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  • What is Secondary Research? | Definition, Types, & Examples

What is Secondary Research? | Definition, Types, & Examples

Published on January 20, 2023 by Tegan George . Revised on January 12, 2024.

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research .

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

Table of contents

When to use secondary research, types of secondary research, examples of secondary research, advantages and disadvantages of secondary research, other interesting articles, frequently asked questions.

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

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existing research

Secondary research can take many forms, but the most common types are:

Statistical analysis

Literature reviews, case studies, content analysis.

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis .

Credible sources for existing data include:

  • The government
  • Government agencies
  • Non-governmental organizations
  • Educational institutions
  • Businesses or consultancies
  • Libraries or archives
  • Newspapers, academic journals, or magazines

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews , observational studies , or other sources of primary data make for great case studies.

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

Primary Research and Secondary Research

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic .

Secondary research is a very common research approach, but has distinct advantages and disadvantages.

Advantages of secondary research

Advantages include:

  • Secondary data is very easy to source and readily available .
  • It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research .
  • As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.
  • Using data from others allows you to show reproducibility and replicability , bolstering prior research and situating your own work within your field.

Disadvantages of secondary research

Disadvantages include:

  • Ease of access does not signify credibility . It’s important to be aware that secondary research is not always reliable , and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test .
  • Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data . Data cleansing may be necessary to test the quality of the research.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What is Secondary Research? | Definition, Types, & Examples. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/methodology/secondary-research/
Largan, C., & Morris, T. M. (2019). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.
Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data. European Journal of Human Genetics , 28 (6), 697–705. https://doi.org/10.1038/s41431-020-0596-x

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13.1 The Research Process: Where to Look for Existing Sources

Learning outcomes.

By the end of this section, you will be able to:

  • Locate and evaluate primary and secondary research materials, including journal articles and essays, books, scholarly and professionally established and maintained databases or archives, and informal electronic networks and Internet sources.
  • Apply methods and technologies commonly used for research in various fields.

Once you have chosen your argumentative research topic, developed a workable research question, and devised a plan for your research as described in Argumentative Research: Enhancing the Art of Rhetoric with Evidence , you are ready to begin the task usually associated with the term research —namely, the collection of sources. One key point to remember at this stage is intentionality; that is, begin with a research plan rather than a collection of everything you find related to your topic. Without a plan, you easily may end up overwhelmed by too many unusable sources. A carefully considered research plan will save you time and energy and help make your search for sources more productive. Access to information is generally not a problem; the problem is knowing where to find the information you need and how to distinguish among types and qualities of sources. In short, finding sources is all about sorting, selecting, and evaluating.

Your specific methods for collecting sources will depend on the details of your research project. However, a good strategy to begin with is to think in terms of needs: What do you need, as the researcher and writer? What do your readers need? This kind of needs assessment is similar to the considerations you make about the rhetorical situation when writing an analysis or argument.

Review Table 13.1 as you conduct a source “needs assessment.”

Generating Key Words

Before you begin locating sources, consider the research terms you will use to find these sources. Most research is categorized according to key terms that are important for understanding the topic and/or methodologies. When beginning the research process, you may find that the ideas or words associated with your topic are not yielding results when you search library or Internet databases (organized collections of information).

If you are running into challenges locating information related to your topic, you may not have chosen the specific key terms needed. Because libraries and online databases generate search results based on algorithms that target keywords, the best way to find the appropriate terminology associated with your topic is to practice generating key terms. You may need a range of keywords, some for library searches and others for online searches. When considering the difference between keyword searches in academic libraries versus online sources, note that most academic libraries use Library of Congress Subject Headings (LCSH) for subject searches of their online catalogs. Many databases use subject searches based on algorithms that may be unique to that database. Don’t become discouraged if you find that the terms for searching in your academic library may be somewhat different from the terms for searching online. As you begin to find sources related to your subject, take notes on the variety of terms that describe your research area. These notes will come in handy as both keyword and subject searches throughout your research process.

The following steps and examples will help you get started:

  • Begin by limiting your topic to one or two sentences or questions. ( What effects do a region’s water and temperatures have on fall foliage? )
  • Highlight specific words that are key to understanding or finding answers to your question. ( What effects do the amount of water a region gets and temperatures for that region have on colors of fall foliage? )
  • Consider words assumed but not mentioned in your question. For instance, the example question implies a search around trees and rainfall; however, trees and rainfall are not mentioned. Add these words to the words highlighted in your question.
  • Consider synonyms for the words you highlighted. A search for synonyms for fall yields harvest , autumn , and autumnal equinox . A search for synonyms for leaves yields foliage , fronds , and stalks . Be sure you understand the meaning of each synonym so that you can choose those that best capture the concepts you seek to research.
  • Try different combinations of the key terms and synonyms to help you find as many sources as you can.

You can find more information about key terms and searches by consulting Writing Process: Informing and Analyzing .

Locating Sources

Once you have identified sources to fit both your and your readers’ needs, you can begin to locate these sources. Throughout the research process, look for sources that will provide enough information for you to form your own opinions or answer your research question(s). Use source materials as support for your own words and ideas. The following are possible locations for source materials:

While much of your writing and research work happens online, libraries remain indispensable to research. Your university’s physical and/or online library is a valuable resource, providing access to databases, books and periodicals (both print and electronic), and other media that might not otherwise be accessible. In many cases, experienced people are available with discipline-specific research advice. To take full advantage of library resources, keep the following suggestions in mind:

  • Visit early and often. As soon as you receive a research assignment, visit the library (physically, virtually, or both) to discover resources available for your project. Even if your initial research indicates a wealth of material, you may be unable to find everything during your first search. You may find that a book has been checked out or that your library doesn’t subscribe to a certain periodical. Furthermore, going to the library can be extremely helpful because you likely will see a range of additional sources simply by looking around the areas in which you locate initial sources.
  • Check general sources first. Look at dictionaries, encyclopedias, atlases, and yearbooks for background information about your topic. An hour spent with these sources will give you a quick overview of the scope of your topic and lead you to more specific information.
  • Talk to librarians. At first, you might show a librarian your assignment and explain your topic and research plans. Later, you might ask for help in finding a particular source or finding out whether the library has additional sources you have not checked yet. Librarians are professional information experts; don’t hesitate to use their expertise.

General Reference Works

General reference works provide background information and basic facts about a topic. To locate these sources, you will need a variety of tools, including the online catalog and databases, as well as periodical indexes. To use these resources effectively, follow this four-step process:

  • Consult general reference works to gain background information and basic facts.
  • Consult specialized reference works to find relevant articles on all topics.
  • Consult the library’s online catalog to identify library books on your topic.
  • Consult other sources as needed.

The summaries, overviews, and definitions in general reference works can help you decide whether to pursue a topic further and where to turn next for information. Because the information in these sources is necessarily general, they will not be sufficient alone as the basis for most research projects and are not strong sources to cite in research papers.

Following are some of the most useful general reference works to provide context and background information for research projects:

  • Almanacs and yearbooks provide up-to-date information, including statistics on politics, agriculture, economics, and population. See especially the Facts on File World News Digest (1940–present), an index to current events reprinted in newspapers worldwide, and the World Almanac and Book of Facts (1868–present), which reviews important events of the past year as well as data on a wide variety of topics, including sports, government, science, business, and education. In addition to current publications, almanacs from recent years or from many years ago provide information about the times in which they were written.
  • Atlases such as the Hammond World Atlas , the National Geographic Atlas of the World , and the Times Atlas of the World can help you identify places anywhere in the world and provide information on population, climate, and industry.
  • Biographical dictionaries contain information about people who have made some mark on history in many different fields. Consult the following: Contemporary Authors (I962–present), containing short biographies of authors who have published during the year; Current Biography (1940–present), containing articles and photographs of people in the news; and Who’s Who in America (1899–present), the standard biographical reference for living Americans.
  • Dictionaries contain definitions and histories of words, along with their syllabication, and correct spelling and usage.
  • Encyclopedias provide basic information, explanations, and definitions of virtually every topic, concept, country, institution, historical person or movement, and cultural artifact imaginable. One-volume works such as the Random House Encyclopedia and the Columbia Encyclopedia give brief overviews. Larger works, such as the New Encyclopædia Britannica (32 volumes, also online), contain more detailed information.

Databases, usually accessed directly through your library website, are indispensable tools for finding both journal and general-audience articles. Some databases contain general-interest information, indexing articles from newspapers, magazines, and sometimes scholarly journals as well. While these databases can be useful when you begin your research, once you have focused your research topic, you likely will need to use subject databases, which index articles primarily from specialized scholarly and technical journals.

The difference between scholarly journal and other articles is important. Although at times these lines are blurred, think of articles found in popular journals or magazines as published widely and usually addressing a general audience. Such materials are useful for obtaining introductory or background information on a topic as well as a sense of the range of factors to consider. Indeed, these sources may help you narrow your topic by giving you a basic understanding of the range and scope of the “conversation” you are entering in your research process. Scholarly journal articles , on the other hand, typically are written and published by academic researchers. These publications often have more specialized information and vocabulary and are most useful after you have narrowed your topic and developed specific research questions. Within the range of scholarly articles are those that are peer reviewed or found in peer-reviewed journals. These journal articles are generally more specific and contain more reliable information because they are written by experts and reviewed by other experts in the field before the article is published. See Compiling Sources for an Annotated Bibliography for more information about peer-reviewed publications.

A good starting point for research is a general-interest database , which covers a wide range of topics from many sources. Several major general-interest databases are listed below; however, many others may be available at your library. A librarian likely can help you find those that may be specific to your university.

