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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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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.

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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How to Write the Methods Section of a Scientific Article

What Is the Methods Section of a Research Paper?

The Methods section of a research article includes an explanation of the procedures used to conduct the experiment. For authors of scientific research papers, the objective is to present their findings clearly and concisely and to provide enough information so that the experiment can be duplicated.

Research articles contain very specific sections, usually dictated by either the target journal or specific style guides. For example, in the social and behavioral sciences, the American Psychological Association (APA) style guide is used to gather information on how the manuscript should be arranged . As with most styles, APA’s objectives are to ensure that manuscripts are written with minimum distractions to the reader. Every research article should include a detailed Methods section after the Introduction.

Why is the Methods Section Important?

The Methods section (also referred to as “Materials and Methods”) is important because it provides the reader enough information to judge whether the study is valid and reproducible.

Structure of the Methods Section in a Research Paper

While designing a research study, authors typically decide on the key points that they’re trying to prove or the “ cause-and-effect relationship ” between objects of the study. Very simply, the study is designed to meet the objective. According to APA, a Methods section comprises of the following three subsections: participants, apparatus, and procedure.

How do You Write a Method Section in Biology?

In biological sciences, the Methods section might be more detailed, but the objectives are the same—to present the study clearly and concisely so that it is understandable and can be duplicated.

If animals (including human subjects) were used in the study, authors should ensure to include statements that they were treated according to the protocols outlined to ensure that treatment is as humane as possible.

The Declaration of Helsinki is a set of ethical principles developed by The World Medical Association to provide guidance to scientists and physicians in medical research involving human subjects.

Research conducted at an institution using human participants is overseen by the Institutional Review Board (IRB) with which it is affiliated. IRB is an administrative body whose purpose is to protect the rights and welfare of human subjects during their participation in the study.

Literature Search

Literature searches are performed to gather as much information as relevant from previous studies. They are important for providing evidence on the topic and help validate the research. Most are accomplished using keywords or phrases to search relevant databases. For example, both MEDLINE and PubMed provide information on biomedical literature. Google Scholar, according to APA, is “one of the best sources available to an individual beginning a literature search.” APA also suggests using PsycINFO and refers to it as “the premier database for locating articles in psychological science and related literature.”

Authors must make sure to have a set of keywords (usually taken from the objective statement) to stay focused and to avoid having the search move far from the original objective. Authors will benefit by setting limiting parameters, such as date ranges, and avoiding getting pulled into the trap of using non-valid resources, such as social media, conversations with people in the same discipline, or similar non-valid sources, as references.

Related: Ready with your methods section and looking forward to manuscript submission ? Check these journal selection guidelines now!

What Should be Included in the Methods Section of a Research Paper?

One commonly misused term in research papers is “methodology.” Methodology refers to a branch of the Philosophy of Science which deals with scientific methods, not to the methods themselves, so authors should avoid using it. Here is the list of main subsections that should be included in the Methods section of a research paper ; authors might use subheadings more clearly to describe their research.

Literature search : Authors should cite any sources that helped with their choice of methods. Authors should indicate timeframes of past studies and their particular parameters.
Study participants : Authors should cite the source from where they received any non-human subjects. The number of animals used, the ages, sex, their initial conditions, and how they were housed and cared for, should be listed. In case of human subjects, authors should provide the characteristics, such as geographical location; their age ranges, sex, and medical history (if relevant); and the number of subjects. In case hospital records were used, authors should include the subjects’ basic health information and vital statistics at the beginning of the study. Authors should also state that written informed consent was provided by each subject.
Inclusion/exclusion criteria : Authors should describe their inclusion and exclusion criteria, how they were determined, and how many subjects were eliminated.
Group characteristics (could be combined with “Study participants”) : Authors should describe how the chosen group was divided into subgroups and their characteristics, including the control. Authors should also describe any specific equipment used, such as housing needs and feed (usually for animal studies). If patient records are reviewed and assessed, authors should mention whether the reviewers were blinded to them.
Procedures : Authors should describe their study design. Any necessary preparations (e.g., tissue samples, drugs) and instruments must be explained. Authors should describe how the subjects were “ manipulated to answer the experimental question .” Timeframes should be included to ensure that the procedures are clear (e.g., “Rats were given XX drug for 14 d”). For animals sacrificed, the methods used and the protocols followed should be outlined.
Statistical analyses: The type of data, how they were measured, and which statistical tests were performed, should be described. (Note: This is not the “results” section; any relevant tables and figures should be referenced later.) Specific software used must be cited.

What Should not be Included in Your Methods Section?

Common pitfalls can make the manuscript cumbersome to read or might make the readers question the validity of the research. The University of Southern California provides some guidelines .

Background information that is not helpful must be avoided.
Authors must avoid providing a lot of detail.
Authors should focus more on how their method was used to meet their objective and less on mechanics .
Any obstacles faced and how they were overcome should be described (often in your “Study Limitations”). This will help validate the results.

According to the University of Richmond , authors must avoid including extensive details or an exhaustive list of equipment that have been used as readers could quickly lose attention. These unnecessary details add nothing to validate the research and do not help the reader understand how the objective was satisfied. A well-thought-out Methods section is one of the most important parts of the manuscript. Authors must make a note to always prepare a draft that lists all parts, allow others to review it, and revise it to remove any superfluous information.

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Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

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Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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How to write the methods section of a research paper

Affiliation.

1 Respiratory Care Services, San Francisco General Hospital, NH:GA-2, 1001 Potrero Avenue, San Francisco, CA 94110, USA. [email protected]
PMID: 15447808

The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the experimental design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. Material in each section should be organized by topic from most to least important.

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Published: 11 October 2016

Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research

Stephen J. Gentles 1 , 4 ,
Cathy Charles 1 ,
David B. Nicholas 2 ,
Jenny Ploeg 3 &
K. Ann McKibbon 1

Systematic Reviews volume 5 , Article number: 172 ( 2016 ) Cite this article

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Overviews of methods are potentially useful means to increase clarity and enhance collective understanding of specific methods topics that may be characterized by ambiguity, inconsistency, or a lack of comprehensiveness. This type of review represents a distinct literature synthesis method, although to date, its methodology remains relatively undeveloped despite several aspects that demand unique review procedures. The purpose of this paper is to initiate discussion about what a rigorous systematic approach to reviews of methods, referred to here as systematic methods overviews , might look like by providing tentative suggestions for approaching specific challenges likely to be encountered. The guidance offered here was derived from experience conducting a systematic methods overview on the topic of sampling in qualitative research.

The guidance is organized into several principles that highlight specific objectives for this type of review given the common challenges that must be overcome to achieve them. Optional strategies for achieving each principle are also proposed, along with discussion of how they were successfully implemented in the overview on sampling. We describe seven paired principles and strategies that address the following aspects: delimiting the initial set of publications to consider, searching beyond standard bibliographic databases, searching without the availability of relevant metadata, selecting publications on purposeful conceptual grounds, defining concepts and other information to abstract iteratively, accounting for inconsistent terminology used to describe specific methods topics, and generating rigorous verifiable analytic interpretations. Since a broad aim in systematic methods overviews is to describe and interpret the relevant literature in qualitative terms, we suggest that iterative decision making at various stages of the review process, and a rigorous qualitative approach to analysis are necessary features of this review type.

Conclusions

We believe that the principles and strategies provided here will be useful to anyone choosing to undertake a systematic methods overview. This paper represents an initial effort to promote high quality critical evaluations of the literature regarding problematic methods topics, which have the potential to promote clearer, shared understandings, and accelerate advances in research methods. Further work is warranted to develop more definitive guidance.

Peer Review reports

While reviews of methods are not new, they represent a distinct review type whose methodology remains relatively under-addressed in the literature despite the clear implications for unique review procedures. One of few examples to describe it is a chapter containing reflections of two contributing authors in a book of 21 reviews on methodological topics compiled for the British National Health Service, Health Technology Assessment Program [ 1 ]. Notable is their observation of how the differences between the methods reviews and conventional quantitative systematic reviews, specifically attributable to their varying content and purpose, have implications for defining what qualifies as systematic. While the authors describe general aspects of “systematicity” (including rigorous application of a methodical search, abstraction, and analysis), they also describe a high degree of variation within the category of methods reviews itself and so offer little in the way of concrete guidance. In this paper, we present tentative concrete guidance, in the form of a preliminary set of proposed principles and optional strategies, for a rigorous systematic approach to reviewing and evaluating the literature on quantitative or qualitative methods topics. For purposes of this article, we have used the term systematic methods overview to emphasize the notion of a systematic approach to such reviews.

The conventional focus of rigorous literature reviews (i.e., review types for which systematic methods have been codified, including the various approaches to quantitative systematic reviews [ 2 – 4 ], and the numerous forms of qualitative and mixed methods literature synthesis [ 5 – 10 ]) is to synthesize empirical research findings from multiple studies. By contrast, the focus of overviews of methods, including the systematic approach we advocate, is to synthesize guidance on methods topics. The literature consulted for such reviews may include the methods literature, methods-relevant sections of empirical research reports, or both. Thus, this paper adds to previous work published in this journal—namely, recent preliminary guidance for conducting reviews of theory [ 11 ]—that has extended the application of systematic review methods to novel review types that are concerned with subject matter other than empirical research findings.

Published examples of methods overviews illustrate the varying objectives they can have. One objective is to establish methodological standards for appraisal purposes. For example, reviews of existing quality appraisal standards have been used to propose universal standards for appraising the quality of primary qualitative research [ 12 ] or evaluating qualitative research reports [ 13 ]. A second objective is to survey the methods-relevant sections of empirical research reports to establish current practices on methods use and reporting practices, which Moher and colleagues [ 14 ] recommend as a means for establishing the needs to be addressed in reporting guidelines (see, for example [ 15 , 16 ]). A third objective for a methods review is to offer clarity and enhance collective understanding regarding a specific methods topic that may be characterized by ambiguity, inconsistency, or a lack of comprehensiveness within the available methods literature. An example of this is a overview whose objective was to review the inconsistent definitions of intention-to-treat analysis (the methodologically preferred approach to analyze randomized controlled trial data) that have been offered in the methods literature and propose a solution for improving conceptual clarity [ 17 ]. Such reviews are warranted because students and researchers who must learn or apply research methods typically lack the time to systematically search, retrieve, review, and compare the available literature to develop a thorough and critical sense of the varied approaches regarding certain controversial or ambiguous methods topics.

While systematic methods overviews , as a review type, include both reviews of the methods literature and reviews of methods-relevant sections from empirical study reports, the guidance provided here is primarily applicable to reviews of the methods literature since it was derived from the experience of conducting such a review [ 18 ], described below. To our knowledge, there are no well-developed proposals on how to rigorously conduct such reviews. Such guidance would have the potential to improve the thoroughness and credibility of critical evaluations of the methods literature, which could increase their utility as a tool for generating understandings that advance research methods, both qualitative and quantitative. Our aim in this paper is thus to initiate discussion about what might constitute a rigorous approach to systematic methods overviews. While we hope to promote rigor in the conduct of systematic methods overviews wherever possible, we do not wish to suggest that all methods overviews need be conducted to the same standard. Rather, we believe that the level of rigor may need to be tailored pragmatically to the specific review objectives, which may not always justify the resource requirements of an intensive review process.

