causation as follows: C causes E if and only if (i) there is some possible intervention on C such that (ii) were this intervention to occur, there would be an association or correlation between C and E. The account highlights idealized experimental intervention as appropriate for the purposes of determining whether C causes E, as it eliminates possibility of confounding. As the induced change is not correlated with potential confounders, the presence of a correlation between C and E upon intervention on C means that C has a causal influence on E.

Interventionism fits Koch’s postulates, particularly his emphasis on (c), i.e., the induction of disease into a healthy animal by inoculation with bacteria from pure culture. In fact, Koch clearly maintains that determining causality between MTB and TB “can only be decided by inoculating pure bacilli,” thus step (c) (quoted in Ross & Woodward, 2016 , p. 44). Footnote 8 Of course, (b) can be seen as a procedure to ensure that (c) obtains the characteristics of a proper intervention: it excludes the possibility that confounding factors are contained in the inoculated material. Causal claims can only be established if the intervention is associated with a change in the incidence of TB (e.g., its presence, absence, rate of occurrence). In accordance with (M), if the inoculation of substances had not led to the occurrence of disease, Koch would not have identified them as the cause of the disease.

Although the discovery of the causal agent addressed the first shortcoming in Villemin’s research, it alone was insufficient to resolve the second shortcoming concerning the mechanism. However, this is clearly a significant issue, in part because it connects with important questions from a clinical perspective. Without an understanding of the mechanism, questions about what holds together the symptoms of TB, whether certain characteristics (e.g., diarrhea) are parts of TB or caused by TB, how MTB is disseminated to other organs, why most individuals with latent infection do not develop the disease, cannot be answered.

3.2 Twentieth-century discoveries

In the twentieth century, a notable breakthrough came with the identification of the mechanism through which MTB interacts intricately with the host’s immune system, leading to TB. Roughly, when MTB reaches the lungs, it is taken up by macrophages, which are immune cells that engulf and destroy foreign particles. However, MTB is able to survive and replicate within the macrophages, which leads to the formation of granulomas that surround the infected macrophages to contain the infection. MTB is sometimes able to resist destruction and containment, eventually causing the macrophages to burst and release more bacteria into the surrounding tissue. The infected tissue becomes inflamed, leading to the formation of the characteristic lesions, or granulomas, in the lungs and other organs. The granulomas can restrict the infection, leading to a latent TB infection, or they can break down, releasing MTB into the lungs, where it can be coughed up and spread to others (for reviews, see Delogu et al., 2013 ; Yan et al., 2022 ).

The mechanism was elucidated over several decades through the significant contributions of numerous researchers. Therefore, it is challenging to pinpoint exactly when and by which researchers a threshold was crossed, marking a stage at which we may speak of researchers having attained objectual understanding of TB. However, once a mechanistic explanation became available that referenced the configuration and activities of component entities, and identified both the normal functioning of macrophages and how MTB disrupts this process, it seems quite intuitive to say that researchers had achieved a significant level of objectual, biomedical understanding of TB. Researchers have progressed beyond merely explaining various aspects of TB; they have crossed a threshold into systematically, objectually understanding TB .

Of course, while this assertion may seem intuitively appealing, it alone raises a crucial question: what is it about mechanistic explanations that renders them necessary for achieving a significant level of objectual understanding? In what follows, the aim will be to show that mechanistic explanations have enabled achieving a level of coherence and integration, offering clear potential to refine theoretical frameworks and clinical practices, and to facilitate the development of more comprehensive taxonomies and classifications. But before doing so, it is worth emphasizing that a sufficient level of objectual biomedical understanding of TB has been achieved, not merely by grasping the relevant mechanistic explanations, but also by integrating this with other pieces of knowledge and understanding already obtained.

For this, we may start by noting how a mechanistic explanation not only overcomes the second shortcoming observed in the research of Villemin and Koch but also enables new insights that carry profound implications for diagnosis, treatment, and prevention strategies, directly affecting patient care and public health initiatives. This underscores an earlier argument that what constitutes a sufficient level of objectual understanding in medicine is context-sensitive and closely linked to a practical orientation. Let us now review a couple of important implications for research and clinical settings.

First, grasping the relevant mechanistic explanation, researchers were able to chart a much more fine-grained intricate web of counterfactual dependencies, which paves the road towards enhanced intervention possibilities concerning TB. Researchers can formulate new hypotheses around potential interventions, such as enhancing the macrophages’ capability to eradicate MTB or inhibiting MTB’s ability to prevent acidification within macrophages (for a review of current research, see e.g., Bo et al., 2023 ).

Second, comprehending the mechanism significantly enhances the ability to interpret and address a range of clinically relevant issues. It provides a unified view of TB, clarifying how its various elements are interrelated, and explaining how seemingly disparate symptoms are interconnected through a common cause. This comprehensive insight into the relationships between TB symptoms and the disease process improves diagnostic accuracy and aids in refining diagnostic criteria. It enables healthcare providers to more effectively differentiate TB from other conditions with similar symptoms, thereby reducing the risk of misdiagnosis. Moreover, this understanding is crucial in explaining why some individuals with latent TB infections do not progress to active disease, a key factor in managing public health risks.

Overall, comprehending the mechanism of TB has facilitated a significant milestone, crossing a threshold into what we may describe as an objectual, biomedical understanding of TB. This had key implications for identifying new treatment targets, enhancing diagnostic methods, and deepening our knowledge of disease transmission and resistance mechanisms—all of which are vital for improving clinical interventions and formulating effective public health strategies. Crossing this threshold is an important milestone, but it is entirely consistent with recognizing that further exploration and deeper understanding may still be necessary. It does not in any way imply that researchers have reached a final stage in their inquiry that would conclude investigation into TB. Indeed, as researcher recognize, many questions remain (for a recent review, see e.g., Bloom, 2023 ; WHO, 2023 ), driving increasingly detailed and nuanced insights to continuously refine existing approaches to treatment and prevention.

4 Concluding remarks

In light of the recent calls to reexamine the foundational aims of medicine, both in research and clinical practice, this paper emphasizes the importance of understanding as a unifying aim in these domains. As underscored by recent editorials cited in the introduction, there is an imperative to revisit not only the practical aims that medicine seeks, but also its epistemic aims. This is particularly salient in a time when the very essence of what constitutes medical science and clinical medicine is under scrutiny. Accordingly, this paper concentrated on the relevant epistemic aims. By exploring different forms of understanding, the paper uses TB as a focal point to argue that a grasp of mechanistic explanations is crucial for reaching a threshold of understanding at which we may speak of an objectual understanding of TB.