  • Academic OneFile from Gale . Based on the access capabilities of your institution, you may be able to use this database, which indexes citations, abstracts, and some full texts in such subjects as the physical sciences, technology, medicine, social sciences, the arts, theology, literature, and more. By using this database, you may be able to retrieve the full text of articles provided in PDF and HTML formats and audio versions of texts in MP3 format.
  • Academic Search Complete from EBSCOhost. Your library also may provide you with access to this database, which indexes citations, abstracts, and full text from journal articles, books, reports, and conference proceedings in all disciplines. An advantage of this database is that you can retrieve full-text articles provided in PDF and HTML forms. Academic Search Complete also provides searchable cited references for nearly 1,000 journals.
  • CQ Researcher . This general database is unique because it publishes well-researched, single-themed 12,000-word reports by respected journalists who have established ethos because of their history of in-depth, unbiased coverage of health, social trends, criminal justice, international affairs, education, the environment, technology, and the economy. These reports can be beneficial at any research stage because they provide an overview, background, chronology, assessment of the current situation, tables and maps, pro/con statements from opposing positions, and bibliographies. Files from before 1996 are in HTML format; newer ones, beginning January 1996, are PDFs.
  • Factiva . Many students find Factiva a useful general tool because it provides full-text news articles and business/industry information from newswires, newspapers, business and industry magazines, television and radio transcripts, financial reports, and news service photos. Within the Factiva database, most content is HTML, though other formats are available for export. The database contains news sources from 1979 to the present and financial data from the 1960s to the present.
  • Google . One of the most frequently used databases for any research is Google. Students often use Google to begin their searches because they can find material from many different sources, both formal and informal, including blogs, journals, websites, and popular magazines. For academic research, you may find it useful to begin with a general Google search and then move to Google Scholar . Google Scholar provides a simple way to do a broad search for scholarly literature across a variety of disciplines and sources—articles, theses, books, et cetera. Within the Google database, you will also find more information or effective uses of Google for your research purposes. See Compiling Sources for an Annotated Bibliography for more information about Google Scholar.
  • Opposing Viewpoints in Context from Gale . As you familiarize yourself with your topic, you may find this database helpful for understanding the parameters of the discussion on your topic. Opposing Viewpoints offers over 20,000 pro/con viewpoint essays on controversial issues and current events, plus thousands of topic overviews, primary source documents, social activist biographies, court case overviews, related full-text periodical articles, statistical tables, and multimedia content.
  • Gale in Context . This database provides curated topic pages that combine academic journal articles, primary sources, reference works, essays, news sources, multimedia, and biographies about people, events, places, and time periods.
  • Web of Science from Clarivate Analytics . The three Web of Science databases index citations from journal articles and conference proceedings in the sciences, social sciences, arts, and humanities. You can access cited reference searches, analyze trends and patterns, and create visual representations of citation relationships. Its contents date from 1900 to the present.

Government Documents

The U.S. government publishes numerous reports, pamphlets, catalogs, and newsletters on most issues of national concern. To access documents from published in 1976 and onward, consult the Catalog of U.S. Government Publications . To find documents published prior to 1976, consult the Monthly Catalog of United States . Both resources should be available electronically and contain listings for materials in formats such as nonprint media, records, CDs, audiocassettes, videotapes, slides, photographs, and other media. Many of these publications may be located through your university’s library catalog as well. Consult a librarian to find out what government documents are available to you and in what forms.

Archives/Special Collections

Many libraries have donated records, papers, or writings that make up archives or special collections containing manuscripts, rare books, architectural drawings, historical photographs and maps, and so on. These, as well as items of local interest such as community and family histories, artifacts, and other memorabilia, are usually found in a special room or section of the library. By consulting these collections or archives, you also may find local or regional atlases, maps, and geographic information systems (GIS). Maps and atlases depict more than roads and boundaries. They include information on population density, language patterns, soil types, and much more. And, as discussed later in this chapter, these materials can figure into research projects as primary data.

University libraries’ special collections often house items donated by alumni, families, and other community groups. For example, one state university library’s special collections, housing a collection of Black Panther and American Communist Party newspapers and pamphlets, celebrated Black History Month with an exhibit featuring the Black Panther Party and the Black Power movement. Included in the exhibit were Black Panther newspapers and pamphlets published in the 1960s and 1970s, as well as earlier civil rights literature from the American Communist Party. This exhibit not only helped students become aware of information about the time and movement but also demonstrated the range and depth of the university’s archive collection.

Interlibrary Loans

Even though libraries house many materials, you may need a source unavailable at your library. If so, you usually can get the source through a networked system called interlibrary loans. Your library will borrow the source for you and provide some guidance as to the form of the materials and how long you will have access to them.

Whichever search tool you use, nothing is magic about information gathered. You will need to use critical skills to evaluate materials gathered from sources, and you will still need to ask these basic questions: Is the author identified? ls that person a professional in the field or an interested amateur? What are their biases likely to be? Does the document represent an individual’s opinion or peer-reviewed research?

Evaluating Sources

One key to judging the validity of sources is analysis. You already may be familiar with analysis, which involves looking at texts, media, or other artifacts to examine their individual parts and make interpretive claims about them. In the research process, analysis involves collecting data, deciding how you want to use that data (what are you looking for?), and applying those criteria to your data. For example, if you were looking at how the presence of social media has changed in television programs in the last five to seven years, you would determine what shows you want to view and what patterns you want to study.

As you analyze sources, you evaluate them in terms of your research needs. On the basis of your needs assessment, you will determine whether a source is acceptable or unacceptable, good or bad, trustworthy or biased. Although firm categories can be useful, you may find a more nuanced evaluation helpful as well. When you look for sources and evaluate them, begin with general questions such as these:

  • How do I want to use this source?
  • Am I able to use it in that way?
  • Might this source be more valuable if used in another way?

When you ask whether a source is acceptable, the answer usually depends on what you want to do with it. Even biased, false, or misleading material can be useful, depending on how a researcher puts it to use. For instance, you may be writing about a particular historical event and come across a magazine article featuring a biased account of that event. If your purpose is to write a brief but accurate description of the event, then this account is of little use. But what if your purpose is to write a critical analysis of the ways in which misleading media coverage of an event has influenced public perception of it? Suddenly, the biased account becomes useful as a specific example of the media coverage you wish to analyze.

A source’s value, therefore, is a function of your purpose for it. Labeling a source as good or bad, truthful or misleading, doesn’t really evaluate its use to you as a researcher and writer; truthful sources can be used poorly, and misleading sources can be used effectively. What matters is whether the source fits your purpose.

Finally, when evaluating a source, consider time ( when was it judged true? ) and perspective ( who said it was true, and for what reason? ).

Locate the Date

Most documents, especially those created since the advent of copyright laws at the end of the 19th century, include their date of publication. Pay attention to the date a source was created, and reflect on what might have happened since then. Information may be outdated and useless. On the other hand, it may still be highly useful—and continuing usefulness is the reason many old texts remain in circulation. Once you locate the source’s date, you can decide whether it will be relevant for your purpose. If you are studying change over time, for example, old statistical information would be useful baseline data to demonstrate what has changed. But if you are studying current culture, dated information may be misleading. In other words, when evaluating whether a dated source serves your purpose, know what that purpose is.

Identify Perspective

To identify and evaluate perspective, ask what viewpoint, or perspective, it represents. Who created the source, and for what purpose? This question can be difficult to answer immediately because an author’s viewpoint is not always identified or summarized in the source itself—and when it is, the information provided, being a creation of the author, cannot always be believed. To trust a source, you need to analyze its assumptions, evidence, biases, and reasoning, which together constitute the author’s perspective. ln essence, you need to ask these questions: What is this writer’s purpose? Is it scholarly analysis, political advocacy, entertainment, or something else? Consider the following:

  • Will a quick perusal of the introduction or first chapter reveal the writer’s assumptions about the subject or audience?
  • Can you tell which statements are facts, which are inferences drawn from facts, and which are strictly opinions?
  • Does a first reading of the evidence persuade you? Is the logic of the position apparent and/or credible?
  • Does the writer omit relevant points?
  • Do the answers to these questions make you more or less willing to accept the author’s conclusions?

Although trying to answer these questions about every source may seem daunting or even futile at first, have patience and give the research process the time it needs. At the beginning of a research project, when you are still trying to gain context and overview and have looked at only one source, you likely will have difficulty recognizing an author’s purpose and viewpoint. However, as you read further and begin to compare and contrast one source with another, differences will emerge, especially if you read extensively and take notes. The more differences you note, the more critically aware you become and the more you understand how and where a source might help you.

Review Critically

To review a source with a critical eye, ask both first and second questions of the text. The answers to first questions are generally factual, the result of probing the text (identifying the title, table of contents, chapter headings, index, and so on). The answers to second questions are more inferential, the result of analyzing assertions, evidence, and language in the text (identifying the perspective of the author and their sources).

Review Internally

Does information in one source support or contradict information in other sources? Do a subject search of the author across platforms to find out how other experts view the author and how your source fits in with the author’s other works.

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Cultural Value Centre logo

How to… find existing research

By robyn dowlen , centre for cultural value.

When you’re planning a research or evaluation project, finding out what research is already out there is an important place to start. But how do you find good research articles that are relevant to your project? And crucially, how do you stay focused and avoid going down a rabbit hole?

In this practical guide, the Centre for Cultural Value’s Postdoctoral Research Associate Dr Robyn Dowlen explains how to search and where to look for relevant research.

Keen to develop creative, value-driven approaches to evaluation? Sign up to our free-to-access course , led by academics and sector experts.

Introduction

When you’re planning your own research and evaluation, do you struggle to find relevant research that has already been conducted? Have you found you can’t access potentially useful research because it’s behind a paywall? Or that reading the abstract or description of articles doesn’t always help you understand whether the research is relevant?

If the answer is yes to any of these questions, this ‘How to…’ guide can help. I'll guide you through how to find good research articles, and identify and access the research relevant to your project.

Why is it important to understand what research is already out there?

  • Before you search...

Where to look

Reviewing literature is a common exercise undertaken by researchers, and an important part of building knowledge in a given field. Looking at the research literature that is already out there can help you or your organisation build a picture of the area you are interested in, and foster opportunities to develop new and exciting research.