The example systematic methods overview on sampling in qualitative research

The principles and strategies we propose in this paper are derived from experience conducting a systematic methods overview on the topic of sampling in qualitative research [ 18 ]. The main objective of that methods overview was to bring clarity and deeper understanding of the prominent concepts related to sampling in qualitative research (purposeful sampling strategies, saturation, etc.). Specifically, we interpreted the available guidance, commenting on areas lacking clarity, consistency, or comprehensiveness (without proposing any recommendations on how to do sampling). This was achieved by a comparative and critical analysis of publications representing the most influential (i.e., highly cited) guidance across several methodological traditions in qualitative research.

The specific methods and procedures for the overview on sampling [ 18 ] from which our proposals are derived were developed both after soliciting initial input from local experts in qualitative research and an expert health librarian (KAM) and through ongoing careful deliberation throughout the review process. To summarize, in that review, we employed a transparent and rigorous approach to search the methods literature, selected publications for inclusion according to a purposeful and iterative process, abstracted textual data using structured abstraction forms, and analyzed (synthesized) the data using a systematic multi-step approach featuring abstraction of text, summary of information in matrices, and analytic comparisons.

For this article, we reflected on both the problems and challenges encountered at different stages of the review and our means for selecting justifiable procedures to deal with them. Several principles were then derived by considering the generic nature of these problems, while the generalizable aspects of the procedures used to address them formed the basis of optional strategies. Further details of the specific methods and procedures used in the overview on qualitative sampling are provided below to illustrate both the types of objectives and challenges that reviewers will likely need to consider and our approach to implementing each of the principles and strategies.

Organization of the guidance into principles and strategies

For the purposes of this article, principles are general statements outlining what we propose are important aims or considerations within a particular review process, given the unique objectives or challenges to be overcome with this type of review. These statements follow the general format, “considering the objective or challenge of X, we propose Y to be an important aim or consideration.” Strategies are optional and flexible approaches for implementing the previous principle outlined. Thus, generic challenges give rise to principles, which in turn give rise to strategies.

We organize the principles and strategies below into three sections corresponding to processes characteristic of most systematic literature synthesis approaches: literature identification and selection ; data abstraction from the publications selected for inclusion; and analysis , including critical appraisal and synthesis of the abstracted data. Within each section, we also describe the specific methodological decisions and procedures used in the overview on sampling in qualitative research [ 18 ] to illustrate how the principles and strategies for each review process were applied and implemented in a specific case. We expect this guidance and accompanying illustrations will be useful for anyone considering engaging in a methods overview, particularly those who may be familiar with conventional systematic review methods but may not yet appreciate some of the challenges specific to reviewing the methods literature.

Results and discussion

Literature identification and selection.

The identification and selection process includes search and retrieval of publications and the development and application of inclusion and exclusion criteria to select the publications that will be abstracted and analyzed in the final review. Literature identification and selection for overviews of the methods literature is challenging and potentially more resource-intensive than for most reviews of empirical research. This is true for several reasons that we describe below, alongside discussion of the potential solutions. Additionally, we suggest in this section how the selection procedures can be chosen to match the specific analytic approach used in methods overviews.

Delimiting a manageable set of publications

One aspect of methods overviews that can make identification and selection challenging is the fact that the universe of literature containing potentially relevant information regarding most methods-related topics is expansive and often unmanageably so. Reviewers are faced with two large categories of literature: the methods literature , where the possible publication types include journal articles, books, and book chapters; and the methods-relevant sections of empirical study reports , where the possible publication types include journal articles, monographs, books, theses, and conference proceedings. In our systematic overview of sampling in qualitative research, exhaustively searching (including retrieval and first-pass screening) all publication types across both categories of literature for information on a single methods-related topic was too burdensome to be feasible. The following proposed principle follows from the need to delimit a manageable set of literature for the review.

Principle #1:

Considering the broad universe of potentially relevant literature, we propose that an important objective early in the identification and selection stage is to delimit a manageable set of methods-relevant publications in accordance with the objectives of the methods overview.

Strategy #1:

To limit the set of methods-relevant publications that must be managed in the selection process, reviewers have the option to initially review only the methods literature, and exclude the methods-relevant sections of empirical study reports, provided this aligns with the review’s particular objectives.

We propose that reviewers are justified in choosing to select only the methods literature when the objective is to map out the range of recognized concepts relevant to a methods topic, to summarize the most authoritative or influential definitions or meanings for methods-related concepts, or to demonstrate a problematic lack of clarity regarding a widely established methods-related concept and potentially make recommendations for a preferred approach to the methods topic in question. For example, in the case of the methods overview on sampling [ 18 ], the primary aim was to define areas lacking in clarity for multiple widely established sampling-related topics. In the review on intention-to-treat in the context of missing outcome data [ 17 ], the authors identified a lack of clarity based on multiple inconsistent definitions in the literature and went on to recommend separating the issue of how to handle missing outcome data from the issue of whether an intention-to-treat analysis can be claimed.

In contrast to strategy #1, it may be appropriate to select the methods-relevant sections of empirical study reports when the objective is to illustrate how a methods concept is operationalized in research practice or reported by authors. For example, one could review all the publications in 2 years’ worth of issues of five high-impact field-related journals to answer questions about how researchers describe implementing a particular method or approach, or to quantify how consistently they define or report using it. Such reviews are often used to highlight gaps in the reporting practices regarding specific methods, which may be used to justify items to address in reporting guidelines (for example, [ 14 – 16 ]).

It is worth recognizing that other authors have advocated broader positions regarding the scope of literature to be considered in a review, expanding on our perspective. Suri [ 10 ] (who, like us, emphasizes how different sampling strategies are suitable for different literature synthesis objectives) has, for example, described a two-stage literature sampling procedure (pp. 96–97). First, reviewers use an initial approach to conduct a broad overview of the field—for reviews of methods topics, this would entail an initial review of the research methods literature. This is followed by a second more focused stage in which practical examples are purposefully selected—for methods reviews, this would involve sampling the empirical literature to illustrate key themes and variations. While this approach is seductive in its capacity to generate more in depth and interpretive analytic findings, some reviewers may consider it too resource-intensive to include the second step no matter how selective the purposeful sampling. In the overview on sampling where we stopped after the first stage [ 18 ], we discussed our selective focus on the methods literature as a limitation that left opportunities for further analysis of the literature. We explicitly recommended, for example, that theoretical sampling was a topic for which a future review of the methods sections of empirical reports was justified to answer specific questions identified in the primary review.

Ultimately, reviewers must make pragmatic decisions that balance resource considerations, combined with informed predictions about the depth and complexity of literature available on their topic, with the stated objectives of their review. The remaining principles and strategies apply primarily to overviews that include the methods literature, although some aspects may be relevant to reviews that include empirical study reports.

Searching beyond standard bibliographic databases

An important reality affecting identification and selection in overviews of the methods literature is the increased likelihood for relevant publications to be located in sources other than journal articles (which is usually not the case for overviews of empirical research, where journal articles generally represent the primary publication type). In the overview on sampling [ 18 ], out of 41 full-text publications retrieved and reviewed, only 4 were journal articles, while 37 were books or book chapters. Since many books and book chapters did not exist electronically, their full text had to be physically retrieved in hardcopy, while 11 publications were retrievable only through interlibrary loan or purchase request. The tasks associated with such retrieval are substantially more time-consuming than electronic retrieval. Since a substantial proportion of methods-related guidance may be located in publication types that are less comprehensively indexed in standard bibliographic databases, identification and retrieval thus become complicated processes.

Principle #2:

Considering that important sources of methods guidance can be located in non-journal publication types (e.g., books, book chapters) that tend to be poorly indexed in standard bibliographic databases, it is important to consider alternative search methods for identifying relevant publications to be further screened for inclusion.

Strategy #2:

To identify books, book chapters, and other non-journal publication types not thoroughly indexed in standard bibliographic databases, reviewers may choose to consult one or more of the following less standard sources: Google Scholar, publisher web sites, or expert opinion.

In the case of the overview on sampling in qualitative research [ 18 ], Google Scholar had two advantages over other standard bibliographic databases: it indexes and returns records of books and book chapters likely to contain guidance on qualitative research methods topics; and it has been validated as providing higher citation counts than ISI Web of Science (a producer of numerous bibliographic databases accessible through institutional subscription) for several non-biomedical disciplines including the social sciences where qualitative research methods are prominently used [ 19 – 21 ]. While we identified numerous useful publications by consulting experts, the author publication lists generated through Google Scholar searches were uniquely useful to identify more recent editions of methods books identified by experts.

Searching without relevant metadata

Determining what publications to select for inclusion in the overview on sampling [ 18 ] could only rarely be accomplished by reviewing the publication’s metadata. This was because for the many books and other non-journal type publications we identified as possibly relevant, the potential content of interest would be located in only a subsection of the publication. In this common scenario for reviews of the methods literature (as opposed to methods overviews that include empirical study reports), reviewers will often be unable to employ standard title, abstract, and keyword database searching or screening as a means for selecting publications.

Principle #3:

Considering that the presence of information about the topic of interest may not be indicated in the metadata for books and similar publication types, it is important to consider other means of identifying potentially useful publications for further screening.

Strategy #3:

One approach to identifying potentially useful books and similar publication types is to consider what classes of such publications (e.g., all methods manuals for a certain research approach) are likely to contain relevant content, then identify, retrieve, and review the full text of corresponding publications to determine whether they contain information on the topic of interest.

In the example of the overview on sampling in qualitative research [ 18 ], the topic of interest (sampling) was one of numerous topics covered in the general qualitative research methods manuals. Consequently, examples from this class of publications first had to be identified for retrieval according to non-keyword-dependent criteria. Thus, all methods manuals within the three research traditions reviewed (grounded theory, phenomenology, and case study) that might contain discussion of sampling were sought through Google Scholar and expert opinion, their full text obtained, and hand-searched for relevant content to determine eligibility. We used tables of contents and index sections of books to aid this hand searching.

Purposefully selecting literature on conceptual grounds

A final consideration in methods overviews relates to the type of analysis used to generate the review findings. Unlike quantitative systematic reviews where reviewers aim for accurate or unbiased quantitative estimates—something that requires identifying and selecting the literature exhaustively to obtain all relevant data available (i.e., a complete sample)—in methods overviews, reviewers must describe and interpret the relevant literature in qualitative terms to achieve review objectives. In other words, the aim in methods overviews is to seek coverage of the qualitative concepts relevant to the methods topic at hand. For example, in the overview of sampling in qualitative research [ 18 ], achieving review objectives entailed providing conceptual coverage of eight sampling-related topics that emerged as key domains. The following principle recognizes that literature sampling should therefore support generating qualitative conceptual data as the input to analysis.

Principle #4:

Since the analytic findings of a systematic methods overview are generated through qualitative description and interpretation of the literature on a specified topic, selection of the literature should be guided by a purposeful strategy designed to achieve adequate conceptual coverage (i.e., representing an appropriate degree of variation in relevant ideas) of the topic according to objectives of the review.