An important limitation of this paper is its focus on a single case: TB. Consequently, there are notable constraints on the breadth of conclusions that can be drawn. However, there are at least some reasons to believe that the findings may have broader applicability. One such reason is that an earlier study on noncommunicable diseases (Varga, 2023 ) have reached similar conclusion. That study revealed that in the case of scurvy, a mechanistic explanation of the condition is necessary for biomedical understanding, but this is not sufficient for understanding in a clinical setting. This earlier study, which examined an emblematic noncommunicable disease, reached a similar conclusion to the current study that focuses on a representative communicable disease. This suggests a potential pattern across various contexts of biomedical research. That said, additional research is required to reinforce this point by investigating whether these conclusions are applicable across a wide spectrum of diseases, including those that are rarer and less prominent. Additionally, it is worth noting that this might differ significantly for conditions where mechanistic explanations have proven challenging to establish. Mental disorders could serve as critical test cases to explore the applicability of our findings in contexts where the underlying mechanisms are less understood.

Interestingly, in an editorial published by the British Medical Journal (Marshall et al., 2018 ) the editors prompt a similar reflection on the purpose of clinical medicine. They challenge the prevailing emphasis on disease-centric care and encourage contemplation of whether a holistic therapeutic relationship with patients might better align with the true aim of medical practice. Though published separately, these editorials collectively highlight a growing movement towards a critical reevaluation of the aims and priorities of both medical science and clinical medicine. The question has sparked considerable interest, with various competing accounts proposing that there is a single, overarching aim (e.g., Broadbent, 2019 ) whereas others suggesting that medicine has multiple aims (e.g., Boorse, 2016 ; Brody & Miller, 1998 ; Schramme, 2017 ).

Munthe ( 2008 ) advocates for an integrated, multidimensional model, highlighting that recent decades have seen the introduction of new objectives focusing on autonomy and equality.

See Bird ( 2019 ) for a helpful discussion of an example from physics.

Other accounts maintain that progress in science occurs when theories come nearer to the truth or when it accumulates solutions to scientific puzzles that are neutral about questions of truth. For a critical review, see Bird ( 2007 ).

Practical understanding (“understanding-how”) typically involves skillful behaviors, relies often on non-propositional knowledge, and is neither explanatory nor susceptible to Gettier-style objections (Bengson, 2017 ). For example, a person may lack the resources to explain the workings of a device but may understand how the device works by way of her skill to adeptly use it.

A mechanism is typically defined as “a structure performing a function in virtue of its component parts, component operations, and their organization” (Bechtel & Abrahamsen, 2005 , p. 423).

For his research, Koch earned the Nobel Prize in 1905.

It makes sense to think that had Koch adhered to a view of causation as merely regularities involving necessary and sufficient conditions that could be discerned through observation, he would not have emphasized (c).

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Highest ocean heat in four centuries places Great Barrier Reef in danger

• Benjamin J. Henley   ORCID: orcid.org/0000-0003-3940-1963 1 , 2 , 3 ,
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• Ove Hoegh-Guldberg   ORCID: orcid.org/0000-0001-7510-6713 6 ,
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• Braddock K. Linsley   ORCID: orcid.org/0000-0003-2085-0662 10  

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• Climate change
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• Palaeoclimate

Mass coral bleaching on the Great Barrier Reef (GBR) in Australia between 2016 and 2024 was driven by high sea surface temperatures (SST) 1 . The likelihood of temperature-induced bleaching is a key determinant for the future threat status of the GBR 2 , but the long-term context of recent temperatures in the region is unclear. Here we show that the January–March Coral Sea heat extremes in 2024, 2017 and 2020 (in order of descending mean SST anomalies) were the warmest in 400 years, exceeding the 95th-percentile uncertainty limit of our reconstructed pre-1900 maximum. The 2016, 2004 and 2022 events were the next warmest, exceeding the 90th-percentile limit. Climate model analysis confirms that human influence on the climate system is responsible for the rapid warming in recent decades. This attribution, together with the recent ocean temperature extremes, post-1900 warming trend and observed mass coral bleaching, shows that the existential threat to the GBR ecosystem from anthropogenic climate change is now realized. Without urgent intervention, the iconic GBR is at risk of experiencing temperatures conducive to near-annual coral bleaching 3 , with negative consequences for biodiversity and ecosystems services. A continuation on the current trajectory would further threaten the ecological function 4 and outstanding universal value 5 of one of Earth’s greatest natural wonders.

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Like many coral reefs globally, the World Heritage-listed GBR in Australia is under threat 4 , 6 . Mass coral bleaching, declining calcification rates 5 , 7 , outbreaks of crown-of-thorns starfish ( Acanthaster spp.) 8 , severe tropical cyclones 9 and overfishing 10 have placed compounding detrimental pressures on the reef ecosystem. Coral bleaching typically occurs when heat stress triggers the breakdown of the symbiosis between corals and their symbiotic dinoflagellates 11 . Although coral bleaching can occur locally as a result of low salinity, cold waters or pollution, regional and global mass bleaching events, in which the majority of corals in one or more regions bleach at once, are strongly associated with increasing SST linked to global warming 2 .

The first modern observations of mass coral bleaching on the GBR occurred in the 1980s, but these events were less widespread and generally less severe 3 than the bleaching events in the twenty-first century 4 . Stress bands in coral skeletal cores have provided potential evidence for pre-1980s bleaching in the GBR and Coral Sea, such as during the 1877–78 El Niño 12 . However, stress bands are evident in relatively few cores before 1980 (ref. 12 ),  suggesting that severe mass bleaching did not occur in the 1800s and most of the 1900s.

As the oceans have warmed, however, mass coral bleaching events have become increasingly lethal to corals 4 . Coral bleaching on the GBR 1 in 1998 coincided with a strong eastern-Pacific El Niño, and in 2002 with a weak El Niño. El Niño events can induce lower cloud cover and increased solar irradiance over the GBR 13 , increasing the risk of thermal stress and mass bleaching events 14 . In 2004, water temperatures were anomalously warm, and although bleaching occurred in the Coral Sea 15 , it was not widespread in the GBR, probably because there was reduced upwelling and an associated reduced influence of nutrients on symbiotic dinoflagellate expulsion 16 .

However, in the nine January–March periods from 2016 to 2024 (inclusive) there were five mass coral bleaching events on the GBR. Each was associated with high SSTs and affected large sections of the reef. GBR mass bleaching occurred in both 2016 and 2017, influenced by the presence of an El Niño event in 2016, and led to the death of at least 50% of shallow-water (depths of 5–10 m) reef-building corals 4 . Major bleaching events occurred again in quick succession in 2020 and 2022, with the accumulated heat stress for large sections of the GBR reaching levels conducive to widespread bleaching but lower levels of coral mortality 1 . The bleaching event in 2022 occurred, unusually, during a La Niña event, which is typically associated with cooler summer SSTs, higher than average rainfall and higher cloud cover on the GBR 1 . At the time of writing, researchers are assessing the impacts of the 2024 mass bleaching event.

The frequency of recent mass coral bleaching and mortality on the GBR is cause for concern. In 2021, the World Heritage Committee of the United Nations Educational, Scientific and Cultural Organization (UNESCO) drafted 17 a decision to inscribe the GBR on the List of World Heritage in Danger, stating that the reef is “facing ascertained danger”, citing recent mass coral bleaching events and insufficient progress by the State Party (Australia) in countering climate change, improving water quality and land management issues. The committee’s adopted decisions 18 have not included inscription of the ‘in danger’ status, but the draft inscription highlights the seriousness of the recent mass coral bleaching events. Authorities in Australia 5 have noted that climate change and coral bleaching have deteriorated the integrity of the outstanding universal value of the GBR, a defining feature of its World Heritage status.