By exploring what has come before, you can avoid doing unnecessary research. You can also build stronger justification for the novelty of your proposed research (i.e. I’m the first to do this), or showcase areas that need further research to strengthen the evidence base (i.e. I can address this gap in research). It can also help to scope who is working within the field. This can open up opportunities for discussion and even research collaborations.

Before you search…

The first thing to note is the expansive nature of literature. There is so much out there, from academic sources to consultancy reports, that it can be difficult to wade through without some guidance.

Here are a few steps to walk through before you scope out the literature.

1. Develop some questions you want to find answers to

When approaching the literature, you want to have some questions in mind that you are looking for the answers to. This will help you find the most relevant research and help you avoid going down a literature rabbit hole. You might find our How to... develop a research question guide a helpful place to start.

Here’s an example. In the context of my PhD research, I was interested in reviewing the literature surrounding the uses of music for people living with dementia. There were already reviews that focused on behavioural and psychological ‘symptoms’ of dementia but none that had considered what was meaningful to people living with dementia themselves.

So, the question I developed to explore the literature was:

What are the experiences of people living with dementia when they take part in music-related activities?

The experiences part of my question narrows the focus to qualitative literature because quantitative literature is not able to illuminate personal experiences as easily. My population is people living with dementia and I was specifically interested in music-related activities .

2. Develop keywords that are specific to your topic area

Now that you’ve landed on your question, you can start to develop keywords that will help you to identify literature that falls within the area you’re interested in. You need to make sure that you break down keywords that are too broad (e.g. arts) to something more specific (e.g. visual art).

Here’s an example of how I broke the keywords down in my review.

You can then use combinations of your search terms to input into search engines, databases and journal search bars, for example. And of course, if you have a link to a library this is often the best place to start.

Now you have your questions and keywords, it’s time to do some searching. There’s a number of ways to search for literature. Here's some of the key routes.

Research repositories

The table below provide some examples of online repositories which can be searched to identify both peer reviewed and non-academic literature. It is by no means extensive and you may come across others.

Examples of research repositories

Most universities will have their own research repository which gives access to preprints or open access articles. University repositories are also a great place to find student dissertations or theses which may not have been written up into publications yet.

There are also topic specific repositories, which bring together academic and non-academic literature within a central database. For example, I used the Repository for Arts and Health Resources to identify literature in our culture, health and wellbeing theme.

You can also search for research through Arts Council England’s research and data pages , as well as the Shared Research Repository for Cultural and Heritage Organisations .

Open access articles

Within each academic journal’s website there will be a search function where you can search by keywords for articles. You can refine the search by ‘Only show open access’ which will only show studies that are fully accessible for free.

For example, if you were interested in the value of music programmes for people living with dementia and searched for open access articles using Ageing & Society , your search would return papers most relevant to your keywords. In this case it is my PhD research findings paper.

Step 1 - search using keywords

Enter your keywords in the search bar

Searching for music dementia using the ageing & society website search bar

Step 2 - refine by open access

Searches will be returned by relevance.

In the Refine listing  column on the left you can refine by access type.

Under Access , tick the 'Only show open access' option to only view articles that are open access.

Ageing & society website search results for open access articles related to music dementia

Google Scholar

Google Scholar is a database that can be used to search for academic literature. While not everything will be open access, it is a useful place to identify preprints and open access sources. Many universities expect preprints to be uploaded by researchers to be able to ensure research is not held behind a paywall. Many of the PDFs you find will be uploaded to university repositories and linked to Google Scholar search results.

This is an example of using Google Scholar to find preprints.

Google Scholar search results for museums decolonization preprints

  • Enter your key terms into the search bar
  • Narrow your search by date range
  • Create an account and save articles that interest you in your library
  • Look for articles that have PDFs attached
  • If a PDF isn’t listed, check all versions

You may not be able to see an article listed with an accompanying PDF, but it is always worth clicking on the ‘All versions’ button beneath articles as there may be an alternative access route.

Google Scholar is also useful if you want to find out about the publications of a specific researcher. Academics can create profiles which collate all their research into one place. Here’s my profile as an example.

Robyn Dowlen's author profile on Google Scholar

If you click the ‘Public Access’ tab, it will take you through to all the open access resources by that author.

Search results for open access articles by Dr Robyn Dowlen using Google Scholar

Bibliographies and reference lists

Each research publication (should) have a list of sources at the end of it. This will list any citations that have been used to support the rationale of the study, as well as supporting the justification of methods etc. This is a great place to find any literature related to the research you have found. Useful citations tend to be within the introduction or literature review of an article.

Centre for Cultural Value resources

The Centre for Cultural Value reviews and summarises existing research to make it more easily accessible so its insights can be understood and applied more widely. We have published a number of research digests which outline what the evidence is for different topic areas and where future research has focused. Digests are published every few months.

We also have case studies and podcast episodes which discuss approaches to evaluation and research, as well as unpicking what future research can build upon.

You may also want to explore CultureCase , which also has accessible research summaries that support arts and cultural activities.

Contact the authors

If open access searching still doesn’t return the literature you are most interested in, you can contact the authors of publications directly to see if they would be happy to share the publication with you – and this might offer up an opportunity to have a conversation with them about their work. It is always worth asking as most researchers are only too happy to be contacted. I always respond to requests, it might just take me a little while to answer.

Every journal article, whether open access or not, has a corresponding author with an email address listed. So, in the case of one of my publications (see below), click ‘show author details’ to display an email address. In some journals you can hover over author names for the email address to be displayed. Most research repositories will also list contact details of research authors.

In this case, click 'Show author details' to find a contact email.

existing research

Paywall - where access is restricted to users without library subscriptions. Typically you can pay for access to the articles.

Peer-reviewed – the process of scrutiny of an author’s work by experts before academic journal articles are accepted for publication.

Preprints – an author’s manuscript that is published ahead of the peer-review process; or the author’s own version of the text which has been accepted for publication but not yet published.

existing research

Related resources

Two women in a park drawing on a map on a table

How to… co-commission research

existing research

How to… develop a research question

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How to…broker a successful academic partnership

Find more resources on the evaluation learning space, sign up for the centre for cultural value’s newsletter to hear about our latest research, resources, events and funding opportunities..

Grad Coach

The Research Gap (Literature Gap)

Everything you need to know to find a quality research gap

By: Ethar Al-Saraf (PhD) | Expert Reviewed By: Eunice Rautenbach (DTech) | November 2022

If you’re just starting out in research, chances are you’ve heard about the elusive research gap (also called a literature gap). In this post, we’ll explore the tricky topic of research gaps. We’ll explain what a research gap is, look at the four most common types of research gaps, and unpack how you can go about finding a suitable research gap for your dissertation, thesis or research project.

Overview: Research Gap 101

  • What is a research gap
  • Four common types of research gaps
  • Practical examples
  • How to find research gaps
  • Recap & key takeaways

What (exactly) is a research gap?

Well, at the simplest level, a research gap is essentially an unanswered question or unresolved problem in a field, which reflects a lack of existing research in that space. Alternatively, a research gap can also exist when there’s already a fair deal of existing research, but where the findings of the studies pull in different directions , making it difficult to draw firm conclusions.

For example, let’s say your research aims to identify the cause (or causes) of a particular disease. Upon reviewing the literature, you may find that there’s a body of research that points toward cigarette smoking as a key factor – but at the same time, a large body of research that finds no link between smoking and the disease. In that case, you may have something of a research gap that warrants further investigation.

Now that we’ve defined what a research gap is – an unanswered question or unresolved problem – let’s look at a few different types of research gaps.

A research gap is essentially an unanswered question or unresolved problem in a field, reflecting a lack of existing research.

Types of research gaps

While there are many different types of research gaps, the four most common ones we encounter when helping students at Grad Coach are as follows:

  • The classic literature gap
  • The disagreement gap
  • The contextual gap, and
  • The methodological gap

Need a helping hand?

existing research

1. The Classic Literature Gap

First up is the classic literature gap. This type of research gap emerges when there’s a new concept or phenomenon that hasn’t been studied much, or at all. For example, when a social media platform is launched, there’s an opportunity to explore its impacts on users, how it could be leveraged for marketing, its impact on society, and so on. The same applies for new technologies, new modes of communication, transportation, etc.

Classic literature gaps can present exciting research opportunities , but a drawback you need to be aware of is that with this type of research gap, you’ll be exploring completely new territory . This means you’ll have to draw on adjacent literature (that is, research in adjacent fields) to build your literature review, as there naturally won’t be very many existing studies that directly relate to the topic. While this is manageable, it can be challenging for first-time researchers, so be careful not to bite off more than you can chew.

Free Webinar: How To Write A Research Proposal

2. The Disagreement Gap

As the name suggests, the disagreement gap emerges when there are contrasting or contradictory findings in the existing research regarding a specific research question (or set of questions). The hypothetical example we looked at earlier regarding the causes of a disease reflects a disagreement gap.

Importantly, for this type of research gap, there needs to be a relatively balanced set of opposing findings . In other words, a situation where 95% of studies find one result and 5% find the opposite result wouldn’t quite constitute a disagreement in the literature. Of course, it’s hard to quantify exactly how much weight to give to each study, but you’ll need to at least show that the opposing findings aren’t simply a corner-case anomaly .

existing research

3. The Contextual Gap

The third type of research gap is the contextual gap. Simply put, a contextual gap exists when there’s already a decent body of existing research on a particular topic, but an absence of research in specific contexts .

For example, there could be a lack of research on:

  • A specific population – perhaps a certain age group, gender or ethnicity
  • A geographic area – for example, a city, country or region
  • A certain time period – perhaps the bulk of the studies took place many years or even decades ago and the landscape has changed.

The contextual gap is a popular option for dissertations and theses, especially for first-time researchers, as it allows you to develop your research on a solid foundation of existing literature and potentially even use existing survey measures.