Strategy #4:

One strategy for choosing the purposeful approach to use in selecting the literature according to the review objectives is to consider whether those objectives imply exploring concepts either at a broad overview level, in which case combining maximum variation selection with a strategy that limits yield (e.g., critical case, politically important, or sampling for influence—described below) may be appropriate; or in depth, in which case purposeful approaches aimed at revealing innovative cases will likely be necessary.

In the methods overview on sampling, the implied scope was broad since we set out to review publications on sampling across three divergent qualitative research traditions—grounded theory, phenomenology, and case study—to facilitate making informative conceptual comparisons. Such an approach would be analogous to maximum variation sampling.

At the same time, the purpose of that review was to critically interrogate the clarity, consistency, and comprehensiveness of literature from these traditions that was “most likely to have widely influenced students’ and researchers’ ideas about sampling” (p. 1774) [ 18 ]. In other words, we explicitly set out to review and critique the most established and influential (and therefore dominant) literature, since this represents a common basis of knowledge among students and researchers seeking understanding or practical guidance on sampling in qualitative research. To achieve this objective, we purposefully sampled publications according to the criterion of influence , which we operationalized as how often an author or publication has been referenced in print or informal discourse. This second sampling approach also limited the literature we needed to consider within our broad scope review to a manageable amount.

To operationalize this strategy of sampling for influence , we sought to identify both the most influential authors within a qualitative research tradition (all of whose citations were subsequently screened) and the most influential publications on the topic of interest by non-influential authors. This involved a flexible approach that combined multiple indicators of influence to avoid the dilemma that any single indicator might provide inadequate coverage. These indicators included bibliometric data (h-index for author influence [ 22 ]; number of cites for publication influence), expert opinion, and cross-references in the literature (i.e., snowball sampling). As a final selection criterion, a publication was included only if it made an original contribution in terms of novel guidance regarding sampling or a related concept; thus, purely secondary sources were excluded. Publish or Perish software (Anne-Wil Harzing; available at http://www.harzing.com/resources/publish-or-perish ) was used to generate bibliometric data via the Google Scholar database. Figure 1 illustrates how identification and selection in the methods overview on sampling was a multi-faceted and iterative process. The authors selected as influential, and the publications selected for inclusion or exclusion are listed in Additional file 1 (Matrices 1, 2a, 2b).

Literature identification and selection process used in the methods overview on sampling [ 18 ]

In summary, the strategies of seeking maximum variation and sampling for influence were employed in the sampling overview to meet the specific review objectives described. Reviewers will need to consider the full range of purposeful literature sampling approaches at their disposal in deciding what best matches the specific aims of their own reviews. Suri [ 10 ] has recently retooled Patton’s well-known typology of purposeful sampling strategies (originally intended for primary research) for application to literature synthesis, providing a useful resource in this respect.

Data abstraction

The purpose of data abstraction in rigorous literature reviews is to locate and record all data relevant to the topic of interest from the full text of included publications, making them available for subsequent analysis. Conventionally, a data abstraction form—consisting of numerous distinct conceptually defined fields to which corresponding information from the source publication is recorded—is developed and employed. There are several challenges, however, to the processes of developing the abstraction form and abstracting the data itself when conducting methods overviews, which we address here. Some of these problems and their solutions may be familiar to those who have conducted qualitative literature syntheses, which are similarly conceptual.

Iteratively defining conceptual information to abstract

In the overview on sampling [ 18 ], while we surveyed multiple sources beforehand to develop a list of concepts relevant for abstraction (e.g., purposeful sampling strategies, saturation, sample size), there was no way for us to anticipate some concepts prior to encountering them in the review process. Indeed, in many cases, reviewers are unable to determine the complete set of methods-related concepts that will be the focus of the final review a priori without having systematically reviewed the publications to be included. Thus, defining what information to abstract beforehand may not be feasible.

Principle #5:

Considering the potential impracticality of defining a complete set of relevant methods-related concepts from a body of literature one has not yet systematically read, selecting and defining fields for data abstraction must often be undertaken iteratively. Thus, concepts to be abstracted can be expected to grow and change as data abstraction proceeds.

Strategy #5:

Reviewers can develop an initial form or set of concepts for abstraction purposes according to standard methods (e.g., incorporating expert feedback, pilot testing) and remain attentive to the need to iteratively revise it as concepts are added or modified during the review. Reviewers should document revisions and return to re-abstract data from previously abstracted publications as the new data requirements are determined.

In the sampling overview [ 18 ], we developed and maintained the abstraction form in Microsoft Word. We derived the initial set of abstraction fields from our own knowledge of relevant sampling-related concepts, consultation with local experts, and reviewing a pilot sample of publications. Since the publications in this review included a large proportion of books, the abstraction process often began by flagging the broad sections within a publication containing topic-relevant information for detailed review to identify text to abstract. When reviewing flagged text, the reviewer occasionally encountered an unanticipated concept significant enough to warrant being added as a new field to the abstraction form. For example, a field was added to capture how authors described the timing of sampling decisions, whether before (a priori) or after (ongoing) starting data collection, or whether this was unclear. In these cases, we systematically documented the modification to the form and returned to previously abstracted publications to abstract any information that might be relevant to the new field.

The logic of this strategy is analogous to the logic used in a form of research synthesis called best fit framework synthesis (BFFS) [ 23 – 25 ]. In that method, reviewers initially code evidence using an a priori framework they have selected. When evidence cannot be accommodated by the selected framework, reviewers then develop new themes or concepts from which they construct a new expanded framework. Both the strategy proposed and the BFFS approach to research synthesis are notable for their rigorous and transparent means to adapt a final set of concepts to the content under review.

Accounting for inconsistent terminology

An important complication affecting the abstraction process in methods overviews is that the language used by authors to describe methods-related concepts can easily vary across publications. For example, authors from different qualitative research traditions often use different terms for similar methods-related concepts. Furthermore, as we found in the sampling overview [ 18 ], there may be cases where no identifiable term, phrase, or label for a methods-related concept is used at all, and a description of it is given instead. This can make searching the text for relevant concepts based on keywords unreliable.

Principle #6:

Since accepted terms may not be used consistently to refer to methods concepts, it is necessary to rely on the definitions for concepts, rather than keywords, to identify relevant information in the publication to abstract.

Strategy #6:

An effective means to systematically identify relevant information is to develop and iteratively adjust written definitions for key concepts (corresponding to abstraction fields) that are consistent with and as inclusive of as much of the literature reviewed as possible. Reviewers then seek information that matches these definitions (rather than keywords) when scanning a publication for relevant data to abstract.

In the abstraction process for the sampling overview [ 18 ], we noted the several concepts of interest to the review for which abstraction by keyword was particularly problematic due to inconsistent terminology across publications: sampling , purposeful sampling , sampling strategy , and saturation (for examples, see Additional file 1 , Matrices 3a, 3b, 4). We iteratively developed definitions for these concepts by abstracting text from publications that either provided an explicit definition or from which an implicit definition could be derived, which was recorded in fields dedicated to the concept’s definition. Using a method of constant comparison, we used text from definition fields to inform and modify a centrally maintained definition of the corresponding concept to optimize its fit and inclusiveness with the literature reviewed. Table 1 shows, as an example, the final definition constructed in this way for one of the central concepts of the review, qualitative sampling .

We applied iteratively developed definitions when making decisions about what specific text to abstract for an existing field, which allowed us to abstract concept-relevant data even if no recognized keyword was used. For example, this was the case for the sampling-related concept, saturation , where the relevant text available for abstraction in one publication [ 26 ]—“to continue to collect data until nothing new was being observed or recorded, no matter how long that takes”—was not accompanied by any term or label whatsoever.

This comparative analytic strategy (and our approach to analysis more broadly as described in strategy #7, below) is analogous to the process of reciprocal translation —a technique first introduced for meta-ethnography by Noblit and Hare [ 27 ] that has since been recognized as a common element in a variety of qualitative metasynthesis approaches [ 28 ]. Reciprocal translation, taken broadly, involves making sense of a study’s findings in terms of the findings of the other studies included in the review. In practice, it has been operationalized in different ways. Melendez-Torres and colleagues developed a typology from their review of the metasynthesis literature, describing four overlapping categories of specific operations undertaken in reciprocal translation: visual representation, key paper integration, data reduction and thematic extraction, and line-by-line coding [ 28 ]. The approaches suggested in both strategies #6 and #7, with their emphasis on constant comparison, appear to fall within the line-by-line coding category.

Generating credible and verifiable analytic interpretations

The analysis in a systematic methods overview must support its more general objective, which we suggested above is often to offer clarity and enhance collective understanding regarding a chosen methods topic. In our experience, this involves describing and interpreting the relevant literature in qualitative terms. Furthermore, any interpretative analysis required may entail reaching different levels of abstraction, depending on the more specific objectives of the review. For example, in the overview on sampling [ 18 ], we aimed to produce a comparative analysis of how multiple sampling-related topics were treated differently within and among different qualitative research traditions. To promote credibility of the review, however, not only should one seek a qualitative analytic approach that facilitates reaching varying levels of abstraction but that approach must also ensure that abstract interpretations are supported and justified by the source data and not solely the product of the analyst’s speculative thinking.

Principle #7:

Considering the qualitative nature of the analysis required in systematic methods overviews, it is important to select an analytic method whose interpretations can be verified as being consistent with the literature selected, regardless of the level of abstraction reached.

Strategy #7:

We suggest employing the constant comparative method of analysis [ 29 ] because it supports developing and verifying analytic links to the source data throughout progressively interpretive or abstract levels. In applying this approach, we advise a rigorous approach, documenting how supportive quotes or references to the original texts are carried forward in the successive steps of analysis to allow for easy verification.

The analytic approach used in the methods overview on sampling [ 18 ] comprised four explicit steps, progressing in level of abstraction—data abstraction, matrices, narrative summaries, and final analytic conclusions (Fig. 2 ). While we have positioned data abstraction as the second stage of the generic review process (prior to Analysis), above, we also considered it as an initial step of analysis in the sampling overview for several reasons. First, it involved a process of constant comparisons and iterative decision-making about the fields to add or define during development and modification of the abstraction form, through which we established the range of concepts to be addressed in the review. At the same time, abstraction involved continuous analytic decisions about what textual quotes (ranging in size from short phrases to numerous paragraphs) to record in the fields thus created. This constant comparative process was analogous to open coding in which textual data from publications was compared to conceptual fields (equivalent to codes) or to other instances of data previously abstracted when constructing definitions to optimize their fit with the overall literature as described in strategy #6. Finally, in the data abstraction step, we also recorded our first interpretive thoughts in dedicated fields, providing initial material for the more abstract analytic steps.

Summary of progressive steps of analysis used in the methods overview on sampling [ 18 ]

In the second step of the analysis, we constructed topic-specific matrices , or tables, by copying relevant quotes from abstraction forms into the appropriate cells of matrices (for the complete set of analytic matrices developed in the sampling review, see Additional file 1 (matrices 3 to 10)). Each matrix ranged from one to five pages; row headings, nested three-deep, identified the methodological tradition, author, and publication, respectively; and column headings identified the concepts, which corresponded to abstraction fields. Matrices thus allowed us to make further comparisons across methodological traditions, and between authors within a tradition. In the third step of analysis, we recorded our comparative observations as narrative summaries , in which we used illustrative quotes more sparingly. In the final step, we developed analytic conclusions based on the narrative summaries about the sampling-related concepts within each methodological tradition for which clarity, consistency, or comprehensiveness of the available guidance appeared to be lacking. Higher levels of analysis thus built logically from the lower levels, enabling us to easily verify analytic conclusions by tracing the support for claims by comparing the original text of publications reviewed.