Although rapidly rising SSTs are attributed to human activities with virtual certainty 19 , understanding the multi-century SST history of the GBR is critical to understanding the influence of SST on mass coral bleaching and mortality in recent decades. Putting aside a problematic attempt to do this 20 , which was discredited 21 , 22 , knowledge of the long-term context for GBR SSTs comes primarily from two multi-century reconstructions based on the geochemistry of coral cores collected from the inner shelf 23 and outer shelf 24 (Flinders Reef) in the central GBR. These reconstructions showed that SSTs in the early 2000s were not unusually high relative to levels in the past three centuries, with five-year mean SSTs (and salinities) estimated to be higher in the 1700s than in the 1900s. However, these records were limited by their relatively coarse five-year sampling resolution and their most recent data point being from the early 2000s. After these studies were published, SSTs in the GBR have continued to rise. Updated analysis of coral data from Flinders Reef provides valuable improved temporal resolution 25 , but interpretations of these records remain limited spatially.

Here, we investigate the recent high SST events in the GBR region in the context of the past four centuries. We combine a network of 22 coral Sr/Ca and δ 18 O palaeothermometer series (Supplementary Tables 1 and 2 ) located in and near to the Coral Sea region to infer spatial mean SST anomalies (SSTAs) for January–March, the months when maximum SST and thermal bleaching are most likely to occur in the Coral Sea 16 , 26 , each year from 1618 to 1995 ( Methods and Supplementary Information ). Anthropogenic climate change began and proceeded entirely within the multi-century lives of some of these massive coral colonies, offering a continuous multi-century record covering the industrial era. We use this 1618–1995 reconstruction and the available 1900–2024 instrumental data to contextualize the modern trend and rank four centuries of January–March SSTAs with greater precision than was previously possible. We then assess the degree of human influence on ocean temperatures in the region using climate model simulations run both with and without anthropogenic forcing.

The instrumental period (1900–present)

Mass coral bleaching on the GBR in 2016, 2017, 2020, 2022 and 2024 during January–March coincided with widespread warm SSTAs in the surrounding seas 1 , including the Coral Sea (Fig. 1a–e , using ERSSTv5 data 27 ). The Coral Sea and GBR have experienced a strong warming trend since 1900 (Fig. 1f ). January–March SSTAs averaged over the GBR are strongly correlated ( ρ  = 0.84, P   ≪  0.01) with those in the broader Coral Sea (Fig. 1f ), including when the long-term warming trend is removed from both time series ( ρ  = 0.69, P  < 0.01; Supplementary Fig. 4 ). Based on the strength of this correlation, we associate high January–March area-averaged Coral Sea SSTAs with increased thermal bleaching risk in the GBR.

a – e , SSTAs (using ERSSTv5 data) for January–March in the Australasian region relative to the 1961–90 average for the five recent GBR mass coral bleaching years: 2016, 2017, 2020, 2022 and 2024. The black box shows the Coral Sea region (4° S–26° S, 142° E–174° E). f , Coral Sea and GBR mean SSTAs for 1900–2024 in January–March relative to the 1961–90 average. The black vertical lines indicate the five recent GBR mass coral bleaching years.

Record temperatures were set in 2016 and 2017 in the Coral Sea, and in 2020 they peaked fractionally below the record high of 2017. The January–March of 2022 was another warm event, the fifth warmest on record at the time. Recent data (ERSSTv5) indicate that 2024 set a new record by a margin of more than 0.19 °C above the previous record for the region. The January–March mean SSTs averaged over the five mass bleaching years during the period 2016–2024 are 0.77 °C higher than the 1961–90 January–March averages in both the Coral Sea and the GBR. The multidecadal warming trend, extreme years and association between GBR and Coral Sea SSTs are similar for the HadISST 28 gridded SST dataset, with some notable differences in the 1900–40 period (Supplementary Fig. 3 ). Furthermore, analysis of modern temperature-sensitive Sr/Ca series from GBR corals for 1900–2017 provides coherent independent evidence of statistically significant multi-decadal warming trends in January–March SSTs in the central and southern GBR (Supplementary Information section  4.2 ).

A multi-century context (1618–present)

Reconstructing Coral Sea January–March SSTs from 1618 to 1995 extends the century-long instrumental record back in time by an additional three centuries (Fig. 2a and Methods ). The reconstruction (calibrated to ERSSTv5) shows that multi-decadal SST variability was a persistent feature in the past. At the centennial timescale, there is relative stability before 1900, with the exception that cooler temperatures prevailed in the 1600s. Warming during the industrial era has been evident since the early 1900s (Fig. 2a ). There is a warming trend for January–March of 0.09 °C per decade for 1900–2024 and 0.12 °C per decade for 1960–2024 (Fig. 1f ) using ERSSTv5 data. Calibrating our reconstruction to HadISST1.1 yields similar results, with some differences in the degree of pre-1900 variability at both multi-decadal and centennial timescales (Supplementary Information section  5.2.6 ).

a , Reconstructed and observed mean January–March SSTAs in the Coral Sea for 1618–2024 relative to 1961–90. Dark blue, highest skill (maximum coefficient of efficiency) reconstruction with the full proxy network; light blue, 5th–95th-percentile reconstruction uncertainty; black, observed (ERSSTv5) data. Red crosses indicate the five recent mass bleaching events. Dashed lines indicate the best estimate (highest skill, red) and 95th-percentile (pink) uncertainty bound for the maximum pre-1900 January–March SSTA. b , Central GBR SSTA for the inner shelf 23 in thick orange and outer shelf 25 (Flinders Reef) in thin orange lines; these series are aligned here (see Methods ) with modern observations of mean GBR SSTAs for January–March relative to 1961–90. Observed data are shown at annual (grey line) and five-year (black line with open circles, plotted at the centre of each five-year period and temporally aligned with the five-year coral series 23 ) resolution. Dashed lines indicate best-estimate pre-1900 January–March maxima for refs. 23 (red) and 25 (pink). Orange shading indicates 5th–95th-percentile uncertainty bounds. Red crosses indicate the five recent mass bleaching events. c , Evaluation metrics for the Coral Sea reconstruction (Supplementary Information section  3.1 ); RE, reduction of error; CE, coefficient of efficiency; Rsq-cal, R-squared in the calibration period; Rsq-ver, R-squared in the verification (evaluation) period. d , Coral data locations relative to source data region (orange box) and Coral Sea region (red box). Coral proxy metadata are given in Supplementary Tables 1 and 2 .