Importantly, if you’re gonna go this route, you need to ensure that there’s a plausible reason why you’d expect potential differences in the specific context you choose. If there’s no reason to expect different results between existing and new contexts, the research gap wouldn’t be well justified. So, make sure that you can clearly articulate why your chosen context is “different” from existing studies and why that might reasonably result in different findings.

Get help finding a research topic

4. The Methodological Gap

Last but not least, we have the methodological gap. As the name suggests, this type of research gap emerges as a result of the research methodology or design of existing studies. With this approach, you’d argue that the methodology of existing studies is lacking in some way , or that they’re missing a certain perspective.

For example, you might argue that the bulk of the existing research has taken a quantitative approach, and therefore there is a lack of rich insight and texture that a qualitative study could provide. Similarly, you might argue that existing studies have primarily taken a cross-sectional approach , and as a result, have only provided a snapshot view of the situation – whereas a longitudinal approach could help uncover how constructs or variables have evolved over time.

existing research

Practical Examples

Let’s take a look at some practical examples so that you can see how research gaps are typically expressed in written form. Keep in mind that these are just examples – not actual current gaps (we’ll show you how to find these a little later!).

Context: Healthcare

Despite extensive research on diabetes management, there’s a research gap in terms of understanding the effectiveness of digital health interventions in rural populations (compared to urban ones) within Eastern Europe.

Context: Environmental Science

While a wealth of research exists regarding plastic pollution in oceans, there is significantly less understanding of microplastic accumulation in freshwater ecosystems like rivers and lakes, particularly within Southern Africa.

Context: Education

While empirical research surrounding online learning has grown over the past five years, there remains a lack of comprehensive studies regarding the effectiveness of online learning for students with special educational needs.

As you can see in each of these examples, the author begins by clearly acknowledging the existing research and then proceeds to explain where the current area of lack (i.e., the research gap) exists.

Free Webinar: How To Find A Dissertation Research Topic

How To Find A Research Gap

Now that you’ve got a clearer picture of the different types of research gaps, the next question is of course, “how do you find these research gaps?” .

Well, we cover the process of how to find original, high-value research gaps in a separate post . But, for now, I’ll share a basic two-step strategy here to help you find potential research gaps.

As a starting point, you should find as many literature reviews, systematic reviews and meta-analyses as you can, covering your area of interest. Additionally, you should dig into the most recent journal articles to wrap your head around the current state of knowledge. It’s also a good idea to look at recent dissertations and theses (especially doctoral-level ones). Dissertation databases such as ProQuest, EBSCO and Open Access are a goldmine for this sort of thing. Importantly, make sure that you’re looking at recent resources (ideally those published in the last year or two), or the gaps you find might have already been plugged by other researchers.

Once you’ve gathered a meaty collection of resources, the section that you really want to focus on is the one titled “ further research opportunities ” or “further research is needed”. In this section, the researchers will explicitly state where more studies are required – in other words, where potential research gaps may exist. You can also look at the “ limitations ” section of the studies, as this will often spur ideas for methodology-based research gaps.

By following this process, you’ll orient yourself with the current state of research , which will lay the foundation for you to identify potential research gaps. You can then start drawing up a shortlist of ideas and evaluating them as candidate topics . But remember, make sure you’re looking at recent articles – there’s no use going down a rabbit hole only to find that someone’s already filled the gap 🙂

Let’s Recap

We’ve covered a lot of ground in this post. Here are the key takeaways:

  • A research gap is an unanswered question or unresolved problem in a field, which reflects a lack of existing research in that space.
  • The four most common types of research gaps are the classic literature gap, the disagreement gap, the contextual gap and the methodological gap. 
  • To find potential research gaps, start by reviewing recent journal articles in your area of interest, paying particular attention to the FRIN section .

If you’re keen to learn more about research gaps and research topic ideation in general, be sure to check out the rest of the Grad Coach Blog . Alternatively, if you’re looking for 1-on-1 support with your dissertation, thesis or research project, be sure to check out our private coaching service .

existing research

Psst… there’s more (for free)

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

You Might Also Like:

How To Find a Research Gap (Fast)

29 Comments

ZAID AL-ZUBAIDI

This post is REALLY more than useful, Thank you very very much

Abdu Ebrahim

Very helpful specialy, for those who are new for writing a research! So thank you very much!!

Zinashbizu

I found it very helpful article. Thank you.

fanaye

Just at the time when I needed it, really helpful.

Tawana Ngwenya

Very helpful and well-explained. Thank you

ALI ZULFIQAR

VERY HELPFUL

A.M Kwankwameri

We’re very grateful for your guidance, indeed we have been learning a lot from you , so thank you abundantly once again.

ahmed

hello brother could you explain to me this question explain the gaps that researchers are coming up with ?

Aliyu Jibril

Am just starting to write my research paper. your publication is very helpful. Thanks so much

haziel

How to cite the author of this?

kiyyaa

your explanation very help me for research paper. thank you

Bhakti Prasad Subedi

Very important presentation. Thanks.

Best Ideas. Thank you.

Getachew Gobena

I found it’s an excellent blog to get more insights about the Research Gap. I appreciate it!

Juliana Otabil

Kindly explain to me how to generate good research objectives.

Nathan Mbandama

This is very helpful, thank you

Favour

Very helpful, thank you.

Vapeuk

Thanks a lot for this great insight!

Effie

This is really helpful indeed!

Guillermo Dimaligalig

This article is really helpfull in discussing how will we be able to define better a research problem of our interest. Thanks so much.

Yisa Usman

Reading this just in good time as i prepare the proposal for my PhD topic defense.

lucy kiende

Very helpful Thanks a lot.

TOUFIK

Thank you very much

Dien Kei

This was very timely. Kudos

Takele Gezaheg Demie

Great one! Thank you all.

Efrem

Thank you very much.

Rev Andy N Moses

This is so enlightening. Disagreement gap. Thanks for the insight.

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Research and innovation menu, research and innovation, existing and ongoing research.

In the foreground a monitor displays a scan of a person's head. In the background, a person is seen in an MRI machine.

The revised Common Rule requires existing research (approved or determined exempt prior to January 21, 2019) to remain compliant with the pre-2018 regulations. Therefore, for existing studies, researchers do not need to take any action at this time beyond maintaining active IRB approval.  Maintaining IRB approval includes submitting for continuing review (unless previously granted exemption).

Existing research can be transitioned to operate under the revised Common rule only after being reviewed and determined to comply with the revised Common Rule. To be brought into compliance with the revised regulations, existing studies may require substantial revisions. 

To assess whether to transition existing research to operate under the revised Common Rule, RCS will work with researchers in advance of a study’s existing expiration and/or when an amendment is submitted. In some cases, it may be best for a project to remain under the pre-2018 regulations. In other cases, it may be best to transition an existing project to operate under the revised Common Rule. 

Process for Transitioning

The revised Common Rule offers flexibility to allow existing studies to remain under the pre-2018 Common Rule. Existing studies are not required to comply with the revised Common Rule. Studies cannot pick and choose between the two sets of regulations. In order to transition to the revised Common Rule, a study must be determined to satisfy all criteria under one set of regulations or the other.

To transition an existing study from oversight under the pre-2018 Common Rule to the 2018 revised Common Rule, the IRB must determine that all criteria in the 2018 revised Common Rule are or can be satisfied. 

Existing studies will be automatically assessed for transition readiness upon submission of an amendment or continuing review. Transition assessment will be based on the considerations described below.

Preliminary Considerations for Transition

To determine if a study is a good fit for transition to the 2018 revised Common Rule, the IRB will consider the study’s progress, study activities, additional regulatory oversight requirements, and the extent of revisions required to achieve compliance.

Key considerations include:

  • Estimated end date
  • Conclusion for active data collection
  • Approaching expiration and extent of changes needed to achieve compliance
  • Whether there are multiple IRB reviews
  • Impact of transition on other sites
  • Collaborating site requirements and/or policies
  • Whether the funding agency requires different or additional regulatory compliance
  • Number of studies, sub studies, subject populations, consent documents, research tasks
  • History/age of protocol
  • Quality and completeness of submission
  • Researcher responsiveness/willingness to transition
  • Whether future phases/design changes are anticipated
  • Previous compliance or other challenges that would or would not make the protocol a good candidate for transition.

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Revolutionizing the Study of Mental Disorders

March 27, 2024 • Feature Story • 75th Anniversary

At a Glance:

  • The Research Domain Criteria framework (RDoC) was created in 2010 by the National Institute of Mental Health.
  • The framework encourages researchers to examine functional processes that are implemented by the brain on a continuum from normal to abnormal.
  • This way of researching mental disorders can help overcome inherent limitations in using all-or-nothing diagnostic systems for research.
  • Researchers worldwide have taken up the principles of RDoC.
  • The framework continues to evolve and update as new information becomes available.

President George H. W. Bush proclaimed  the 1990s “ The Decade of the Brain  ,” urging the National Institutes of Health, the National Institute of Mental Health (NIMH), and others to raise awareness about the benefits of brain research.

“Over the years, our understanding of the brain—how it works, what goes wrong when it is injured or diseased—has increased dramatically. However, we still have much more to learn,” read the president’s proclamation. “The need for continued study of the brain is compelling: millions of Americans are affected each year by disorders of the brain…Today, these individuals and their families are justifiably hopeful, for a new era of discovery is dawning in brain research.”

An image showing an FMRI machine with computer screens showing brain images. Credit: iStock/patrickheagney.

Still, despite the explosion of new techniques and tools for studying the brain, such as functional magnetic resonance imaging (fMRI), many mental health researchers were growing frustrated that their field was not progressing as quickly as they had hoped.

For decades, researchers have studied mental disorders using diagnoses based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)—a handbook that lists the symptoms of mental disorders and the criteria for diagnosing a person with a disorder. But, among many researchers, suspicion was growing that the system used to diagnose mental disorders may not be the best way to study them.