Integrative versus interpretive methods overviews

The analytic product of systematic methods overviews is comparable to qualitative evidence syntheses, since both involve describing and interpreting the relevant literature in qualitative terms. Most qualitative synthesis approaches strive to produce new conceptual understandings that vary in level of interpretation. Dixon-Woods and colleagues [ 30 ] elaborate on a useful distinction, originating from Noblit and Hare [ 27 ], between integrative and interpretive reviews. Integrative reviews focus on summarizing available primary data and involve using largely secure and well defined concepts to do so; definitions are used from an early stage to specify categories for abstraction (or coding) of data, which in turn supports their aggregation; they do not seek as their primary focus to develop or specify new concepts, although they may achieve some theoretical or interpretive functions. For interpretive reviews, meanwhile, the main focus is to develop new concepts and theories that integrate them, with the implication that the concepts developed become fully defined towards the end of the analysis. These two forms are not completely distinct, and “every integrative synthesis will include elements of interpretation, and every interpretive synthesis will include elements of aggregation of data” [ 30 ].

The example methods overview on sampling [ 18 ] could be classified as predominantly integrative because its primary goal was to aggregate influential authors’ ideas on sampling-related concepts; there were also, however, elements of interpretive synthesis since it aimed to develop new ideas about where clarity in guidance on certain sampling-related topics is lacking, and definitions for some concepts were flexible and not fixed until late in the review. We suggest that most systematic methods overviews will be classifiable as predominantly integrative (aggregative). Nevertheless, more highly interpretive methods overviews are also quite possible—for example, when the review objective is to provide a highly critical analysis for the purpose of generating new methodological guidance. In such cases, reviewers may need to sample more deeply (see strategy #4), specifically by selecting empirical research reports (i.e., to go beyond dominant or influential ideas in the methods literature) that are likely to feature innovations or instructive lessons in employing a given method.

In this paper, we have outlined tentative guidance in the form of seven principles and strategies on how to conduct systematic methods overviews, a review type in which methods-relevant literature is systematically analyzed with the aim of offering clarity and enhancing collective understanding regarding a specific methods topic. Our proposals include strategies for delimiting the set of publications to consider, searching beyond standard bibliographic databases, searching without the availability of relevant metadata, selecting publications on purposeful conceptual grounds, defining concepts and other information to abstract iteratively, accounting for inconsistent terminology, and generating credible and verifiable analytic interpretations. We hope the suggestions proposed will be useful to others undertaking reviews on methods topics in future.

As far as we are aware, this is the first published source of concrete guidance for conducting this type of review. It is important to note that our primary objective was to initiate methodological discussion by stimulating reflection on what rigorous methods for this type of review should look like, leaving the development of more complete guidance to future work. While derived from the experience of reviewing a single qualitative methods topic, we believe the principles and strategies provided are generalizable to overviews of both qualitative and quantitative methods topics alike. However, it is expected that additional challenges and insights for conducting such reviews have yet to be defined. Thus, we propose that next steps for developing more definitive guidance should involve an attempt to collect and integrate other reviewers’ perspectives and experiences in conducting systematic methods overviews on a broad range of qualitative and quantitative methods topics. Formalized guidance and standards would improve the quality of future methods overviews, something we believe has important implications for advancing qualitative and quantitative methodology. When undertaken to a high standard, rigorous critical evaluations of the available methods guidance have significant potential to make implicit controversies explicit, and improve the clarity and precision of our understandings of problematic qualitative or quantitative methods issues.

A review process central to most types of rigorous reviews of empirical studies, which we did not explicitly address in a separate review step above, is quality appraisal . The reason we have not treated this as a separate step stems from the different objectives of the primary publications included in overviews of the methods literature (i.e., providing methodological guidance) compared to the primary publications included in the other established review types (i.e., reporting findings from single empirical studies). This is not to say that appraising quality of the methods literature is not an important concern for systematic methods overviews. Rather, appraisal is much more integral to (and difficult to separate from) the analysis step, in which we advocate appraising clarity, consistency, and comprehensiveness—the quality appraisal criteria that we suggest are appropriate for the methods literature. As a second important difference regarding appraisal, we currently advocate appraising the aforementioned aspects at the level of the literature in aggregate rather than at the level of individual publications. One reason for this is that methods guidance from individual publications generally builds on previous literature, and thus we feel that ahistorical judgments about comprehensiveness of single publications lack relevance and utility. Additionally, while different methods authors may express themselves less clearly than others, their guidance can nonetheless be highly influential and useful, and should therefore not be downgraded or ignored based on considerations of clarity—which raises questions about the alternative uses that quality appraisals of individual publications might have. Finally, legitimate variability in the perspectives that methods authors wish to emphasize, and the levels of generality at which they write about methods, makes critiquing individual publications based on the criterion of clarity a complex and potentially problematic endeavor that is beyond the scope of this paper to address. By appraising the current state of the literature at a holistic level, reviewers stand to identify important gaps in understanding that represent valuable opportunities for further methodological development.

To summarize, the principles and strategies provided here may be useful to those seeking to undertake their own systematic methods overview. Additional work is needed, however, to establish guidance that is comprehensive by comparing the experiences from conducting a variety of methods overviews on a range of methods topics. Efforts that further advance standards for systematic methods overviews have the potential to promote high-quality critical evaluations that produce conceptually clear and unified understandings of problematic methods topics, thereby accelerating the advance of research methodology.

Hutton JL, Ashcroft R. What does “systematic” mean for reviews of methods? In: Black N, Brazier J, Fitzpatrick R, Reeves B, editors. Health services research methods: a guide to best practice. London: BMJ Publishing Group; 1998. p. 249–54.

Google Scholar

Cochrane handbook for systematic reviews of interventions. In. Edited by Higgins JPT, Green S, Version 5.1.0 edn: The Cochrane Collaboration; 2011.

Centre for Reviews and Dissemination: Systematic reviews: CRD’s guidance for undertaking reviews in health care . York: Centre for Reviews and Dissemination; 2009.

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700–0.

Barnett-Page E, Thomas J. Methods for the synthesis of qualitative research: a critical review. BMC Med Res Methodol. 2009;9(1):59.

Article PubMed PubMed Central Google Scholar

Kastner M, Tricco AC, Soobiah C, Lillie E, Perrier L, Horsley T, Welch V, Cogo E, Antony J, Straus SE. What is the most appropriate knowledge synthesis method to conduct a review? Protocol for a scoping review. BMC Med Res Methodol. 2012;12(1):1–1.

Article Google Scholar

Booth A, Noyes J, Flemming K, Gerhardus A. Guidance on choosing qualitative evidence synthesis methods for use in health technology assessments of complex interventions. In: Integrate-HTA. 2016.

Booth A, Sutton A, Papaioannou D. Systematic approaches to successful literature review. 2nd ed. London: Sage; 2016.

Hannes K, Lockwood C. Synthesizing qualitative research: choosing the right approach. Chichester: Wiley-Blackwell; 2012.

Suri H. Towards methodologically inclusive research syntheses: expanding possibilities. New York: Routledge; 2014.

Campbell M, Egan M, Lorenc T, Bond L, Popham F, Fenton C, Benzeval M. Considering methodological options for reviews of theory: illustrated by a review of theories linking income and health. Syst Rev. 2014;3(1):1–11.

Cohen DJ, Crabtree BF. Evaluative criteria for qualitative research in health care: controversies and recommendations. Ann Fam Med. 2008;6(4):331–9.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reportingqualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Article PubMed Google Scholar

Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLoS Med. 2010;7(2):e1000217.

Moher D, Tetzlaff J, Tricco AC, Sampson M, Altman DG. Epidemiology and reporting characteristics of systematic reviews. PLoS Med. 2007;4(3):e78.

Chan AW, Altman DG. Epidemiology and reporting of randomised trials published in PubMed journals. Lancet. 2005;365(9465):1159–62.

Alshurafa M, Briel M, Akl EA, Haines T, Moayyedi P, Gentles SJ, Rios L, Tran C, Bhatnagar N, Lamontagne F, et al. Inconsistent definitions for intention-to-treat in relation to missing outcome data: systematic review of the methods literature. PLoS One. 2012;7(11):e49163.

Article CAS PubMed PubMed Central Google Scholar

Gentles SJ, Charles C, Ploeg J, McKibbon KA. Sampling in qualitative research: insights from an overview of the methods literature. Qual Rep. 2015;20(11):1772–89.

Harzing A-W, Alakangas S. Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison. Scientometrics. 2016;106(2):787–804.

Harzing A-WK, van der Wal R. Google Scholar as a new source for citation analysis. Ethics Sci Environ Polit. 2008;8(1):61–73.

Kousha K, Thelwall M. Google Scholar citations and Google Web/URL citations: a multi‐discipline exploratory analysis. J Assoc Inf Sci Technol. 2007;58(7):1055–65.

Hirsch JE. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci U S A. 2005;102(46):16569–72.

Booth A, Carroll C. How to build up the actionable knowledge base: the role of ‘best fit’ framework synthesis for studies of improvement in healthcare. BMJ Quality Safety. 2015;24(11):700–8.

Carroll C, Booth A, Leaviss J, Rick J. “Best fit” framework synthesis: refining the method. BMC Med Res Methodol. 2013;13(1):37.

Carroll C, Booth A, Cooper K. A worked example of “best fit” framework synthesis: a systematic review of views concerning the taking of some potential chemopreventive agents. BMC Med Res Methodol. 2011;11(1):29.

Cohen MZ, Kahn DL, Steeves DL. Hermeneutic phenomenological research: a practical guide for nurse researchers. Thousand Oaks: Sage; 2000.

Noblit GW, Hare RD. Meta-ethnography: synthesizing qualitative studies. Newbury Park: Sage; 1988.

Book Google Scholar

Melendez-Torres GJ, Grant S, Bonell C. A systematic review and critical appraisal of qualitative metasynthetic practice in public health to develop a taxonomy of operations of reciprocal translation. Res Synthesis Methods. 2015;6(4):357–71.

Article CAS Google Scholar

Glaser BG, Strauss A. The discovery of grounded theory. Chicago: Aldine; 1967.

Dixon-Woods M, Agarwal S, Young B, Jones D, Sutton A. Integrative approaches to qualitative and quantitative evidence. In: UK National Health Service. 2004. p. 1–44.

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The systematic methods overview used as a worked example in this article (Gentles SJ, Charles C, Ploeg J, McKibbon KA: Sampling in qualitative research: insights from an overview of the methods literature. The Qual Rep 2015, 20(11):1772-1789) is available from http://nsuworks.nova.edu/tqr/vol20/iss11/5 .

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SJG wrote the first draft of this article, with CC contributing to drafting. All authors contributed to revising the manuscript. All authors except CC (deceased) approved the final draft. SJG, CC, KAB, and JP were involved in developing methods for the systematic methods overview on sampling.