Our best-estimate (highest skill; Methods ) annual-resolution Coral Sea reconstruction (Fig. 2a ), using the full coral network calibrated to the ERSSTv5 instrumental data, indicates that the January–March mean SSTAs in 2016, 2017, 2020, 2022 and 2024 were, respectively, 1.50 °C, 1.54 °C, 1.53 °C, 1.46 °C and 1.73 °C above the 1618–1899 (hereafter ‘pre-1900’) reconstructed average. Using the same best-estimate reconstruction, Coral Sea January–March SSTs during these GBR mass bleaching years were five of the six warmest years the region has experienced in the past 400 years (Fig. 2a ).

By comparing the recent warm events to the reconstruction’s uncertainty range ( Methods ), we quantify, using likelihood terminology consistent with recent reports from the Intergovernmental Panel on Climate Change 19 , that the recent heat extremes in 2017, 2020 and 2024 are ‘extremely likely’ (>95th percentile; Fig. 2a ) to be higher than any January–March in the period 1618–1899. Furthermore, the heat extremes in 2016 and 2022 are (at least) ‘very likely’ (>90th percentile) to be above the pre-1900 maximum. We perform a series of tests that verify that our findings are not simply an artefact of the nature of the coral network itself (Supplementary Information section 5.2 ). In a network perturbation test, we generate 22 subsets of the reconstruction by adding proxy records incrementally in order from the highest to the lowest correlation with the target (Supplementary Information section  5.2.5 ). We confirm that 2017, 2020 and 2024 were ‘extremely likely’ (>95th percentile) to have been warmer than any year pre-1900 (using ERSSTv5 data) for all of these proxy subsets. Furthermore, in 20 of the 22 subsets, 2016 was also ‘extremely likely’ (>95th percentile), rather than ‘very likely’, to be warmer (2022 was ‘extremely likely’ in 14 of the 22 subsets). All our additional tests, including a reconstruction with only Sr/Ca coral data (thereby omitting the possibility of any non-temperature signal in δ 18 O coral on the reconstruction), achieve high reconstruction skill and confirm the extraordinary nature of recent extreme temperatures in the multi-century context (Supplementary Information section  5.2 ). Analyses using HadISST1.1 generally show lower correlations with the coral data and reconstructions with slightly warmer regional SSTs before 1900, along with more-muted centennial and multi-decadal variability in the pre-instrumental period. Nevertheless, the HadISST1.1-calibrated reconstructions show that the recent thermal extremes are well above the best estimate (highest skill) of the pre-1900 maximum of reconstructed January–March SSTAs (Supplementary Fig. 42 ). Furthermore, lower SSTAs (in the HadISST1.1 data) relative to the previous three centuries (as in our reconstructions calibrated to HadISST1.1), coupled with the recently observed mass coral bleaching events, could indicate that long-lived corals have a greater sensitivity to warming than is currently recognized.

Reconstructed regional GBR SSTAs based on a five-year-resolution, multi-century coral δ 18 O record from the central inshore GBR 23 (Fig. 2b ) show similarly strong warming since 1900 but more multi-decadal-to-centennial variability than the Coral Sea reconstruction. Recent five-year mean January–March GBR SSTAs narrowly exceed the best estimate of the maximum pre-1900 five-year mean since the early 1600s (Fig. 2b ). The averages for the five-year periods centred on 2018 and 2022 exceed the pre-1900 maximum by 0.11 °C and 0.06 °C, respectively. Results are similar using the five-year-resolution Flinders Reef (central outer shelf) 24 record (Supplementary Fig. 24 ), although its interpretation is limited by the lack of uncertainty estimates available for that record. Our Coral Sea reconstruction incorporates an updated (annual resolution) record from Flinders Reef 25 , which indicates similar centennial trends (thin orange line in Fig. 2b ) and shows that the recent high January–March SSTA events have approached the estimated local pre-1900 maximum SSTA. Although contiguous multi-century cores from within the GBR are limited in their spatial extent, twentieth-century warming is evident in these records.

The extraordinary nature of the recent Coral Sea January–March SSTs in the context of the past 400 years is further illustrated by comparing the ranked temperature anomalies (Fig. 3 ) for the combined reconstructed and instrumental period from 1618–2024, incorporating reconstruction uncertainty ( Methods ). The mass coral bleaching years of 2016, 2017, 2020, 2022 and 2024, and the heat event of 2004, stand out as the warmest events across the whole 407-year record. The warmest three years (2024, 2017 and 2020) exceed the upper uncertainty bound (95th percentile) of the warmest reconstructed January–March in the pre-1900 period (pink (upper) dashed line in Fig. 3 ); 2016, 2004 and 2022 exceed the 90th percentile bound (red (lower) dashed line in Fig. 3 ). The warming trend is clear in the association between the ascending rank of the temperature anomalies and the year (shown as the colour of the filled circles in Fig. 3 ). Despite high interannual variability, 78 of the warmest 100 January–March periods between 1618 and 2024 occurred after 1900, and the 23 warmest all occur after 1900. The warmest 20 January–March periods all occur after 1950, coinciding with accelerated global warming.

Ranked January–March SSTAs for 1618–2024 relative to 1961–90 (coloured circles) from the best-estimate (highest skill, full coral network) reconstruction (1618–1899) and instrumental (ERSSTv5) data (1900–2024). The year is indicated by the colour of the filled circles. The 5th–95th-percentile uncertainty bounds of the pre-1900 reconstructed SSTAs are shown by small grey dots. The year labels indicate the warmest six years on record, five of which were mass coral bleaching years on the GBR. The pink (upper) dashed line indicates the 95th-percentile uncertainty bound of the maximum pre-1900 reconstructed SSTA; the red (lower) dashed line indicates the 90th-percentile limit.

Assessing anthropogenic influence

Using climate model simulations from the most recent (sixth) phase of the Coupled Model Intercomparison Project 29 (CMIP6), we assess the human influence on January–March SSTAs in the Coral Sea. The model simulations are from two experiments in the Detection and Attribution Model Intercomparison Project (DAMIP) 30 . The first set of simulations represents historical climate conditions, including both the natural and human influences on the climate system over the 1850–2014 period (‘historical’; red in Fig. 4 ). The second experiment is a counterfactual climate that spans the same period and uses the same models but includes only natural influences on the climate, omitting all human influences (‘historical-natural’; blue in Fig. 4 ). The historical experiment includes anthropogenic emissions of greenhouse gases and aerosols, stratospheric ozone changes and anthropogenic land-use changes; the historical-natural experiment does not. Variations in natural climate forcings, such as from volcanic eruptions and solar variability, are incorporated in both experiments. We include models that have a transient climate response (the global mean surface-temperature anomaly at the time of a doubling of atmospheric CO 2 concentration) in the range 1.4–2.2 °C, which is deemed ‘likely’ by the science community 31 ( Methods and Supplementary Information ).