“There are many benefits to using the DSM in medical settings—it provides reliability and ease of diagnosis. It also provides a clear-cut diagnosis for patients, which can be necessary to request insurance-based coverage of healthcare or job- or school-based accommodations,” said Bruce Cuthbert, Ph.D., who headed the workgroup that developed NIMH’s Research Domain Criteria Initiative. “However, when used in research, this approach is not always ideal.”

Researchers would often test people with a specific diagnosed DSM disorder against those with a different disorder or with no disorder and see how the groups differed. However, different mental disorders can have similar symptoms, and people can be diagnosed with several different disorders simultaneously. In addition, a diagnosis using the DSM is all or none—patients either qualify for the disorder based on their number of symptoms, or they don’t. This black-and-white approach means there may be people who experience symptoms of a mental disorder but just miss the cutoff for diagnosis.

Dr. Cuthbert, who is now the senior member of the RDoC Unit which orchestrates RDoC work, stated that “Diagnostic systems are based on clinical signs and symptoms, but signs and symptoms can’t really tell us much about what is going on in the brain or the underlying causes of a disorder. With modern neuroscience, we were seeing that information on genetic, pathophysiological, and psychological causes of mental disorders did not line up well with the current diagnostic disorder categories, suggesting that there were central processes that relate to mental disorders that were not being reflected in DMS-based research.”

Road to evolution

Concerned about the limits of using the DSM for research, Dr. Cuthbert, a professor of clinical psychology at the University of Minnesota at the time, approached Dr. Thomas Insel (then NIMH director) during a conference in the autumn of 2008. Dr. Cuthbert recalled saying, “I think it’s really important that we start looking at dimensions of functions related to mental disorders such as fear, working memory, and reward systems because we know that these dimensions cut across various disorders. I think NIMH really needs to think about mental disorders in this new way.”

Dr. Cuthbert didn’t know it then, but he was suggesting something similar to ideas that NIMH was considering. Just months earlier, Dr. Insel had spearheaded the inclusion of a goal in NIMH’s 2008 Strategic Plan for Research to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.”

Unaware of the new strategic goal, Dr. Cuthbert was surprised when Dr. Insel's senior advisor, Marlene Guzman, called a few weeks later to ask if he’d be interested in taking a sabbatical to help lead this new effort. Dr. Cuthbert soon transitioned into a full-time NIMH employee, joining the Institute at an exciting time to lead the development of what became known as the Research Domain Criteria (RDoC) Framework. The effort began in 2009 with the creation of an internal working group of interdisciplinary NIMH staff who identified core functional areas that could be used as examples of what research using this new conceptual framework looked like.

The workgroup members conceived a bold change in how investigators studied mental disorders.

“We wanted researchers to transition from looking at mental disorders as all or none diagnoses based on groups of symptoms. Instead, we wanted to encourage researchers to understand how basic core functions of the brain—like fear processing and reward processing—work at a biological and behavioral level and how these core functions contribute to mental disorders,” said Dr. Cuthbert.

This approach would incorporate biological and behavioral measures of mental disorders and examine processes that cut across and apply to all mental disorders. From Dr. Cuthbert’s standpoint, this could help remedy some of the frustrations mental health researchers were experiencing.

Around the same time the workgroup was sharing its plans and organizing the first steps, Sarah Morris, Ph.D., was a researcher focusing on schizophrenia at the University of Maryland School of Medicine in Baltimore. When she first read these papers, she wondered what this new approach would mean for her research, her grants, and her lab.

She also remembered feeling that this new approach reflected what she was seeing in her data.

“When I grouped my participants by those with and without schizophrenia, there was a lot of overlap, and there was a lot of variability across the board, and so it felt like RDoC provided the pathway forward to dissect that and sort it out,” said Dr. Morris.

Later that year, Dr. Morris joined NIMH and the RDoC workgroup, saying, “I was bumping up against a wall every day in my own work and in the data in front of me. And the idea that someone would give the field permission to try something new—that was super exciting.”

The five original RDoC domains of functioning were introduced to the broader scientific community in a series of articles published in 2010  .

To establish the new framework, the RDoC workgroup (including Drs. Cuthbert and Morris) began a series of workshops in 2011 to collect feedback from experts in various areas from the larger scientific community. Five workshops were held over the next two years, each with a different broad domain of functioning based upon prior basic behavioral neuroscience. The five domains were called:

  • Negative valence (which included processes related to things like fear, threat, and loss)
  • Positive valence (which included processes related to working for rewards and appreciating rewards)
  • Cognitive processes
  • Social processes
  • Arousal and regulation processes (including arousal systems for the body and sleep).

At each workshop, experts defined several specific functions, termed constructs, that fell within the domain of interest. For instance, constructs in the cognitive processes domain included attention, memory, cognitive control, and others.

The result of these feedback sessions was a framework that described mental disorders as the interaction between different functional processes—processes that could occur on a continuum from normal to abnormal. Researchers could measure these functional processes in a variety of complementary ways—for example, by looking at genes associated with these processes, the brain circuits that implement these processes, tests or observations of behaviors that represent these functional processes, and what patients report about their concerns. Also included in the framework was an understanding that functional processes associated with mental disorders are impacted and altered by the environment and a person’s developmental stage.

Preserving momentum

An image depicting the RDoC Framework that includes four overlapping circles (titled: Lifespan, Domains, Units of Analysis, and Environment).

Over time, the Framework continued evolving and adapting to the changing science. In 2018, a sixth functional area called sensorimotor processes was added to the Framework, and in 2019, a workshop was held to better incorporate developmental and environmental processes into the framework.;

Since its creation, the use of RDoC principles in mental health research has spread across the U.S. and the rest of the world. For example, the Psychiatric Ratings using Intermediate Stratified Markers project (PRISM)   , which receives funding from the European Union’s Innovative Medicines Initiative, is seeking to link biological markers of social withdrawal with clinical diagnoses using RDoC-style principles. Similarly, the Roadmap for Mental Health Research in Europe (ROAMER)   project by the European Commission sought to integrate mental health research across Europe using principles similar to those in the RDoC Framework.;

Dr. Morris, who has acceded to the Head of the RDoC Unit, commented: “The fact that investigators and science funders outside the United States are also pursuing similar approaches gives me confidence that we’ve been on the right pathway. I just think that this has got to be how nature works and that we are in better alignment with the basic fundamental processes that are of interest to understanding mental disorders.”

The RDoC framework will continue to adapt and change with emerging science to remain relevant as a resource for researchers now and in the future. For instance, NIMH continues to work toward the development and optimization of tools to assess RDoC constructs and supports data-driven efforts to measure function within and across domains.

“For the millions of people impacted by mental disorders, research means hope. The RDoC framework helps us study mental disorders in a different way and has already driven considerable change in the field over the past decade,” said Joshua A. Gordon, M.D., Ph.D., director of NIMH. “We hope this and other innovative approaches will continue to accelerate research progress, paving the way for prevention, recovery, and cure.”

Publications

Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine , 11 , 126. https://doi.org/10.1186/1741-7015-11-126  

Cuthbert B. N. (2014). Translating intermediate phenotypes to psychopathology: The NIMH Research Domain Criteria. Psychophysiology , 51 (12), 1205–1206. https://doi.org/10.1111/psyp.12342  

Cuthbert, B., & Insel, T. (2010). The data of diagnosis: New approaches to psychiatric classification. Psychiatry , 73 (4), 311–314. https://doi.org/10.1521/psyc.2010.73.4.311  

Cuthbert, B. N., & Kozak, M. J. (2013). Constructing constructs for psychopathology: The NIMH research domain criteria. Journal of Abnormal Psychology , 122 (3), 928–937. https://doi.org/10.1037/a0034028  

Garvey, M. A., & Cuthbert, B. N. (2017). Developing a motor systems domain for the NIMH RDoC program.  Schizophrenia Bulletin , 43 (5), 935–936. https://doi.org/10.1093/schbul/sbx095  

Insel, T. (2013). Transforming diagnosis . http://www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml

Kozak, M. J., & Cuthbert, B. N. (2016). The NIMH Research Domain Criteria initiative: Background, issues, and pragmatics. Psychophysiology , 53 (3), 286–297. https://doi.org/10.1111/psyp.12518  

Morris, S. E., & Cuthbert, B. N. (2012). Research Domain Criteria: Cognitive systems, neural circuits, and dimensions of behavior. Dialogues in Clinical Neuroscience , 14 (1), 29–37. https://doi.org/10.31887/DCNS.2012.14.1/smorris  

Sanislow, C. A., Pine, D. S., Quinn, K. J., Kozak, M. J., Garvey, M. A., Heinssen, R. K., Wang, P. S., & Cuthbert, B. N. (2010). Developing constructs for psychopathology research: Research domain criteria. Journal of Abnormal Psychology , 119 (4), 631–639. https://doi.org/10.1037/a0020909  

  • Presidential Proclamation 6158 (The Decade of the Brain) 
  • Research Domain Criteria Initiative website
  • Psychiatric Ratings using Intermediate Stratified Markers (PRISM)  
  • Roadmap for Mental Health Research in Europe (ROAMER)  

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Creating a Corporate Social Responsibility Program with Real Impact

  • Emilio Marti,
  • David Risi,
  • Eva Schlindwein,
  • Andromachi Athanasopoulou

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Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

  • EM Emilio Marti is an associate professor at the Rotterdam School of Management, Erasmus University. His research focuses on corporate sustainability with a specific focus on sustainable investing.
  • DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
  • ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
  • AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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Cancer research landmarks, cancer hallmarks review, cancer biology, cancer immunology, cancer metabolism and molecular mechanisms, translational cancer biology, computational cancer biology and technology, journal archive, cancer research (1941-present; volumes 1-current), the american journal of cancer (1931-1940; volumes 15-40), the journal of cancer research (1916-1930); volumes 1-14), table of contents, supporting convergence science, targeting metabolic dependencies fueling the tca cycle to circumvent therapy resistance in acute myeloid leukemia, personalized cancer vaccines directed against tumor mutations: building evidence from mice to humans, cancer builds a noxious partnership with psychologic stress, the rigidity connection: matrix stiffness and its impact on cancer progression, exploring ferroptosis-inducing therapies for cancer treatment: challenges and opportunities, chromatin remodelers are regulators of the tumor immune microenvironment, sarcoma cells secrete hypoxia-modified collagen vi to weaken the lung endothelial barrier and promote metastasis.