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Stephen J. Gentles, Cathy Charles & K. Ann McKibbon

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David B. Nicholas

School of Nursing, McMaster University, Hamilton, Ontario, Canada

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Gentles, S.J., Charles, C., Nicholas, D.B. et al. Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research. Syst Rev 5 , 172 (2016). https://doi.org/10.1186/s13643-016-0343-0

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Received : 06 June 2016

Accepted : 14 September 2016

Published : 11 October 2016

DOI : https://doi.org/10.1186/s13643-016-0343-0

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access

Nicholas C. Coombs 1 ,
Duncan G. Campbell 2 &
James Caringi 1

BMC Health Services Research volume 22 , Article number: 438 ( 2022 ) Cite this article

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Ensuring access to healthcare is a complex, multi-dimensional health challenge. Since the inception of the coronavirus pandemic, this challenge is more pressing. Some dimensions of access are difficult to quantify, namely characteristics that influence healthcare services to be both acceptable and appropriate. These link to a patient’s acceptance of services that they are to receive and ensuring appropriate fit between services and a patient’s specific healthcare needs. These dimensions of access are particularly evident in rural health systems where additional structural barriers make accessing healthcare more difficult. Thus, it is important to examine healthcare access barriers in rural-specific areas to understand their origin and implications for resolution.

We used qualitative methods and a convenience sample of healthcare providers who currently practice in the rural US state of Montana. Our sample included 12 healthcare providers from diverse training backgrounds and specialties. All were decision-makers in the development or revision of patients’ treatment plans. Semi-structured interviews and content analysis were used to explore barriers–appropriateness and acceptability–to healthcare access in their patient populations. Our analysis was both deductive and inductive and focused on three analytic domains: cultural considerations, patient-provider communication, and provider-provider communication. Member checks ensured credibility and trustworthiness of our findings.

Five key themes emerged from analysis: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US.

Conclusions

Inadequate access to healthcare is an issue in the US, particularly in rural areas. Rural healthcare consumers compose a hard-to-reach patient population. Too few providers exist to meet population health needs, and fragmented communication impairs rural health systems’ ability to function. These issues exacerbate the difficulty of ensuring acceptable and appropriate delivery of healthcare services, which compound all other barriers to healthcare access for rural residents. Each dimension of access must be monitored to improve patient experiences and outcomes for rural Americans.

Peer Review reports

Unequal access to healthcare services is an important element of health disparities in the United States [ 1 ], and there remains much about access that is not fully understood. The lack of understanding is attributable, in part, to the lack of uniformity in how access is defined and evaluated, and the extent to which access is often oversimplified in research [ 2 ]. Subsequently, attempts to address population-level barriers to healthcare access are insufficient, and access remains an unresolved, complex health challenge [ 3 , 4 , 5 ]. This paper presents a study that aims to explore some of the less well studied barriers to healthcare access, particularly those that influence healthcare acceptability and appropriateness.

In truth, healthcare access entails a complicated calculus that combines characteristics of individuals, their households, and their social and physical environments with characteristics of healthcare delivery systems, organizations, and healthcare providers. For one to fully ‘access’ healthcare, they must have the means to identify their healthcare needs and have available to them care providers and the facilities where they work. Further, patients must then reach, obtain, and use the healthcare services in order to have their healthcare needs fulfilled. Levesque and colleagues critically examined access conceptualizations in 2013 and synthesized all ways in which access to healthcare was previously characterized; Levesque et al. proposed five dimensions of access: approachability, acceptability, availability, affordability and appropriateness [ 2 ]. These refer to the ability to perceive, seek, reach, pay for, and engage in services, respectively.

According to Levesque et al.’s framework, the five dimensions combine to facilitate access to care or serve as barriers. Approachability indicates that people facing health needs understand that healthcare services exist and might be helpful. Acceptability represents whether patients see healthcare services as consistent or inconsistent with their own social and cultural values and worldviews. Availability indicates that healthcare services are reached both physically and in a timely manner. Affordability simplifies one’s capacity to pay for healthcare services without compromising basic necessities, and finally, appropriateness represents the fit between healthcare services and a patient’s specific healthcare needs [ 2 ]. This study focused on the acceptability and appropriateness dimensions of access.

Before the novel coronavirus (SARS-CoV-2; COVID-19) pandemic, approximately 13.3% of adults in the US did not have a usual source of healthcare [ 6 ]. Millions more did not utilize services regularly, and close to two-thirds reported that they would be debilitated by an unexpected medical bill [ 7 , 8 , 9 ]. Findings like these emphasized a fragility in the financial security of the American population [ 10 ]. These concerns were exacerbated by the pandemic when a sudden surge in unemployment increased un- and under-insurance rates [ 11 ]. Indeed, employer-sponsored insurance covers close to half of Americans’ total cost of illness [ 12 ]. Unemployment linked to COVID-19 cut off the lone outlet to healthcare access for many. Health-related financial concerns expanded beyond individuals, as healthcare organizations were unequipped to manage a simultaneous increase in demand for specialized healthcare services and a steep drop off for routine revenue-generating healthcare services [ 13 ]. These consequences of the COVID-19 pandemic all put additional, unexpected pressure on an already fragmented US healthcare system.

Other structural barriers to healthcare access exist in relation to the rural–urban divide. Less than 10% of US healthcare resources are located in rural areas where approximately 20% of the American population resides [ 14 ]. In a country with substantially fewer providers per capita compared to many other developed countries, persons in rural areas experience uniquely pressing healthcare provider shortages [ 15 , 16 ]. Rural inhabitants also tend to have lower household income, higher rates of un- or under-insurance, and more difficulty with travel to healthcare clinics than urban dwellers [ 17 ]. Subsequently, persons in rural communities use healthcare services at lower rates, and potentially preventable hospitalizations are more prevalent [ 18 ]. This disparity often leads rural residents to use services primarily for more urgent needs and less so for routine care [ 19 , 20 , 21 ].

The differences in how rural and urban healthcare systems function warranted a federal initiative to focus exclusively on rural health priorities and serve as counterpart to Healthy People objectives [ 22 ]. The rural determinants of health, a more specific expression of general social determinants, add issues of geography and topography to the well-documented social, economic and political factors that influence all Americans’ access to healthcare [ 23 ]. As a result, access is consistently regarded as a top priority in rural areas, and many research efforts have explored the intersection between access and rurality, namely within its less understood dimensions (acceptability and appropriateness) [ 22 ].

Acceptability-related barriers to care

Acceptability represents the dimension of healthcare access that affects a patient’s ability to seek healthcare, particularly linked to one’s professional values, norms and culture [ 2 ]. Access to health information is an influential factor for acceptable healthcare and is essential to promote and maintain a healthy population [ 24 ]. According to the Centers for Disease Control and Prevention, health literacy or a high ‘health IQ’ is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others, which impacts healthcare use and system navigation [ 25 ]. The literature indicates that lower levels of health literacy contribute to health disparities among rural populations [ 26 , 27 , 28 ]. Evidence points to a need for effective health communication between healthcare organizations and patients to improve health literacy [ 24 ]. However, little research has been done in this area, particularly as it relates to technologically-based interventions to disseminate health information [ 29 ].

Stigma, an undesirable position of perceived diminished status in an individual’s social position, is another challenge that influences healthcare acceptability [ 30 ]. Those who may experience stigma fear negative social consequences in relation to care seeking. They are more likely to delay seeking care, especially among ethnic minority populations [ 31 , 32 ]. Social media presents opportunities for the dissemination of misleading medical information; this runs further risk for stigma [ 33 ]. Stigma is difficult to undo, but research has shown that developing a positive relationship with a healthcare provider or organization can work to reduce stigma among patients, thus promoting healthcare acceptability [ 34 ].

A provider’s attempts to engage patients and empower them to be active decision-makers regarding their treatment has also been shown to improve healthcare acceptability. One study found that patients with heart disease who completed a daily diary of weight and self-assessment of symptoms, per correspondence with their provider, had better care outcomes than those who did not [ 35 ]. Engaging with household family members and involved community healers also mitigates barriers to care, emphasizing the importance of a team-based approach that extends beyond those who typically provide healthcare services [ 36 , 37 ]. One study, for instance, explored how individuals closest to a pregnant woman affect the woman’s decision to seek maternity care; partners, female relatives, and community health-workers were among the most influential in promoting negative views, all of which reduced a woman’s likelihood to access care [ 38 ].

Appropriateness-related barriers to care

Appropriateness marks the dimension of healthcare access that affects a patient’s ability to engage, and according to Levesque et al., is of relevance once all other dimensions (the ability to perceive, seek, reach and pay for) are achieved [ 2 ]. The ability to engage in healthcare is influenced by a patient’s level of empowerment, adherence to information, and support received by their healthcare provider. Thus, barriers to healthcare access that relate to appropriateness are often those that indicate a breakdown in communication between a patient with their healthcare provider. Such breakdown can involve a patient experiencing miscommunication, confrontation, and/or a discrepancy between their provider’s goals and their own goals for healthcare. Appropriateness represents a dimension of healthcare access that is widely acknowledged as an area in need of improvement, which indicates a need to rethink how healthcare providers and organizations can adapt to serve the healthcare needs of their communities [ 39 ]. This is especially true for rural, ethnic minority populations, which disproportionately experience an abundance of other barriers to healthcare access. Culturally appropriate care is especially important for members of minority populations [ 40 , 41 , 42 ]. Ultimately, patients value a patient-provider relationship characterized by a welcoming, non-judgmental atmosphere [ 43 , 44 ]. In rural settings especially, level of trust and familiarity are common factors that affect service utilization [ 45 ]. Evidence suggests that kind treatment by a healthcare provider who promotes patient-centered care can have a greater overall effect on a patient’s experience than a provider’s degree of medical knowledge or use of modern equipment [ 46 ]. Of course, investing the time needed to nurture close and caring interpersonal connections is particularly difficult in under-resourced, time-pressured rural health systems [ 47 , 48 ].

The most effective way to evaluate access to healthcare largely depends on which dimensions are explored. For instance, a population-based survey can be used to measure the barrier of healthcare affordability. Survey questions can inquire directly about health insurance coverage, care-related financial burden, concern about healthcare costs, and the feared financial impacts of illness and/or disability. Many national organizations have employed such surveys to measure affordability-related barriers to healthcare. For example, a question may ask explicitly about financial concerns: ‘If you get sick or have an accident, how worried are you that you will not be able to pay your medical bills?’ [ 49 ]. Approachability and availability dimensions of access are also studied using quantitative analysis of survey questions, such as ‘Is there a place that you usually go to when you are sick or need advice about your health?’ or ‘Have you ever delayed getting medical care because you couldn’t get through on the telephone?’ In contrast, the remaining two dimensions–acceptability and appropriateness–require a qualitative approach, as the social and cultural factors that determine a patient’s likelihood of accepting aspects of the services that are to be received (acceptability) and the fit between those services and the patient’s specific healthcare needs (appropriateness) can be more abstract [ 50 , 51 ]. In social science, qualitative methods are appropriate to generate knowledge of what social events mean to individuals and how those individuals interact within them; these methods allow for an exploration of depth rather than breadth [ 52 , 53 ]. Qualitative methods, therefore, are appropriate tools for understanding the depth of healthcare providers’ experiences in the inherently social context of seeking and engaging in healthcare.