Climate-model simulations of Coral Sea January–March SSTAs relative to the 1850–1900 average for the period 1850–2014, for models within the ‘likely’ range for their transient climate response 31 . The blue line (median) and light blue shading (5th–95th-percentile limits) are from the ‘historical-natural’ climate model simulations (no anthropogenic climate forcing); the red line and light red shading are from the ‘historical’ simulations (anthropogenic influences on the climate included) using the same set of climate models. The climate-model-derived time of emergence of anthropogenic climate change, shown by the grey and black vertical lines (1976 and 1997), is when the ratio of the climate change signal to the standard deviation of noise/variability 32 across model ensemble members first rises above 1 and 2, respectively. All models are represented equally in the model ensemble.

It is only with the incorporation of anthropogenic influences on the climate that the model simulations capture the modern-era warming of the Coral Sea January–March SSTA (Fig. 4 ). The median of the historical simulations has statistically significant warming trends of 0.05 °C, 0.10 °C and 0.15 °C per decade for the periods from 1900, 1950 and 1970 to 2014, respectively; the equivalent historical-natural trends are smaller in magnitude than ±0.01 °C per decade. To further explore the centennial-scale trends, we use a bootstrap ensemble ( Methods ) of the two sets of 165-year simulations from 1850–2014. We found that 100% of the historical bootstrap ensemble has statistically significant positive trends ( Methods ) for 1900–2014, but this value is 0% for the historical-natural ensemble. The observed (ERSSTv5) mean SSTA for 2016–2024 of 0.60 °C relative to 1961–90 is warmer than any nine-year sequence in the 7,095 simulated years in the historical-natural experiments from models with transient climate responses in the ‘likely’ range 31 .

We also use the simulations to estimate the time of emergence of the anthropogenic influence on January–March Coral Sea SSTAs above the natural background variability. The anthropogenic warming signal 32 increases from near zero in 1900 to around 0.5 standard deviations of the variability (‘noise’) in 1960. The climate change signal-to-noise ratio then increases rapidly from 1960 to 2014, exceeding 1.0 in 1976, 2.0 in 1997 and around 2.8 by 2014, the end of these simulations (Fig. 4 , Methods and Supplementary Fig. 50 ). Anthropogenic impacts on the climate are virtually certain to be the primary driver of this long-term warming in the Coral Sea.

Previously, our knowledge of the SST history of the GBR and the Coral Sea region has been highly dependent on instrumental observations, with the exception of the five-year-resolution multi-century coral Sr/Ca and U/Ca SST reconstructions from the two point locations in the central GBR 23 , 24 , an update at one of these locations 25 , seasonal resolution ‘floating’ (in time) chronologies from the GBR in the Holocene 33 , 34 and point SST estimates further back in time 35 . Thus, the context of recent warming trends in the Coral Sea and GBR and their relation to natural variability on decadal to centennial timescales is largely unknown without reconstructions such as the one we developed here.

Our coral proxy network is located mostly beyond the GBR, in the Coral Sea, and some series are located outside the Coral Sea region (Fig. 2d ). The selection of the Coral Sea as a study region allowed for a larger sample of contributing coral proxy data than exists for the GBR. However, coral bleaching on the GBR can be influenced by factors other than large-scale SST, including local oceanic and atmospheric dynamics that can modulate the occurrence and severity of thermal bleaching and mortality events 13 . Nonetheless, warming of seasonal SSTs over the larger Coral Sea region is likely to prime the background state and increase the likelihood of smaller spatio-temporal-scale heat anomalies. Furthermore, where we use only the five-year resolution series directly from the GBR to reconstruct GBR SSTAs, we draw similar conclusions about the long-term trajectory of SSTAs as for our full coral network (Fig. 2b and Supplementary Fig. 24 ). Furthermore, short modern coral series from within the GBR, analysed in this study, document a multi-decadal warming signal that is coherent with instrumental data (Supplementary Figs. 29 and 30 ). Nonetheless, additional high-resolution, multi-century, temperature-sensitive coral geochemical series from within the GBR would help unravel the local and remote ocean–atmosphere contributions to past bleaching events and reduce uncertainties.

The focus on the larger Coral Sea study region also takes advantage of the global modelling efforts of CMIP6. The large number of ensemble members available for CMIP6 means that greater climate model diversity, and therefore greater certainty in our attribution analysis, is possible compared with most single model analyses. There is also a methodological benefit in having high replication of the same experiments run with multiple climate models. However, coarse-resolution global-scale models do not accurately simulate smaller-scale processes, such as inshore currents and mesoscale eddies in the Coral Sea or the Gulf of Carpentaria, which probably affect local surface temperatures and variations in nutrient upwelling in the GBR 36 , 37 . Upwelling on the GBR is linked to the strength of the East Australian Current 16 , the southward branch of the South Pacific subtropical gyre. The CMIP-scale models we use do capture these gyre dynamics. The models show that the East Australian Current is expected to increase in strength as the climate continues to warm through this century 38 , and this may lead to more nutrient inputs that can exacerbate coral sensitivity to rising heat stress 39 , 40 . As well as focusing our model analysis on the larger Coral Sea region, we use a three-month time step. In doing so, we minimize the impact of model spatio-temporal resolution on our inferences about the role of anthropogenic greenhouse-gas emissions on the SST conditions that give rise to GBR mass bleaching.

Remaining uncertainties

We present analyses and interpretations that are as robust as possible given currently available data and methods. However, several sources of remaining uncertainty mean that future reconstructions of past Coral Sea and GBR SSTs could differ from those presented here. Although bias corrections are applied to observational SST datasets such as ERSST and HadISST, these datasets probably retain biases, especially for the period during and before 1945 (ref. 41 ), and these may not be fully accounted for in the uncertainty estimates 42 . Because our reconstructions are calibrated directly to these datasets, future observational-bias corrections are likely to improve proxy-based reconstructions.

Reconstructions of SST that use coral δ 18 O records may be susceptible to the influence of changes in the coral δ 18 O–SST relationship on time periods longer than the instrumental training period, along with non-SST changes in the δ 18 O of seawater, which can covary with salinity. As such, new coral records of temperature-sensitive trace-element ratios such as Sr/Ca, Li/Mg or U/Ca may prove influential in future efforts to distinguish between changes in past temperature and hydroclimate. Owing to the limited availability of multi-century coral data from within the GBR itself, the reconstructed low-frequency variability of GBR SSTs in recent centuries is likely to change as more temperature proxy data become available. It is also likely that new sub-annual resolution records would aid in removing potential signal damping or bias from our use of some annual-resolution records to reconstruct seasonal SSTAs.

Ecological consequences

With global warming of 0.8–1.1 °C above pre-industrial levels 19 there has been a marked increase in mass coral bleaching globally 43 . Even limiting global warming to the Paris Agreement’s ambitious 1.5 °C level would be likely to lead to the loss of 70–90% of corals that are on reefs today 44 . If all current international mitigation commitments are implemented, global mean surface temperature is still estimated to increase in the coming decades, with estimates varying between 1.9 °C (ref. 45 ) and 3.2 °C (ref. 46 ) above pre-industrial levels by the end of this century. Global warming above 2 °C would have disastrous consequences for coral ecosystems 19 , 44 and the hundreds of millions of people who currently depend on them.