Collagen type VI modified by hypoxia-induced PLOD2 is secreted by sarcoma cells and binds to integrin β1 on endothelial cells to induce barrier dysfunction, which promotes sarcoma vascular dissemination and metastasis.

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Lactate Utilization Enables Metabolic Escape to Confer Resistance to BET Inhibition in Acute Myeloid Leukemia

Lactate utilization allows AML myeloblasts to maintain metabolic integrity and circumvent antileukemic therapy, which supports testing of lactate utilization inhibitors in clinical settings to overcome BET inhibitor resistance in AML.

The Extracellular Niche and Tumor Microenvironment Enhance KRAS Inhibitor Efficacy in Pancreatic Cancer

Pharmacologic inhibition of KRAS elicits varied responses in pancreatic cancer 2D cell lines, 3D organoid cultures, and xenografts, underscoring the importance of mechanotransduction and the tumor microenvironment in regulating therapeutic responses.

Cyclic Fasting–Mimicking Diet Plus Bortezomib and Rituximab Is an Effective Treatment for Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia cells resist fasting-mimicking diet by inducing proteasome activation to escape starvation, which can be targeted using proteasome inhibition by bortezomib treatment to impede leukemia progression and prolong survival.

Subclonal Cancer Driver Mutations Are Prevalent in the Unresected Peritumoral Edema of Adult Diffuse Gliomas

True2 is a next-generation sequencing workflow that facilitates unbiased discovery of somatic mutations across the full range of variant allele frequencies, which could help identify residual disease vulnerabilities for targeted adjuvant therapies.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non–Small Cell Lung Cancer

Leveraging artificial intelligence–powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

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In line with the critical approach of this book, it is useful to precede our own contribution to the field of research with a discussion and evaluation of the methods and approaches to researching sexting that have been used in research to date. This chapter starts with a critical analysis of the existing surveys into sexting practices by young people and then looks more closely at current qualitative research. A review at the time of writing identified ten such quantitative surveys. 1 Most of these are aimed at identifying the prevalence of sexting among young people and only a small proportion drill further into the practices of, and the motives or reasons for, sexting (National Campaign to Prevent Teen and Unplanned Pregnancy 2008; Mitchell et al. 2012; Dake et al. 2013), or the emotional or practical consequences of sexting (Dake et al. 2013; Phippen 2009; National Campaign to Prevent Teen and Unplanned Pregnancy 2008; Strassberg et al. 2013; Mitchell et al. 2012; Talion et al. 2012).

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Crofts, T., Lee, M., McGovern, A., Milivojevic, S. (2015). Review of Existing Research. In: Sexting and Young People. Palgrave Macmillan, London. https://doi.org/10.1057/9781137392817_7

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  • Published: 01 April 2024

Large language models reveal big disparities in current wildfire research

  • Zhengyang Lin   ORCID: orcid.org/0000-0002-9237-1380 1   na1 ,
  • Anping Chen   ORCID: orcid.org/0000-0003-2085-3863 2   na1 ,
  • Xuhui Wang   ORCID: orcid.org/0000-0003-0818-9816 1 ,
  • Zhihua Liu 3 &
  • Shilong Piao   ORCID: orcid.org/0000-0001-8057-2292 1  

Communications Earth & Environment volume  5 , Article number:  168 ( 2024 ) Cite this article

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  • Fire ecology
  • Natural hazards

Contemporary fire-human-climate nexus has led to a surge in publication numbers across diverse research disciplines beyond the capability of experts from a single discipline. Here, we employed a generalized large language model to capture the dynamics of wildfire research published between 1980 and 2022. More than 60,000 peer-reviewed papers were scanned and analyzed. Through integrating geographical metadata extracted by the artificial intelligence and satellite wildfire datasets, we found large disparities in geographic patterns and research themes. The hottest spot of wildfire research is western United States, accounting for 15% of publications but only 0.5% of global burnt area, while the world’s most widely burnt region, like Siberia and Africa are largely underrepresented by contemporary publications. Similar discrepancies are found between the fuel of wildfire and its ignition and climatic drivers, between socioeconomic development and wildfire mitigation, raising concerns on sustainable wildfire managements and calling for further artificial intelligence-aided transdisciplinary collaborations.

Introduction

The escalating severity and frequency of wildfires and the risks they pose to ecosystems and society have stimulated a rising interest in wildfire research. Indeed, a search of the Web of Science (WOS) bibliographic database using wildfire-related keywords returned over 100,000 papers (see Methods). More notably, the number of publications per year has surged by more than fourfold in the last two decades (Fig.  1 ). This rapid growth in wildfire research has been contributed by an interdisciplinary community. However, communications across disciplines remain limited, raising the question of how research on this topic is distributed across various aspects of wildfire research and geographical regions. Addressing this issue is crucial for identifying research gaps and informing future research priorities.

figure 1

The topics are marked by circles and bold-oblique font, sub-topics by rounded rectangles and normal font. Different colors indicate different topics; while the sizes of these markers and the widths of their connecting lines represent the numbers of publications.

On the other hand, the growing volume of literature also poses grand challenges for literature reanalysis that aims to recap knowledge consensus and identify research gaps. Traditional expert-based methods, such as literature reviews and meta-analyses 1 , 2 , 3 , are often constrained to a limited number of publications, typically a few hundred. More recently, artificial intelligence (AI)-based approaches, including deep-learning methods, develop the capacity for analyzing large volumes of literature, offering a solution to bridge the gap between the exponentially increasing number of publications and the ability to effectively synthesize them 4 . In particular, the rapid growth in the size of Large Language Models (LLMs) 5 , 6 , 7 have enabled end-users to perform a variety of complex tasks beyond semantic analyses 8 , making it a potentially powerful tool for overcoming the limitations of traditional literature analyses. These LLMs, represented by ChatGPT, are known for substantially large corpus in their training stages and capable for general tasks, providing the confidence in completing tasks such as textual geographical entity recognition. Furthermore, these models have demonstrated unprecedented “emergent abilities”, even reaching human-level performance in various complex tasks 9 , 10 , 11 .

Here, we designed a literature analysis workflow (Supplementary Fig.  1 ) to disentangle thematic and regional patterns in recent wildfire research with gpt-3.5-turbo (updated on 13th Feb 2023), the same LLM model underlying the public version of ChatGPT. The model has already been proved to function effectively even without fine-tune, making it more convenient for the initial information extraction and classification processes. A recent study also showed comparable or even better performance in ChatGPT than conventional Natural Language Processing (NLP) method (e.g., BERT) models especially on inference and question-answering tasks 12 . We used the LLM to first categorize the collected publications into different themes related to various aspects of wildfire research, including its causes, consequences, and the methodologies used to detect or simulate wildfire dynamics (see Methods). Additionally, we extracted the geolocation information and converted it from textual descriptions in titles and abstracts into numeric forms with the maximal outside boundary coordinates, also known as geoparsing. Ultimately, we were able to include 60,488 relevant articles for subsequent analysis (see Methods for paper inclusion criteria). We focused on addressing the following questions: (1) What prevailing preferences and trends have emerged in wildfire research over recent decades? (2) How do spatio-temporal variations in research paradigms manifest? (3) What are the real-world implications of disparities in research of wildfire components and actual wildfire activities for populations and socioeconomic development?

Among all the keywords associated with wildfire research, “vegetation” emerged as the most frequently discussed, accounting for 47% of the papers. Within the “vegetation” category, forest fires attracted the most attention (72%; Fig.  1 ). There was also a large volume of wildfire studies involving “anthropogenic,” “atmospheric,” and “modeling” aspects.

From a temporal evolutionary perspective, the recent rapid increase in the overall number of wildfire publications (Fig.  2a ) is comparable to other trending key words such as climate change 4 . Furthermore, we also observed distinct shifts in topics that have experienced the most rapid growth since 1990s (Fig.  2a ). For instance, when comparing research preferences in the 2000s to that in the 1990s, we found that discussions on the “hydrological” and “atmospheric” impacts of wildfires were among the fastest-growing topics. Since the turn of the century, the widespread adoption of advanced satellite-based instrumentation and retrieval/analysis algorithms, exemplified by the deployment of the Moderate Resolution Imaging Spectroradiometer (MODIS), has catalyzed increased scholarly interest in “remote sensing” themes 13 . Similar shifts have been observed in the most recent decade, during which the domains of “climate change” and “anthropogenic” influences have gained prominence in wildfire research 4 .

figure 2

a Growth in the number of wildfire publications. The dotted and solid lines represent the “total wildfire publications” and “geoparsed wildfire publications”. Boxes highlight three topics that have the highest increase rate during their past decades. b The spatial patterns of total geoparsed publication numbers during 1980 to 2022. The blank pixels represent no observed burned area according to a long-term global burned area product, AVHRR-LTDR (1982-2018) 31 .

Using the LLM to extract and parse geolocation information from the texts (see Methods), we were able to map published papers according to their focused regions (Fig.  2b ). An analysis of affiliations of full authors’ lists of selected papers uncovered a consistent pattern that countries with higher levels of development dominate the research landscape. Specifically, 87.1% of the total research contributions come from the leading 20 countries by publication count, with more than half of them classified as high-income economies (Supplementary Fig.  2 ). In addition, 517 (70.4%) of the 732 papers dedicated to wildfire datasets were contributed from high-income countries, while less than 10% originated from lower-middle income countries. Clearly, such publication biases in wildfire research papers and datasets were primarily due to resource inequality (e.g., funding to conduct research, generate and maintain databases) between developed and developing countries. Inadequate funding could largely hamper the investment in wildfire research, monitoring systems, and policy measures for mitigation 14 , 15 .