In sum, acceptability- and appropriateness-related barriers to healthcare access are multi-layered, complex and abundant. Ensuring access becomes even more challenging if structural barriers to access are factored in. In this study, we aimed to explore barriers to healthcare access among persons in Montana, a historically underserved, under-resourced, rural region of the US. Montana is the fourth largest and third least densely populated state in the country; more than 80% of Montana counties are classified as non-core (the lowest level of urban/rural classification), and over 90% are designated as health professional shortage areas [ 54 , 55 ]. Qualitative methods supported our inquiry to explore barriers to healthcare access related to acceptability and appropriateness.

Participants

Qualitative methods were utilized for this interpretive, exploratory study because knowledge regarding barriers to healthcare access within Montana’s rural health systems is limited. We chose Montana healthcare providers, rather than patients, as the population of interest so we may explore barriers to healthcare access from the perspective of those who serve many persons in rural settings. Inclusion criteria required study participants to provide direct healthcare to patients at least one-half of their time. We defined ‘provider’ as a healthcare organization employee with clinical decision-making power and the qualifications to develop or revise patients’ treatment plans. In an attempt to capture a group of providers with diverse experience, we included providers across several types and specialties. These included advanced practice registered nurses (APRNs), physicians (MDs and DOs), and physician assistants (PAs) who worked in critical care medicine, emergency medicine, family medicine, hospital medicine, internal medicine, pain medicine, palliative medicine, pediatrics, psychiatry, and urgent care medicine. We also included licensed clinical social workers (LCSWs) and clinical psychologists who specialize in behavioral healthcare provision.

Recruitment and Data Collection

We recruited participants via email using a snowball sampling approach [ 56 ]. We opted for this approach because of its effectiveness in time-pressured contexts, such as the COVID-19 pandemic, which has made healthcare provider populations hard to reach [ 57 ]. Considering additional constraints with the pandemic and the rural nature of Montana, interviews were administered virtually via Zoom video or telephone conferencing with Zoom’s audio recording function enabled. All interviews were conducted by the first author between January and September 2021. The average length of interviews was 50 min, ranging from 35 to 70 min. There were occasional challenges experienced during interviews (poor cell phone reception from participants, dropped calls), in which case the interviewer remained on the line until adequate communication was resumed. All interviews were included for analysis and transcribed verbatim into NVivo Version 12 software. All qualitative data were saved and stored on a password-protected University of Montana server. Hard-copy field notes were securely stored in a locked office on the university’s main campus.

Data analysis included a deductive followed by an inductive approach. This dual analysis adheres to Levesque’s framework for qualitative methods, which is discussed in the Definition of Analytic Domains sub-section below. Original synthesis of the literature informed the development of our initial deductive codebook. The deductive approach was derived from a theory-driven hypothesis, which consisted of synthesizing previous research findings regarding acceptability- and appropriateness-related barriers to care. Although the locations, patient populations and specific type of healthcare services varied by study in the existing literature, several recurring barriers to healthcare access were identified. We then operationalized three analytic domains based on these findings: cultural considerations, patient-provider communication, and provider-provider communication. These domains were chosen for two reasons: 1) the terms ‘culture’ and ‘communication’ were the most frequently documented characteristics across the studies examined, and 2) they each align closely with the acceptability and appropriateness dimensions of access to healthcare, respectively. In addition, ‘culture’ is included in the definition of acceptability and ‘communication’ is a quintessential aspect of appropriateness. These domains guided the deductive portion of our analysis, which facilitated the development of an interview guide used for this study.

Interviews were semi-structured to allow broad interpretations from participants and expand the open-ended characterization of study findings. Data were analyzed through a flexible coding approach proposed by Deterding and Waters [ 58 ]. Qualitative content analysis was used, a method particularly beneficial for analyzing large amounts of qualitative data collected through interviews that offers possibility of quantifying categories to identify emerging themes [ 52 , 59 ]. After fifty percent of data were analyzed, we used an inductive approach as a formative check and repeated until data saturation, or the point at which no new information was gathered in interviews [ 60 ]. At each point of inductive analysis, interview questions were added, removed, or revised in consideration of findings gathered [ 61 ]. The Standards for Reporting Qualitative Research (SRQR) was used for reporting all qualitative data for this study [ 62 ]. The first and third authors served as primary and secondary analysts of the qualitative data and collaborated to triangulate these findings. An audit approach was employed, which consisted of coding completed by the first author and then reviewed by the third author. After analyses were complete, member checks ensured credibility and trustworthiness of findings [ 63 ]. Member checks consisted of contacting each study participant to explain the study’s findings; one-third of participants responded and confirmed all findings. All study procedures were reviewed and approved by the Human Subjects Committee of the authors’ institution’s Institutional Review Board.

Definitions of Analytic Domains

Cultural considerations.

Western health systems often fail to consider aspects of patients’ cultural perspectives and histories. This can manifest in the form of a providers’ lack of cultural humility. Cultural humility is a process of preventing imposition of one’s worldview and cultural beliefs on others and recognizing that everyone’s conception of the world is valid. Humility cultivates sensitive approaches in treating patients [ 64 ]. A lack of cultural humility impedes the delivery of acceptable and appropriate healthcare [ 65 ], which can involve low empathy or respect for patients, or dismissal of culture and traditions as superstitions that interfere with standard treatments [ 66 , 67 ]. Ensuring cultural humility among all healthcare employees is a step toward optimal healthcare delivery. Cultural humility is often accomplished through training that can be tailored to particular cultural- or gender-specific populations [ 68 , 69 ]. Since cultural identities and humility have been marked as factors that can heavily influence patients’ access to care, cultural considerations composed our first analytic domain. To assess this domain, we asked participants how they address the unique needs of their patients, how they react when they observe a cultural behavior or attitude from a patient that may not directly align with their treatment plan, and if they have received any multicultural training or training on cultural considerations in their current role.

Patient-provider communication

Other barriers to healthcare access can be linked to ineffective patient-provider communication. Patients who do not feel involved in healthcare decisions are less likely to adhere to treatment recommendations [ 70 ]. Patients who experience communication difficulties with providers may feel coerced, which generates disempowerment and leads patients to employ more covert ways of engagement [ 71 , 72 ]. Language barriers can further compromise communication and hinder outcomes or patient progress [ 73 , 74 ]. Any miscommunication between a patient and provider can affect one’s access to healthcare, namely affecting appropriateness-related barriers. For these reasons, patient-provider communication composed our second analytic domain. We asked participants to highlight the challenges they experience when communicating with their patients, how those complications are addressed, and how communication strategies inform confidentiality in their practice. Confidentiality is a core ethical principle in healthcare, especially in rural areas that have smaller, interconnected patient populations [ 75 ].

Provider-Provider Communication

A patient’s journey through the healthcare system necessitates sufficient correspondence between patients, primary, and secondary providers after discharge and care encounters [ 76 ]. Inter-provider and patient-provider communication are areas of healthcare that are acknowledged to have some gaps. Inconsistent mechanisms for follow up communication with patients in primary care have been documented and emphasized as a concern among those with chronic illness who require close monitoring [ 68 , 77 ]. Similar inconsistencies exist between providers, which can lead to unclear care goals, extended hospital stays, and increased medical costs [ 78 ]. For these reasons, provider-provider communication composed our third analytic domain. We asked participants to describe the approaches they take to streamline communication after a patient’s hospital visit, the methods they use to ensure collaborative communication between primary or secondary providers, and where communication challenges exist.

Healthcare provider characteristics

Our sample included 12 providers: four in family medicine (1 MD, 1 DO, 1 PA & 1 APRN), three in pediatrics (2 MD with specialty in hospital medicine & 1 DO), three in palliative medicine (2 MDs & 1 APRN with specialty in wound care), one in critical care medicine (DO with specialty in pediatric pulmonology) and one in behavioral health (1 LCSW with specialty in trauma). Our participants averaged 9 years (range 2–15) as a healthcare provider; most reported more than 5 years in their current professional role. The diversity of participants extended to their patient populations as well, with each participant reporting a unique distribution of age, race and level of medical complexity among their patients. Most participants reported that a portion of their patients travel up to five hours, sometimes across county- or state-lines, to receive care.

Theme 1: A friction exists between aspects of patients’ rural identities and healthcare systems

Our participants comprised a collection of medical professions and reported variability among health-related reasons their patients seek care. However, most participants acknowledged similar characteristics that influence their patients’ challenges to healthcare access. These identified factors formed categories from which the first theme emerged. There exists a great deal of ‘rugged individualism’ among Montanans, which reflects a self-sufficient and self-reliant way of life. Stoicism marked a primary factor to characterize this quality. One participant explained:

True Montanans are difficult to treat medically because they tend to be a tough group. They don’t see doctors. They don’t want to go, and they don’t want to be sick. That’s an aspect of Montana that makes health culture a little bit difficult.

Another participant echoed this finding by stating:

The backwoods Montana range guy who has an identity of being strong and independent probably doesn’t seek out a lot of medical care or take a lot of medications. Their sense of vitality, independence and identity really come from being able to take care and rely on themselves. When that is threatened, that’s going to create a unique experience of illness.

Similar responses were shared by all twelve participants; stoicism seemed to be heavily embedded in many patient populations in Montana and serves as a key determinant of healthcare acceptability. There are additional factors, however, that may interact with stoicism but are multiply determined. Stigma is an example of this, presented in this context as one’s concern about judgement by the healthcare system. Respondents were openly critical of this perception of the healthcare system as it was widely discussed in interviews. One participant stated:

There is a real perception of a punitive nature in the medical community, particularly if I observe a health issue other than the primary reason for one’s hospital visit, whether that may be predicated on medical neglect, delay of care, or something that may warrant a report to social services. For many of the patients and families I see, it’s not a positive experience and one that is sometimes an uphill barrier that I work hard to circumnavigate.

Analysis of these factors suggest that low use of healthcare services may link to several characteristics, including access problems. Separately, a patient’s perceived stigma from healthcare providers may also impact a patient’s willingness to receive services. One participant put it best by stating

Sometimes, families assume that I didn’t want to see them because they will come in for follow up to meet with me but end up meeting with another provider, which is frustrating because I want to maintain patients on my panel but available time and resource occasionally limits me from doing so. It could be really hard adapting to those needs on the fly, but it’s an honest miss.

When a patient arrives for a healthcare visit and experiences this frustration, it may elicit a patient’s perceptions of neglect or disorganization. This ‘honest miss’ may, in turn, exacerbate other acceptable-related barriers to care.