Coral reefs of the future, if they can persist, are likely to have a different community structure to those in the recent past, probably one with much less diversity in coral species 4 . This is because mass bleaching events have a differential impact on different coral species. For example, fast-growing branching and tabulate corals are affected more than slower-growing massive species because they have different thermal tolerance 4 . The simplification of reef structures will have adverse impacts on the many thousands of species that rely on the complex three-dimensional structure of reefs 4 . Therefore, even with an ambitious long-term international mitigation goal, the ecological function 4 of the GBR is likely to deteriorate further 5 before it stabilizes.

Coral adaptation and acclimatization may be the only realistic prospect for the conservation of some parts of the GBR this century. However, although adaptation opportunities may be plausible to some extent 47 , they are no panacea because evolutionary changes to fundamental variables such as temperature take decades, if not centuries, to occur, especially in long-lived species such as reef-building corals 48 . There is currently no clear evidence of the real-time evolution of thermally tolerant corals 48 . Most rapid changes depend on a history of exposure to key genetic types and extremes, and there are limitations to genetic adaptation that prevent species-level adaptation to environments outside of their ecological and evolutionary history 19 . Model projections also indicate that rates of coral adaptation are too slow to keep pace with global warming 49 . In a rapidly warming world, the temperature conditions that give rise to mass coral bleaching events are likely to soon become commonplace. So, although we may see some resilience of coral to future marine heat events through acclimatization, thermal refugia are likely to be overwhelmed 50 . Global warming of more than 1.5 °C above pre-industrial levels will probably be catastrophic for coral reefs 44 .

Our new multi-century reconstruction illustrates the exceptional nature of ocean surface warming in the Coral Sea today and the resulting existential risk for the reef-building corals that are the backbone of the GBR. The reconstruction shows that SSTs were relatively cool and stable for hundreds of years, and that recent January–March ocean surface heat in the Coral Sea is unprecedented in at least the past 400 years. The coral colonies and reefs that have lived through the past several centuries, and that yielded the valuable Sr/Ca and δ 18 O data on which our reconstruction is based, are themselves under serious threat. Our analysis of climate-model simulations confirms that human influence is the driver of recent January–March Coral Sea surface warming. Together, the evidence presented in our study indicates that the GBR is in danger. Given this, it is conceivable that UNESCO may in the future reconsider its determination that the iconic GBR is not in danger. In the absence of rapid, coordinated and ambitious global action to combat climate change, we will likely be witness to the demise of one of Earth’s great natural wonders.

Instrumental observations

The Coral Sea and GBR area-averaged monthly SSTAs relative to 1961–90 for January–March are obtained from version 5 of the Extended Reconstructed Sea Surface Temperature dataset (ERSSTv5) 27 . We compare our results using ERSSTv5 with those generated using the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST1.1) 28 . We use only post-1900 instrumental SST observations here. Although gridded datasets have some coverage before 1900, ship-derived temperature data in the region for that period are too sparse to be reliable for calibrating our reconstruction (Supplementary Information section  1.2 ). The regional mean for the GBR is computed using the seven grid-cell locations used by the Australian Bureau of Meteorology (Supplementary Information section  1.1 ). We define the Coral Sea region as the ocean areas inside 4° S–26° S, 142° E–174° E.

Coral-derived temperature proxy data

We use a network of 22 published and publicly available sub-annual and annual resolution temperature-sensitive coral geochemical series (proxies; Fig. 2d , Supplementary Tables 1 and 2 , and Supplementary Fig. 5a–v ) from the western tropical Pacific in our source data region (4° N–27° S, 134° E–184° E) that cover at least the period from 1900 to 1995. Of these 22 series, 16 are δ 18 O, which are in per mil (‰) notation relative to Vienna PeeDee Belemnite (VPDB) 51 ; the remaining six are Sr/Ca series. The coral data are used as predictors in the reconstruction of January–March mean SSTAs in the Coral Sea region. We apply the inverse Rosenblatt transformation 52 , 53 to the coral data to ensure that our reconstruction predictors are normally distributed. Sub-annually resolved series are converted to the annual time step by averaging across the November–April window. This maximizes the detection of the summer peak values, allowing for some inaccuracy in sub-annual dating and the timing of coral skeleton deposition 54 , 55 . A small fraction (less than 0.8%) of missing data is infilled using the regularized expectation maximization (RegEM) algorithm 56 (Supplementary Information section  2.3 ), after which the proxy series are standardized such that each has a mean of zero and a standard deviation of one over their common 1900–1995 period.

Reconstruction method

To produce our Coral Sea reconstruction, we use nested principal component regression 57 (PCR), in which the principal components of the network of 22 coral proxies are used as regressors against the target-region January–March SSTA relative to the 1961–90 average. We perform the reconstructions separately for each nest of proxies, where a nest is a set of proxies that cover the same time period. The longest nest dates back to 1618, when at least two series are available. The nests allow for the use of all coral proxies over the full time period of their coverage. The 96-year portion of the instrumental period (1900–1995) that overlaps with the reconstruction period is used for calibration and evaluation (or equivalently, verification) against observations. We reconstruct regional SSTAs from the principal components of the coral network of δ 18 O and Sr/Ca data, rather than their local SST calibrations, to minimize the number of computational steps and to aid in representing the full reconstruction uncertainty.

Principal component analysis (PCA) is used to reduce the dimensionality of the proxy matrix, as follows. Let P ( t , r ) denote the palaeoclimate-data matrix during the time period t  = 1,..., n at an annual time step for proxy series r  = 1,..., p . PCA is undertaken on this matrix during the calibration period, P cal . We obtain the principal component coefficients matrix P coeff ( r , e ) for principal components e  = 1,..., n PC and principal component scores P score ( t , e ), which are representations of the input matrix P cal in the principal component space. P score is truncated to include n PC,use principal components to form \({P}_{{\rm{score}}}^{{\prime} }\) such that the variance of the proxy network explained by the n PC,use principal components is greater than \({\sigma }_{{\rm{expl}}}^{2}\) (which we set to 95%). Reconstruction tests in which \({\sigma }_{{\rm{expl}}}^{2}\) is varied from 70% to 95% show that our results are not strongly sensitive to this choice, and tests based on lag-one autoregressive noise for \({\sigma }_{{\rm{expl}}}^{2}\) from 50% to 99% further support this choice (Supplementary Information section  3.2 ). These principal components are used as predictors against which the Coral Sea January–March instrumental SSTAs are regressed. We regress the standardized SSTA target data during the calibration period, I cal , against the retained principal components of the predictor data, \({P}_{{\rm{score}}}^{{\prime} }\) :

Thus, we obtain n PC,use estimates of the regression coefficients γ e with gaussian error term ε t  ~  N (0, \({\sigma }_{N}^{2}\) ). The principal components are extended back into the pre-instrumental period by multiplying the entire proxy matrix P ( t , p ) with the truncated principal component coefficient matrix \({P}_{{\rm{coeff}}}^{{\prime} }\) ( t , e ) to obtain \({Q}_{{\rm{coeff}}}^{{\prime} }\) :