Furthermore, we also found a notable discrepancy in the attentions of study areas (Fig.  2a ). For instance, while the burned area in the western United States accounts for less than 0.5% of the global burned area over the past two decades, this region emerged as a primary global research hotspot for wildfire, accounting for 15.0% of the total published papers. Other well-known wildfire-prone regions, including Canada, the Amazon, Australia, Mediterranean regions, northern India, and northeast/south China, also received substantial attention, each being the subject of more than one thousand papers (Fig.  2a ). On the other hand, despite accounting for more than 1.3% of global burned area and 4% of global fire emissions, vast Siberia has been largely underrepresented in the current literature 16 . Using the ratio of publication rates to the observed burned areas or fire emissions as indicators, it is evident that the African continent also emerges as a major understudied region (Fig.  3 and Supplementary Fig.  2 ).

figure 3

We classified the pixels into three major categories with imbalance level scales from 1 to 6. The percentile thresholds for pixels were established at the 50th and 90th percentiles for assessing the relative scale of publications, ensuring inclusion of studies spanning from 1980 to 2022. These thresholds corresponded to multi-year averages of 19.5 and 31.6 papers per year, respectively. Regarding burned area analysis, a 90th percentile threshold (equivalent to 554.3 hectares per year) and a frequency threshold (requiring at least 90% coverage across the entire temporal span of the burned area product) were applied. Blank pixels indicate areas where no burned area was observed within AVHRR-LTDR 31 .

At the biome level, our analyses reveal a large disparity in research attention and the actual occurrence and impact of wildfires in grasslands and savannas in Africa and northern Australia (Fig.  3 ). Despite constituting just 8% of the global population, these biomes bear the burden of 72% of the global total burned area. In contrast, 14% and 69% of the global population experience medium (categories 3 and 4) and low (categories 1 and 2) levels of disparity, with only 8% and 18% of the burned area, respectively. Together, areas characterized by a high level of under-representation in wildfire research account for only 2% of the global Gross Domestic Product (GDP, in 2011 purchasing power parity international dollars), a demonstration of low economic capacity for both wildfire management and research. In particular, the high costs of implementing state-of-the-art fire management strategies pose a substantial challenge for these communities with limited economic resources 17 . Furthermore, developing economies in the tropics are also vulnerable to rapid land-use changes 18 , 19 . These changes can trigger a positive feedback loop involving wildfires, land use, and climate, further increasing the likelihood and severity of wildfires. In addition to burned areas, it is important to note that fire emissions may impact or extend to places beyond local ecosystems via atmospheric transportation (Supplementary Fig.  3 ). Especially, public health expenditures associated with exposure to open fires impose a considerable burden on regions marked by large disparities in wildfire research 14 , often due to inadequate study and research. In our analysis, we found that 39% of the population and 24% of socioeconomic development were exposed to high imbalance level, most of which lack of adequate representation, with 77% of total fire-induced carbon emissions. In contrast, 41% of the population and 48% of the GDP were found in regions with low imbalance levels, with only 5% of total wildfire emissions. It should also be mentioned that our analysis may still fall short in fully assessing complexity and scale of imbalances, given the diverse significance and impacts of wildfires across different regions and ecosystems. For example, a large proportion of burned area in Africa comes from routine seasonal burning in savanna and cropland 20 . On the other hand, the high-latitude forests act as essential carbon sink but are concurrently highly susceptible to severe wildfires, which can lead to profound disruptions in their carbon sequestration and release processes 21 .

Building on the conceptual framework of fire research components known as the “fire triangle” model 22 , we further examined changes in research attention to different wildfire topics since 1980s (Fig.  4 ). Overall, “vegetation” attracted the most attention in terms of the amount of literature, not only due to its direct ecological and socioeconomic impacts caused by vegetation fires but also its critical role as “fuel” in the “fire triangle.” With rapidly changing fire weather driven by climate change, the aspect of “climate” in fire research has gradually become a focus of recent wildfire-related research. Regarding ignition sources, our analysis revealed changes in both the quantity and varying levels of attention given to human versus natural causes across different regions (Supplementary Fig.  4 ). Human-caused fires are increasingly dominating wildfire research in South America, Europe, and numerous parts of Asia. Although in regions like the Amazon and Southeast Asia, commodity-driven deforestation remains the primary cause of forest loss 23 , 24 , fires ignited for land clearing purposes to create cropland, pastures, and plantations are common practices 23 . These fires sometimes spiral out of human control and can escalate into numerous active fire spots and extensive burned area 25 , 26 . Among the research with inferred ignition sources, “Vegetation,” “Atmospheric” are ranked as the top topics for both the human and natural-caused fires in most areas. China and India show different preferences in ignition studies, likely due to the lower percentage of burned areas, higher instances of crop residual burning, and heightened population exposure 27 , 28 .

figure 4

The changes of research paradigms were assessed by the relative increase in the number of publications primarily concerning one of the three “fire triangles” (ignition, climate, fuel (vegetation)) 22 . Connection lines denote publications addressing two of the triangle points (aspects). Each different aspect is marked by a different color. The chronological sequence of these research focuses is also illustrated with color changes. The sizes of circles and widths of lines indicate the number of annual publications within a specific decade. The background is depicted by light gray dashed lines, representing the axis of increasing frequency of publications.

In conclusion, the utilization of LLM allowed us to efficiently and quickly monitor research trends across numerous specific research questions and objectives, while also in a transparent and upgradable manner. By deploying an AI-aided approach, we uncovered large disparities among various wildfire research components and quantified their associated levels of imbalance with an extensive database of tens of thousands of peer-reviewed papers. This becomes especially relevant in light of the ongoing rapid global changes that are expected to heighten fire risks 29 , 30 . We found that the pronounced imbalances in many fire-prone regions (e.g., Africa and the Amazon) coincides with the less-developed and less-resilient to increasing fire activities. Such disparity undermines our ability to understand the historical role of fire in local ecosystems and society, and to design mitigation strategies coping with anticipated future global changes.

Query from bibliographic database

To identify and gather as much relevant literature as possible on the topic of “wildfires,” we conducted a thorough search of the Web of Science bibliographic database. The detail of searching and query results are demonstrated as below:

Temporal range: 1900-13th Feb. 2023

Keywords: see details in Supplementary Table  1

A total of 103,720 peer-reviewed papers and conference reports were selected from the query after duplicates removed.

Information extraction

We implemented the gpt-3.5-turbo model by utilizing the OpenAI API, which is identical to the model used in the ChatGPT product and requires no fine-tuning steps. Based on 1,569 independently screened results (with 899 identified as “related to wildfire research” through human classification), we employed a cross-validation approach to test the model’s ability to make “rational” decisions and automatically eliminate items with little relevance to the key topic. Binary comparisons between the model outputs and human-supervised results achieved an average F1 score of 0.85 through bootstrapping with 1000 repetitions, a value comparable to that of previous research based on BERT. Initially, 72,352–80,297 papers were selected as belonging to the category “related to wildfire research” based on various descriptions of prompt instructions. We systematically designed a set of advanced prompts to extract relevant information from the literature, such as the major and minor disciplines, study area, study period, stage of fire (whole fire process/pre-fire/actively burning/post-fire), and other key information summarized from abstracts. To ensure maximum coverage, we carefully tested and refined the prompts. Additionally, we utilized the geoparser capabilities of the model to extract location information from the text and converted it to sets of coordinates describing the maximum boundaries of geographical entities at 1° spatial resolution. Country codes under the standard ISO-3166 were also returned where applicable. The valid geoparsing results within the scope of wildfire research returned a total of 60,488 articles, making up the primary database for subsequent analysis. The workflow, including the geolocation parsing process, is illustrated in Supplementary Fig.  1 .

Prompt examples

In contrast to feature engineering, data cleaning, and other machine-learning processing approaches, LLMs prefer prompting interfaces as the method to interact with them. The quality of prompts directly affects the effectiveness of results. Here, we provide the prompt lines that used in this study to extract information:

Key information extraction

“You are a perfect classifier. List all the following contents and start with a new line. I would like you to (not specific or not applicable marked as N/Az):

(1) determine if the text is related to wildfire research (1 for yes and 0 for no, item name: relaty);

(2) if not (relaty eq 0), marked as NANA_zxy and then stop this dialogue;

(3) if so (relaty eq 1), select at least one topic from the following pool: vegetation/zoological/atmospheric/climate change/ecological/environmental/anthropogenic/hydrological/modeling/remote sensing/site-level observation/soil, list the type of article (research/review/opinion/policy/letters), study area, study period (e.g., 2005, 2001–2010), stage of fire (whole fire process/pre-fire/actively burning /post-fire). List the above information in different lines.

Notice: medical-relevant papers should be marked as NANA_zxy and not be considered wildfire research.”

Geolocation parser

“You are an accurate geoparser with outputs in 1° spatial resolution (per grid). Parse the following text into separate regions. Start with a new paragraph for every single region. Define the maximum outer boundary as MOB ([left-top location, right-bottom location]) and the central point as CP. All the MOB and CP are recorded in the pairs of (latitude, longitude) and integers.

(1) if it is a qualified geographical location:

(a) no smaller than the area of 1 grid, list the parsed name and MOB;

(b) smaller than the area of 1 grid, list the parsed name and CP;

(2) if not, only return ‘None + ‘; no extra notes/raw texts nor explanations should be returned.”

Ignition classifications

“does the given text contain any descriptions of ignition sources?

if does, select from the below type: natural-caused/human-caused/mixed; default as mixed; if not, return N/A; do not return any extra/further words.”