Theme 2: Facilitating access to healthcare requires application of and respect for cultural differences

The biomedical model is the standard of care utilized in Western medicine [ 79 , 80 ]. However, the US comprises people with diverse social and cultural identities that may not directly align with Western conceptions of health and wellness. Approximately 11.5% of the Montana population falls within an ethnic minority group. 6.4% are of American Indian or Alaska Native origin, 0.5% are of Black or African American origin, 0.8% are of Asian origin and 3.8% are of multiple or other origins. [ 81 ]. Cultural insensitivity is acknowledged in health services research as an active deterrent for appropriate healthcare delivery [ 65 ]. Participants for this study were asked how they react when a patient brings up a cultural attitude or behavior that may impact the proposed treatment plan. Eight participants noted a necessity for humility when this occurs. One participant conceptualized this by stating:

When this happens, I learn about individuals and a way of life that is different to the way I grew up. There is a lot of beauty and health in a non-patriarchal, non-dominating, non-sexist framework, and when we can engage in such, it is really expansive for my own learning process.

The participants who expressed humility emphasized that it is best to work in tandem with their patient, congruently. Especially for those with contrasting worldviews, a provider and a patient working as a team poses an opportunity to develop trust. Without it, a patient can easily fall out of the system, further hindering their ability to access healthcare services in the future. One participant stated:

The approach that ends up being successful for a lot of patients is when we understand their modalities, and they have a sense we understand those things. We have to show understanding and they have to trust. From there, we can make recommendations to help get them there, not decisions for them to obey, rather views based on our experiences and understanding of medicine.

Curiosity was another reaction noted by a handful of participants. One participant said:

I believe patients and their caregivers can be engaged and loving in different ways that don’t always follow the prescribed approach in the ways I’ve been trained, but that doesn’t necessarily mean that they are detrimental. I love what I do, and I love learning new things or new approaches, but I also love being surprised. My style of medicine is not to predict peoples’ lives, rather to empower and support what makes life meaningful for them.

Participants mentioned several other characteristics that they use in practice to prevent cultural insensitivity and support a collaborative approach to healthcare. Table 1 lists these facilitating characteristics and quotes to explain the substance of their benefit.

Consensus among participants indicated that the use of these protective factors to promote cultural sensitivity and apply them in practice is not standardized. When asked, all but two participants said they had not received any culturally-based training since beginning their practice. Instead, they referred to developing skills through “on the job training” or “off the cuff learning.” The general way of medicine, one participant remarked, was to “throw you to the fire.” This suggested that use of standardized cultural humility training modules for healthcare providers was not common practice. Many attributed this to time constraints.

Individual efforts to gain culturally appropriate skills or enhance cultural humility were mentioned, however. For example, three participants reported that they attended medical conferences to discuss cultural challenges within medicine, one participant sought out cultural education within their organization, and another was invited by Native American community members to engage in traditional peace ceremonies. Participants described these additional efforts as uncommon and outside the parameters of a provider’s job responsibilities, as they require time commitments without compensation.

Additionally, eight participants said they share their personal contact information with patients so they may call them directly for medical needs. The conditions and frequency with which this is done was variable and more common among providers in specialized areas of medicine or those who described having a manageable patient panel. All who reported that they shared their personal contact information described it as an aspect of rural health service delivery that is atypical in other, non-rural healthcare systems.

Theme 3: Communication between healthcare providers is systematically fragmented

Healthcare is complex and multi-disciplinary, and patients’ treatment is rarely overseen by a single provider [ 82 ]. The array of provider types and specialties is vast, as is the range of responsibilities ascribed to providers. Thus, open communication among providers both within and between healthcare systems is vital for the success of collaborative healthcare [ 83 ]. Without effective communication achieved between healthcare providers, the appropriate delivery of healthcare services may be become compromised. Our participants noted that they face multiple challenges that complicate communication with other providers. Miscommunication between departments, often implicating the Emergency Department (ED), was a recurring point noted among participants. One participant who is a primary care physician said:

If one of my patients goes to the ER, I don’t always get the notes. They’re supposed to send them to the patient’s primary care doc. The same thing happens with general admissions, but again, I often find out from somebody else that my patient was admitted to the hospital.

This failure to communicate can negatively impact the patient, particularly if time sensitivity or medical complexity is essential to treatment. A patient’s primary care physician is the most accurate source of their medical history; without an effective way to obtain and synthesize a patient’s health information, there may be increased risk of medical error. One participant in a specialty field stated:

One of the biggest barriers I see is obtaining a concise description of a patient’s history and needs. You can imagine if you’re a mom and you’ve got a complicated kid. You head to the ER. The ER doc looks at you with really wide eyes, not knowing how to get information about your child that’s really important.

This concern was highlighted with a specific example from a different participant:

I have been unable to troubleshoot instances when I send people to the ER with a pretty clear indication for admission, and then they’re sent home. For instance, I had an older fellow with pretty severe chronic kidney disease. He presented to another practitioner in my office with shortness of breath and swelling and appeared to have newly onset decompensated heart failure. When I figured this out, I sent him to the ER, called and gave my report. The patient later came back for follow up to find out not only that they had not been admitted but they lost no weight with outpatient dialysis . I feel like a real opportunity was missed to try to optimize the care of the patient simply because there was poor communication between myself and the ER. This poor guy… He ended up going to the ER four times before he got admitted for COVID-19.

In some cases, communication breakdown was reported as the sole cause of a poor outcome. When communication is effective, each essential member of the healthcare team is engaged and collaborating with the same information. Some participants called this process ‘rounds’ when a regularly scheduled meeting is staged between a group of providers to ensure access to accurate patient information. Accurate communication may also help build trust and improve a patient’s experience. In contrast, ineffective communication can result in poor clarity regarding providers’ responsibilities or lost information. Appropriate delivery of healthcare considers the fit between providers and a patient’s specific healthcare needs; the factors noted here suggest that provider-provider miscommunication can adversely affect this dimension of healthcare access.

Another important mechanism of communication is the sharing of electronic medical records (EMRs), a process that continues to shift with technological advances. Innovation is still recent enough, however, for several of our study participants to be able to recall a time when paper charts were standard. Widespread adoption and embrace of the improvements inherent in electronic medical records expanded in the late 2000’s [ 84 ]. EMRs vastly improved the ability to retain, organize, safeguard, and transfer health information. Every participant highlighted EMRs at one point or another and often did so with an underlying sense of anger or frustration. Systematic issues and problems with EMRs were discussed. One participant provided historical context to such records:

Years back, the government aimed to buy an electronic medical record system, whichever was the best, and a number of companies created their own. Each were a reasonable system, so they all got their checks and now we have four completely separate operating systems that do not talk to each other. The idea was to make a router or some type of relay that can share information back and forth. There was no money in that though, so of course, no one did anything about it. Depending on what hospital, clinic or agency you work for, you will most likely work within one of these systems. It was a great idea; it just didn’t get finished.

Seven participants confirmed these points and their impacts on making coordination more difficult, relying on outdated communication strategies more often than not. Many noted this even occurs between facilities within the same city and in separate small metropolitan areas across the state. One participant said:

If my hospital decides to contract with one EMR and the hospital across town contracts with another, correspondence between these hospitals goes back to traditional faxing. As a provider, you’re just taking a ‘fingered crossed’ approach hoping that the fax worked, is picked up, was put in the appropriate inbox and was actually looked at. Information acquisition and making sure it’s timely are unforeseen between EMRs.

Participants reported an “astronomic” number of daily faxes and telephone calls to complete the communication EMRs were initially designed to handle. These challenges are even more burdensome if a patient moves from out of town or out of state; obtaining their medical records was repeatedly referred to as a “chore” so onerous that it often remains undone. Another recurring concern brought up by participants regarded accuracy within EMRs to lend a false sense of security. They are not frequently updated, not designed to be family-centered and not set up to do anything automatically. One participant highlighted these limitations by stating:

I was very proud of a change I made in our EMR system [EPIC], even though it was one I never should have had to make. I was getting very upset because I would find out from my nursing assistant who read the obituary that one of my patients had died. There was a real problem with the way the EMR was notifying PCP’s, so I got an EPIC-level automated notification built into our EMR so that any time a patient died, their status would be changed to deceased and a notification would be sent to their PCP. It’s just really awful to find out a week later that your patient died, especially when you know these people and their families really well. It’s not good care to have blind follow up.

Whether it be a physical or electronic miscommunication between healthcare providers, the appropriate delivery of healthcare can be called to question

Theme 4: Time and resource constraints disproportionately harm rural health systems

Several measures of system capacity suggest the healthcare system in the US is under-resourced. There are fewer physicians and hospital beds per capita compared to most comparable countries, and the growth of healthcare provider populations has stagnated over time [ 15 ]. Rural areas, in particular, are subject to resource limitations [ 16 ]. All participants discussed provider shortages in detail. They described how shortages impact time allocation in their day-to-day operations. Tasks like patient intakes, critical assessments, and recovering information from EMRs take time, of which most participants claimed to not have enough of. There was also a consensus in having inadequate time to spend on medically complex cases. Time pressures were reported to subsequently influence quality of care. One participant stated:

With the constant pace of medicine, time is not on your side. A provider cannot always participate in an enriching dialogue with their patients, so rather than listen and learn, we are often coerced into the mindset of ‘getting through’ this patient so we can move on. This echoes for patient education during discharge, making the whole process more arduous than it otherwise could be if time and resources were not as sparse.

Depending on provider type, specialty, and the size of patient panels, four participants said they have the luxury of extending patient visits to 40 + minutes. Any flexibility with patient visits was regarded as just that: a luxury. Very few providers described the ability to coordinate their schedules as such. This led some study participants to limit the number of patients they serve. One participant said:

We simply don’t have enough clinicians, which is a shame because these people are really skilled, exceptional, brilliant providers but are performing way below their capacity. Because of this, I have a smaller case load so I can engage in a level of care that I feel is in the best interest of my patients. Everything is a tradeoff. Time has to be sacrificed at one point or another. This compromise sets our system up to do ‘ok’ work, not great work.

Of course, managing an overly large number of patients with high complexity is challenging. Especially while enduring the burden of a persisting global pandemic, participants reflected that the general outlook of administering healthcare in the US is to “do more with less.” This often forces providers to delegate responsibilities, which participants noted has potential downsides. One participant described how delegating patient care can cause problems.

Very often will a patient schedule a follow up that needs to happen within a certain time frame, but I am unable to see them myself. So, they are then placed with one of my mid-level providers. However, if additional health issues are introduced, which often happens, there is a high-risk of bounce-back or need to return once again to the hospital. It’s an inefficient vetting process that falls to people who may not have specific training in the labs and imaging that are often included in follow up visits. Unfortunately, it’s a forlorn hope to have a primary care physician be able to attend all levels of a patient’s care.

Several participants described how time constraints stretch all healthcare staff thin and complicate patient care. This was particularly important among participants who reported having a patient panel exceeding 1000. There were some participants, however, who praised the relationships they have with their nurse practitioners and physician’s assistants and mark transparency as the most effective way to coordinate care. Collectively, these clinical relationships were built over long standing periods of time, a disadvantage to providers at the start of their medical career. All but one participant with over a decade of clinical experience mentioned the usefulness of these relationships. The factors discussed in Theme 4 are directly linked to the Availability dimension of access to healthcare. A patient’s ability to reach care is subject to the capacity of their healthcare provider(s). Additionally, further analysis suggests these factors also link to the Appropriateness dimension because the quality of patient-provider relationships may be negatively impacted if a provider’s time is compromised.

Theme 5: Profits are prioritized over addressing barriers to healthcare access in the US.