The reconstruction proceeds with the fitted regression coefficients γ e and extended coefficient matrix \({Q}_{{\rm{coeff}}}^{{\prime} }\) to obtain a reconstruction time series R m ( t ) for a given nest of proxy series

The standardized reconstruction R m ( t ) is then calibrated to the instrumental data such that the standard deviation and mean of the reconstruction and target during the calibration interval are equal. As well as obtaining reconstructions for each nest of available proxies, we compute stitched reconstructions S c ( t ) for each calibration period c , which include at each time step the reconstructed data for the proxy nest with maximum coefficient of efficiency 58 , 59 (Supplementary Information section  3.1 ). This procedure is performed for contiguous calibration intervals between 60 and 80 years duration between 1900 and 1995, with interval width and location increments of two years, reserving the remaining data in the overlapping period for independent evaluation, and for all proxy nests. The reconstruction error is modelled with a lag-one autoregressive process fitted to the residuals. We evaluate the capacity of our reconstruction method to achieve spurious skill from overfitting by performing a test in which we replace the coral data with synthetic noise (Supplementary Information section  3.2i ). We find that reconstructions based on synthetic noise achieve extremely low or zero skill and as more noise principal components are included in the regression, the evaluation metrics indicate declining skill. Our reconstruction and evaluation methods therefore guard against the potential for spurious skill.

Pseudo-proxy reconstructions

Our reconstruction method is further evaluated by using a pseudo-proxy modelling approach based on the Community Earth System Model (CESM) Last Millennium Experiment (LME) 60 , for which there are 13 full-forcing ensemble members covering the period 850–2005. We use the pseudo-proxy reconstructions to evaluate our reconstruction method and coral network in a fully coupled climate-model environment. We form pseudo-proxies by extracting from each LME ensemble member the SST and sea surface salinity (SSS) from the 1.5° × 1.5° grid cell located nearest to our coral data. We then apply proxy system models in the form of linear regression models, basing δ 18 O on both SST and SSS, and Sr/Ca on SST only (Supplementary Information section  3.3 ). We set the spatial and temporal availability of the pseudo-coral network to match that of the coral network. We then apply our PCR reconstruction and evaluation procedure to the pseudo-proxy network, taking advantage of the availability of the modelled Coral Sea SSTA data across the multi-century period of 1618–2005, which allows for the evaluation of the pseudo-proxy reconstruction over this entire time period. We first test our method using a ‘perfect proxy’ approach (with no proxy measurement error) before superimposing synthetic noise on the pseudo-proxy time series, evaluating our methodology at two separate levels of measurement error, quantified by signal-to-noise ratios of 1.0 and 4.0. The evaluation metrics for these tests indicate that our coral network and reconstruction method obtain skilful reconstructions of Coral Sea SSTAs in the climate-model environment (Supplementary Figs. 17b , 18 , 20b , 21 , 22b and 23 ).

Comparison with independent coral datasets

We use two multi-century five-year-resolution coral series from the central GBR 23 , 24 (Fig. 2b and Supplementary Fig. 24 ) and a network of sub-annual and annual resolution modern coral series (dated from 1900 onwards but not covering the full 1900–1995 period) from 44 sites in the GBR (Supplementary Information section  4.2 ) for independent evaluation of coral-derived evidence for warming in the region. We estimate five-year GBR SSTAs (Fig. 2b ) by aligning the post-1900 mean and variance of the proxy and instrumental (ERSSTv5) data.

Reconstruction sensitivity to non-SST influences

Of the 22 available coral series, 16 are records of δ 18 O, a widely used measure of the ratio of the stable isotopes 18 O and 16 O. In the tropical Pacific Ocean, δ 18 O is significantly correlated with SST 61 , 62 , 63 , 64 . Coral δ 18 O is also sensitive to the δ 18 O of seawater 65 , which can reflect advection of different water masses and/or changes in freshwater input, such as from riverine sources or precipitation, which in turn co-vary with SSS. Thus, it is generally considered that the main non-SST contributions to coral δ 18 O are processes that co-vary with SSS 62 , 66 . Our methodology minimizes the influence of non-temperature impacts on the reconstruction by exploiting the contrast in spatial heterogeneity between SST and SSS in January–March (Supplementary Information section  5.1 ). SSS is spatially inhomogeneous in the tropical Pacific 66 , 67 , leading to low coherence in SSS signals across our coral network. By contrast, the strong and coherent SST signal across our coral network locations and the Coral Sea region leads to principal components that are strongly representative of SST variations. This produces a skilful reconstruction of SST, as determined by evaluation against independent observations, and low correlations with SSS across the Coral Sea region (Supplementary Fig. 31 ).

Although the likelihood of non-SST influences on our SST reconstruction is low, we nonetheless test the sensitivity of our reconstruction and its associated interpretations to the possibility of these influences on the coral data. The tests compute the correlations between our best-estimate SSTA reconstruction (highest coefficient of efficiency) and observations of SSS, along with a series of additional reconstructions based on subsets of our coral network. The correlations between our highest coefficient of efficiency January–March Coral Sea SSTA reconstruction and January–March SSS are mapped for the Coral Sea and its neighbouring domain using three instrumental SSS datasets (Supplementary Fig. 31 ). Correlations are not statistically significant over most of the domain. Noting differing spatial correlation patterns between the instrumental SSS datasets 68 , which also cover different time periods (Supplementary Information section  5.1 ), we undertake six sensitivity tests using subsets of the coral network (Supplementary Information section  5.2 ). We use the following combinations of coral series: (1) the full network of 22 δ 18 O and Sr/Ca series (Figs. 2a and 3 ); (2) a subset of the six available Sr/Ca series (Supplementary Figs. 32 – 33 ), to test how the reconstruction is influenced by the inclusion of coral δ 18 O records; (3) a fixed nest subset of the five longest coral series, extending back to at least 1700 (Supplementary Figs. 34 – 35 ), to test for the potential influence of combining series of differing lengths (from our splicing of portions of the best reconstructions from each nest); (4) a subset of the ten coral series that are most strongly correlated with the target (Supplementary Figs. 36 and 37 ), to test how our reconstruction is influenced by the inclusion of coral series that are less strongly correlated with our target; (5) a subset of coral series that excludes the six records that are reported to potentially include biological mediation or non-climatic effects, or have low correlation with the target (Supplementary Figs. 38 and 39 ), to test their influence on the reconstruction; and (6) a network perturbation test comprising 22 separate subsets of proxies, in which proxy records are added incrementally in order of highest to lowest correlation with the target, starting with a single coral series and increasing the number of included proxies to all 22 series in our network (Supplementary Information section  5.2.5 ), to systematically quantify the influence of gradually including more coral datasets on our reconstruction and its interpretations.