Multisource data

To ensure comparable spatial and temporal coverage with the evidence synthesis map, we applied different sources of supplementary data to depict the comparisons between publication attention and observed wildfire occurrence or emission. The burned area data were from AVHRR-LTDR from 1982 to 2018 at 0.05-degree spatial resolution 31 . This global burned area product was derived from a long-term data record generated from advanced very high-resolution radiometer images. Fire emission data were obtained from GFED4.1 s from 1997 to 2021, with inclusion of contributions from small fires and revised fuel consumption parameterizations optimized 16 . We used the Gridded Population of the World, Version 4 (GPWv4, Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11) as the human population inputs. This product was adjusted to match the 2015 Revision of the United Nation’s World Population Prospects (UN WPP) country totals for the years from 2000 to 2020 at 5-year intervals 32 . Socioeconomic development indicators were obtained from Gridded global datasets for Gross Domestic Product from 1990 to 2015 33 . We resampled and reprojected the results from above datasets to align with the same preferences as the publication patterns.

Data availability

Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data is publicly available from ESA Climate Change Initiative (Fire_cci) ( https://climate.esa.int/en/projects/fire/ ). The global fire emission database (GFED4.1 s) is available from its repository: https://www.globalfiredata.org/data.html . The Gridded Population of the World (GPWv4) is publicly available from NASA’s Data Center, Socioeconomic Data and Applications Center (SEDAC): https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11 . Gridded global datasets for Gross Domestic Product can be accessed through https://doi.org/10.5061/dryad.dk1j0 . The selected literature and processed data to create the figures are available through Zenodo: https://zenodo.org/records/10859331 .

Code availability

The python programming codes used for the generation of ChatGPT outputs of this study are publicly available through the open-access repository https://zenodo.org/records/10811211 .

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Acknowledgements

This study was funded by National Natural Science Foundation of China (42041007, 42171096 & 42301085). We also acknowledge supports by High-performance Computing Platform of Peking University. A.C. acknowledges support from the US Geological Survey (G22AC00431-00). 

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These authors contributed equally: Zhengyang Lin, Anping Chen.

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Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

Zhengyang Lin, Xuhui Wang & Shilong Piao

Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

Anping Chen

CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

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Xuhui Wang & Zhengyang Lin conceived and designed the study. Zhengyang Lin performed the analyses and drew the figures. Zhengyang Lin, Anping Chen and Xuhui Wang wrote the first version of the manuscript, with inputs and revisions from Zhihua Liu and Shilong Piao. All authors contributed to interpretation of the results.

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Lin, Z., Chen, A., Wang, X. et al. Large language models reveal big disparities in current wildfire research. Commun Earth Environ 5 , 168 (2024). https://doi.org/10.1038/s43247-024-01341-7

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  • v.71(2); 2018 Apr

Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

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Object name is kjae-2018-71-2-103f1.jpg

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

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Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

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Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

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Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

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Object name is kjae-2018-71-2-103f5.jpg

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

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Object name is kjae-2018-71-2-103f6.jpg

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

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Non-invasive screening tools may miss heart failure for certain patients and should be used with caution, warns study

by Jessica Stanley, University of Adelaide

Non-invasive screening tools may miss heart failure for certain patients and should be used with caution

Existing non-invasive screening tools may miss heart failure among patients with atrial fibrillation (AF) and should be used with caution, according to recent research.

The warning follows a University of Adelaide study looking at the effectiveness of the internationally recommended screening tools for diagnosing a specific condition called heart failure with preserved ejection fraction in AF patients.

The results of the study, titled "Utility and Validity of the HFA-PEFF and H 2 FPEF Scores in Patients With Symptomatic Atrial Fibrillation," have been published in the journal JACC: Heart Failure .

"This is the first study to look at whether two of these non-invasive scoring systems can be used to detect this condition in AF patients," said first author Dr. Jonathan Ariyaratnam—a Post-Doctoral Fellow with the Center for Heart Rhythm Disorders at the University of Adelaide.

"Diagnosing this type of heart failure in AF patients is particularly challenging because the symptoms of both conditions are very similar and can be overlooked to the detriment of the patient's long-term treatment."

AF causes the heart to beat irregularly and faster than normal, increasing the risk of blood clots in the heart. It can lead to life-threatening conditions such as stroke and heart failure, the latter of which affects 64 million people worldwide.

A previous study by the Center for Heart Rhythm Disorders used an invasive testing protocol to reveal that almost 75% of patients with symptomatic AF had features of heart failure.

A total of 120 AF patients who underwent ablation procedures took part in this latest clinical trial, which was carried out at the Center for Heart Rhythm Disorders between 2020 and 2022.

The first screening tool was the HFA-PEFF score, which uses a diagnostic algorithm based on resting echocardiogram, stress tests and blood markers to estimate the likelihood of a patient having heart failure.

The second screening tool was H 2 FPEF which uses cardiovascular risk factors and resting echocardiographic parameters to predict the probability of the patient having heart failure. Both methods were compared with the current gold standard diagnostic tool, which is more invasive, requiring direct access to the heart using specialized catheters.

"We discovered that the first screening tool was less likely to detect heart failure in younger, obese men with AF. The second method was more sensitive but AF patients who were showing early signs of heart failure were missed," said senior author Dr. Adrian Elliott from the University of Adelaide's Center for Heart Rhythm Disorders and the Royal Adelaide Hospital.

"While the less invasive methods of testing were able to diagnose heart failure in AF patients with moderate accuracy, invasive testing is still needed to confirm results and remains the best diagnostic tool. Based on this, I would recommend these scoring systems be used with caution in AF patients," said senior co-author, the University of Adelaide's Professor Prash Sanders, Director of the Center for Heart Rhythm Disorders and Director of Cardiac Electrophysiology and Pacing at the Royal Adelaide Hospital

Heart failure is a growing issue. Its rapid rise is attributed to the aging population and increasing rates of high blood pressure, diabetes and obesity—all risk factors for the condition.

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IMAGES

  1. The Research Process

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  2. Scientific Research

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  3. The Literature gap and future research-research process in scientific

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  4. How to Give Overview of Existing Research to Your Topic

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  6. What is Research Gap? And How to Identify it

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  1. How to Write a Literature Review

    It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic. There are five key steps to writing a literature review: Search for relevant literature; Evaluate sources; Identify themes, debates, and gaps

  2. Conceptual review papers: revisiting existing research to develop and

    Conceptual review papers can theoretically enrich the field of marketing by reviewing extant knowledge, noting tensions and inconsistencies, identifying important gaps as well as key insights, and proposing agendas for future research. The result of this process is a theoretical contribution that refines, reconceptualizes, or even replaces existing ways of viewing a phenomenon. This paper ...

  3. What is Secondary Research?

    Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research. Example: Secondary research.

  4. Methodological Approaches to Literature Review

    The literature review can serve various functions in the contexts of education and research. It aids in identifying knowledge gaps, informing research methodology, and developing a theoretical framework during the planning stages of a research study or project, as well as reporting of review findings in the context of the existing literature.

  5. What is Research? Definition, Types, Methods and Process

    Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study.

  6. An overview of methodological approaches in systematic reviews

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  7. The importance of crafting a good introduction to scholarly research

    More than simply reporting the existing research, one of the key objectives in any literature review is to summarise and synthesise existing research on the intended topic in order to analyse the significance of the research in question. In this process, diverse ideas can be merged to form fresh new perspectives. Any gaps, limitations, or ...

  8. 13.1 The Research Process: Where to Look for Existing Sources

    Learning Outcomes. By the end of this section, you will be able to: Locate and evaluate primary and secondary research materials, including journal articles and essays, books, scholarly and professionally established and maintained databases or archives, and informal electronic networks and Internet sources.

  9. What Synthesis Methodology Should I Use? A Review and Analysis of

    The current state of research synthesis reflects significant advancements in emerging synthesis studies that integrate diverse data types and sources. New approaches to research synthesis provide a much broader range of review alternatives available to health and social science students and researchers.

  10. How to... find existing research

    Here are a few steps to walk through before you scope out the literature. 1. Develop some questions you want to find answers to. When approaching the literature, you want to have some questions in mind that you are looking for the answers to. This will help you find the most relevant research and help you avoid going down a literature rabbit ...

  11. Guidance on Conducting a Systematic Literature Review

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  12. Existing Research on Academic Careers

    Existing research has placed emphasis on various aspects of what drives academic careers. A first idea is that a professorship reflects, or should reflect, academic performance, that is, the individual's recognised contributions to learning and science, and not draw from "non-academic" (illegitimate) sources of career advancement such as local networks, clan loyalties and inherited prestige.

  13. What Is A Research Gap (With Examples)

    A research gap is an unanswered question or unresolved problem in a field, which reflects a lack of existing research in that space. The four most common types of research gaps are the classic literature gap, the disagreement gap, the contextual gap and the methodological gap.

  14. Research Gap

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  25. Review of Existing Research

    This chapter starts with a critical analysis of the existing surveys into sexting practices by young people and then looks more closely at current qualitative research. A review at the time of writing identified ten such quantitative surveys. 1 Most of these are aimed at identifying the prevalence of sexting among young people and only a small ...

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  27. 79 Words and Phrases for Existing Research

    Existing Research synonyms - 79 Words and Phrases for Existing Research. current investigations. existing studies. ongoing research. existing search. available studies. current investigation. existing investigation. present research.

  28. Introduction to systematic review and meta-analysis

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  29. Non-invasive screening tools may miss heart failure for certain

    Existing non-invasive screening tools may miss heart failure among patients with atrial fibrillation (AF) and should be used with caution, according to recent research.

  30. Update: NIA Funding Line Policy for FY 2024

    Update: NIA Funding Line Policy for FY 2024 On Saturday, March 23, 2024, the President signed into law: H.R. 2882, the "Further Consolidated Appropriations Act, 2024," which provides funding through September 30, 2024, for all departments and agencies of the Federal Government, including the NIH.