The evaluation metrics (Fig. 2c and Supplementary Figs. 32b , 34b , 36b and 38b ) indicate a skilful reconstruction back to 1618 for the reconstructions based on the Full, Sr/Ca only, Long, Best-10 and OmitBioMed networks. These reconstructions explain 82.7%, 80.6%, 77.6%, 79.8% and 80.4% (R-squared values) of the variance in January–March SSTAs, respectively, in the independent evaluation periods (using ERSSTv5b). All coral subsets in the network perturbation test produce skilful reconstructions (Supplementary Fig. 40 ). The highest-skill reconstructions for all subsets in the network perturbation test align with our key interpretations (Supplementary Figs. 41 and 42 ). Together, our sensitivity tests show that the coral network, observational data and reconstruction methodology are a sound basis for reconstructing Coral Sea January–March SSTAs in past centuries and contextualizing recent high-SST events ( Supplementary Information ).

Climate-model attribution ensembles and experiments

The multi-model attribution analysis used here is based on simulations from CMIP6. We analyse simulations from the historical experiment (including natural and anthropogenic influences for 1850–2014) and the historical-natural experiment (natural-only forcings for 1850–2014). We select climate models for which monthly surface temperature is available in at least three historical and historical-natural simulations (Supplementary Table 5 ). All model simulations are interpolated to a common regular 1.5° × 1.5° latitude–longitude grid. January–March SSTAs relative to 1961–90 are calculated for each simulation. The full historical all-forcings ensemble is composed of 14 models with 268 simulations for 1850–2014. The natural-only ensemble is composed of the same 14 models with 95 individual simulations. A subset of climate models in the CMIP6 ensemble are considered by the science community to be ‘too hot’, simulating warming in response to increased atmospheric carbon dioxide concentrations that is larger than that supported by independent evidence 31 . We omit these models from our analysis by including only models with a transient climate response in the ‘likely’ range 31 of 1.4–2.2 °C. Our results are not strongly sensitive to this selection (Supplementary Information section  6.3 ). The ten remaining models yield a total of 25,410 years from 154 historical ensemble members and 7,095 years from 43 historical-natural ensemble members. We weight the models equally in our analysis using bootstrap sampling. We report linear trends based on simple linear regression models fitted with ordinary least squares. The statistical significance of linear trends is assessed using the Spearman’s rank correlation test 69 .

Time of emergence of the anthropogenic impact

We assess the anthropogenic influence on SSTAs in the Coral Sea region by starting with the assumption that any anthropogenic influence on SSTAs in the Coral Sea is indistinguishable from natural variability at the commencement of the model experiments. We measure the impact of anthropogenic influence on the climate in the region using a signal-to-noise approach 32 , 70 . We calculate the anthropogenic ‘signal’ as the mean of the difference between the smoothed (using a 41-year Lowess filter) modelled historical Coral Sea SSTA and the mean smoothed modelled historical-natural SSTA. Our ‘noise’ is the standard deviation of the difference between the modelled historical SSTA and its smoothed time series (Supplementary Information section  6 ).

Methods additionally rely on Supplementary Information and refs. 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 .

Data availability

The ERSSTv5 instrumental SST data are available from the US National Oceanic and Atmospheric Administration at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html . The HadISST1.1 data are available from the UK Met Office at https://www.metoffice.gov.uk/hadobs/hadisst/ . The original coral palaeoclimate data are available at the links provided in Supplementary Table 2 . Land areas for maps are obtained from the Mapping Toolbox v.23.2 in Matlab v.2023b and the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHS) Database at https://www.soest.hawaii.edu/pwessel/gshhg/ through the m_map toolbox by R. Pawlowicz, available at https://www.eoas.ubc.ca/%7Erich/map.html . Prepared data from the coral geochemical series, reconstructions and climate models that support the findings of this study are available at: https://doi.org/10.24433/CO.4883292.v1 .

Code availability

The code that supports the findings of this study is available and can be run at : https://doi.org/10.24433/CO.4883292.v1 .

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Acknowledgements

We acknowledge the originators of the coral data cited in Supplementary Tables 1 and 2 ; S. E. Perkins-Kirkpatrick and the deceased G. J. van Oldenborgh 105 for contributions to an earlier version of this manuscript; E. P. Dassié and J. Zinke for discussions and data; R. Neukom for advice on an earlier version of the reconstruction code; and B. Trewin and K. Braganza for advice about the Bureau of Meteorology GBR SST time series. B.J.H. and H.V.M. acknowledge support from an Australian Research Council (ARC) SRIEAS grant, Securing Antarctica’s Environmental Future (SR200100005), and ARC Discovery Project DP200100206. A.D.K. acknowledges support from an ARC DECRA (DE180100638) and the Australian government’s National Environmental Science Program. B.J.H. and A.D.K. acknowledge an affiliation with the ARC Centre of Excellence for Climate Extremes (CE170100023). H.V.M. acknowledges support from an ARC Future Fellowship (FT140100286). A.K.A. acknowledges support from an Australian government research training program scholarship and an AINSE postgraduate research award. Funding was provided to B.K.L. by the Vetlesen Foundation through a gift to the Lamont-Doherty Earth Observatory. Grants to B.K.L. enabled the generation of coral oxygen isotope and Sr/Ca data from Fiji that were used in our reconstruction (US National Science Foundation OCE-0318296 and ATM-9901649 and US National Oceanic and Atmospheric Administration NA96GP0406). We acknowledge the support of the NCI facility in Australia and the World Climate Research Programme’s working group on coupled modelling, which is responsible for CMIP. We thank the climate-modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure in partnership with the Global Organisation for Earth System Science Portals.

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Benjamin J. Henley, Helen V. McGregor & Ariella K. Arzey

Securing Antarctica’s Environmental Future, University of Wollongong, Wollongong, New South Wales, Australia

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Benjamin J. Henley

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Andrew D. King & David J. Karoly

ARC Centre of Excellence for Climate Extremes, University of Melbourne, Parkville, Victoria, Australia

Andrew D. King

School of the Environment, The University of Queensland, Brisbane, Queensland, Australia

Ove Hoegh-Guldberg

Australian Institute of Marine Science, Townsville, Queensland, Australia

Janice M. Lough

ARC Centre of Excellence for Coral Reef Studies and School of Earth Sciences, University of Western Australia, Crawley, Western Australia, Australia

Thomas M. DeCarlo

Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA

Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

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Contributions

B.J.H., H.V.M. and A.D.K. conceived the study and developed the methodology. B.J.H. did most of the analysis. A.K.A. contributed analysis of modern coral data (Supplementary Information section  4.2 ). T.M.D. contributed analysis of instrumental data coverage (Supplementary Information section  1.2 ). B.K.L. contributed sub-annual coral data. B.J.H. and H.V.M. led the preparation of the manuscript, with contributions from A.D.K., O.H.-G., A.K.A., D.J.K., J.M.L., T.M.D. and B.K.L. Generative artificial intelligence was not used in any aspect of this study or manuscript.

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Henley, B.J., McGregor, H.V., King, A.D. et al. Highest ocean heat in four centuries places Great Barrier Reef in danger. Nature 632 , 320–326 (2024). https://doi.org/10.1038/s41586-024-07672-x

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