The US healthcare system functions partially for-profit in the public and private sectors. The federal government provides funding for national programs such as Medicare, but a majority of Americans access healthcare through private employer plans [ 85 ]. As a result, uninsurance rates influence healthcare access. Though the rate of the uninsured has dropped over the last decade through expansion of the Affordable Care Act, it remains above 8 percent [ 86 ]. Historically, there has been ethical criticism in the literature of a for-profit system as it is said to exacerbate healthcare disparities and constitute unfair competition against nonprofit institutions. Specifically, the US healthcare system treats healthcare as a commodity instead of a right, enables organizational controls that adversely affect patient-provider relationships, undermines medical education, and constitutes a medical-industrial complex that threatens influence on healthcare-related public policy [ 87 ]. Though unprompted by the interviewer, participants raised many of these concerns. One participant shared their views on how priorities stand in their practice:

A lot of the higher-ups in the healthcare system where I work see each patient visit as a number. It’s not that they don’t have the capacity to think beyond that, but that’s what their role is, making sure we’re profitable. That’s part of why our healthcare system in the US is as broken as it is. It’s accentuated focus on financially and capitalistically driven factors versus understanding all these other barriers to care.

Eight participants echoed a similar concept, that addressing barriers to healthcare access in their organizations is largely complicated because so much attention is directed on matters that have nothing to do with patients. A few other participants supported this by alluding to a “cherry-picking” process by which those at the top of the hierarchy devote their attention to the easiest tasks. One participant shared an experience where contrasting work demands between administrators and front-line clinical providers produces adverse effects:

We had a new administrator in our hospital. I had been really frustrated with the lack of cultural awareness and curiosity from our other leaders in the past, so I offered to meet and take them on a tour of the reservation. This was meant to introduce them to kids, families and Tribal leaders who live in the area and their interface with healthcare. They declined, which I thought was disappointing and eye-opening.

Analysis of these factors suggest that those who work directly with patients understand patient needs better than those who serve in management roles. This same participant went on to suggest an ulterior motive for a push towards telemedicine, as administrators primarily highlight the benefit of billing for virtual visits instead of the nature of the visits themselves.

This study explored barriers and facilitators to healthcare access from the perspective of rural healthcare providers in Montana. Our qualitative analysis uncovered five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. Themes 2 and 3 were directly supported by earlier qualitative studies that applied Levesque’s framework, specifically regarding healthcare providers’ poor interpersonal quality and lack of collaboration with other providers that are suspected to result from a lack of provider training [ 67 , 70 ]. This ties back to the importance of cultural humility, which many previous culture-based trainings have referred to as cultural competence. Cultural competence is achieved through a plethora of trainings designed to expose providers to different cultures’ beliefs and values but induces risk of stereotyping and stigmatizing a patient’s views. Therefore, cultural humility is the preferred idea, by which providers reflect and gain open-ended appreciation for a patient’s culture [ 88 ].

Implications for Practice

Perhaps the most substantial takeaway is how embedded rugged individualism is within rural patient populations and how difficult that makes the delivery of care in rural health systems. We heard from participants that stoicism and perceptions of stigma within the system contribute to this, but other resulting factors may be influential at the provider- and organizational-levels. Stoicism and perceived stigma both appear to arise, in part, from an understandable knowledge gap regarding the care system. For instance, healthcare providers understand the relations between primary and secondary care, but many patients may perceive both concepts as elements of a single healthcare system [ 89 ]. Any issue experienced by a patient when tasked to see both a primary and secondary provider may result in a patient becoming confused [ 90 ]. This may also overlap with our third theme, as a disjointed means of communication between healthcare providers can exacerbate patients’ negative experiences. One consideration to improve this is to incorporate telehealth programs into an existing referral framework to reduce unnecessary interfacility transfers; telehealth programs have proven effective in rural and remote settings [ 91 ].

In fact, telehealth has been rolled out in a variety of virtual platforms throughout its evolution, its innovation matched with continued technological advancement. Simply put, telehealth allows health service delivery from a distance; it allows knowledge and practice of clinical care to be in a different space than a patient. Because of this, a primary benefit of telehealth is its impact on improving patient-centered outcomes among those living in rural areas. For instance, text messaging technology improves early infant diagnosis, adherence to recommended diagnostic testing, and participant engagement in lifestyle change interventions [ 92 , 93 , 94 ]. More sophisticated interventions have found their way into smartphone-based technology, some of which are accessible even without an internet connection [ 95 , 96 ]. Internet accessibility is important because a number of study participants noted internet connectivity as a barrier for patients who live in low resource communities. Videoconferencing is another function of telehealth that has delivered a variety of health services, including those which are mental health-specific [ 97 ], and mobile health clinics have been used in rural, hard-to-reach settings to show the delivery of quality healthcare is both feasible and acceptable [ 98 , 99 , 100 ]. While telehealth has potential to reduce a number of healthcare access barriers, it may not always address the most pressing healthcare needs [ 101 ]. However, telehealth does serve as a viable, cost-effective alternative for rural populations with limited physical access to specialized services [ 102 ]. With time and resource limitations acknowledged as a key theme in our study, an emphasis on expanding telehealth services is encouraged as it will likely have significant involvement on advancing healthcare in the future, especially as the COVID-19 pandemic persists [ 103 ].

Implications for Policy

One could argue that most of the areas of fragmentation in the US healthcare system can be linked to the very philosophy on which it is based: an emphasis on profits as highest priority. Americans are, therefore, forced to navigate a health service system that does not work solely in their best interests. It is not surprising to observe lower rates of healthcare usage in rural areas, which may be a result from rural persons’ negative views of the US healthcare system or a perception that the system does not exist to support wellness. These perceptions may interact with ‘rugged individualism’ to squelch rural residents’ engagement in healthcare. Many of the providers we interviewed for this study appeared to understand this and strived to improve their patients’ experiences and outcomes. Though these efforts are admirable, they may not characterize all providers who serve in rural areas of the US. From a policy standpoint, it is important to recognize these expansive efforts from providers. If incentives were offered to encourage maximum efforts be made, it may lessen burden due to physician burnout and fatigue. Of course, there is no easy fix to the persisting limit of time and resources for providers, problems that require workforce expansion. Ultimately, though, the current structure of the US healthcare system is failing rural America and doing little to help the practice of rural healthcare providers.

Implications for Future Research

It is important for future health systems research efforts to consider issues that arise from both individual- and system-level access barriers and where the two intersect. Oftentimes, challenges that appear linked to a patient or provider may actually stem from an overarching system failure. If failures are critically and properly addressed, we may refine our understanding of what we can do in our professional spaces to improve care as practitioners, workforce developers, researchers and advocates. This qualitative study was exploratory in nature. It represents a step forward in knowledge generation regarding challenges in access to healthcare for rural Americans. Although mental health did not come up by design in this study, future efforts exploring barriers to healthcare access in rural systems should focus on access to mental healthcare. In many rural areas, Montana included, rates of suicide, substance use and other mental health disorders are highly prevalent. These characteristics should be part of the overall discussion of access to healthcare in rural areas. Optimally, barriers to healthcare access should continue to be explored through qualitative and mixed study designs to honor its multi-dimensional stature.

Strengths and Limitations

It is important to note first that this study interviewed healthcare providers instead of patients, which served as both a strength and limitation. Healthcare providers were able to draw on numerous patient-provider experiences, enabling an account of the aggregate which would have been impossible for a patient population. However, accounts of healthcare providers’ perceptions of barriers to healthcare access for their patients may differ from patients’ specific views. Future research should examine acceptability- and appropriateness-related barriers to healthcare access in patient populations. Second, study participants were recruited through convenience sampling methods, so results may be biased towards healthcare providers who are more invested in addressing barriers to healthcare access. Particularly, the providers interviewed for this study represented a subset who go beyond expectations of their job descriptions by engaging with their communities and spending additional uncompensated time with their patients. It is likely that a provider who exhibits these behavioral traits is more likely to participate in research aimed at addressing barriers to healthcare access. Third, the inability to conduct face-to-face interviews for our qualitative study may have posed an additional limitation. It is possible, for example, that in-person interviews might have resulted in increased rapport with study participants. Notwithstanding this possibility, the remote interview format was necessary to accommodate health risks to the ongoing COVID-19 pandemic. Ultimately, given our qualitative approach, results from our study cannot be generalizable to all rural providers’ views or other rural health systems. In addition, no causality can be inferred regarding the influence of aspects of rurality on access. The purpose of this exploratory qualitative study was to probe research questions for future efforts. We also acknowledge the authors’ roles in the research, also known as reflexivity. The first author was the only author who administered interviews and had no prior relationships with all but one study participant. Assumptions and pre-dispositions to interview content by the first author were regularly addressed throughout data analysis to maintain study integrity. This was achieved by conducting analysis by unique interview question, rather than by unique participant, and recoding the numerical order of participants for each question. Our commitment to rigorous qualitative methods was a strength for the study for multiple reasons. Conducting member checks with participants ensured trustworthiness of findings. Continuing data collection to data saturation ensured dependability of findings, which was achieved after 10 interviews and confirmed after 2 additional interviews. We further recognize the heterogeneity in our sample of participants, which helped generate variability in responses. To remain consistent with appropriate means of presenting results in qualitative research however, we shared minimal demographic information about our study participants to ensure confidentiality.

The divide between urban and rural health stretches beyond a disproportionate allocation of resources. Rural health systems serve a more complicated and hard-to-reach patient population. They lack sufficient numbers of providers to meet population health needs. These disparities impact collaboration between patients and providers as well as the delivery of acceptable and appropriate healthcare. The marker of rurality complicates the already cumbersome challenge of administering acceptable and appropriate healthcare and impediments stemming from rurality require continued monitoring to improve patient experiences and outcomes. Our qualitative study explored rural healthcare providers’ views on some of the social, cultural, and programmatic factors that influence access to healthcare among their patient populations. We identified five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. This study provides implications that may shift the landscape of a healthcare provider’s approach to delivering healthcare. Further exploration is required to understand the effects these characteristics have on measurable patient-centered outcomes in rural areas.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to individual privacy could be compromised but are available from the corresponding author on reasonable request.

Ethics approval and consent to participate.

All study procedures and methods were carried out in accordance with relevant guidelines and regulations from the World Medical Association Declaration of Helsinki. Ethics approval was given by exempt review from the Institutional Review Board (IRB) at the University of Montana (IRB Protocol No.: 186–20). Participants received oral and written information about the study prior to interview, which allowed them to provide informed consent for the interviews to be recorded and used for qualitative research purposes. No ethical concerns were experienced in this study pertaining to human subjects.

Consent for publication.

The participants consented to the publication of de-identified material from the interviews.

Riley WJ. Health Disparities: Gaps in Access, Quality and Affordability of Medical Care. Trans Am Clin Climatol Assoc. 2012;123:167–74.

PubMed PubMed Central Google Scholar

Levesque J, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12:18. https://doi.org/10.1186/1475-9276-12-18 .

Article PubMed PubMed Central Google Scholar

Serban N. A Multidimensional Framework for Measuring Access. In: Serban N. Healthcare System Access: Measurement, Inference, and Intervention. New Jersey: John Wiley & Sons; 2019. p. 13–59.

Google Scholar

Roncarolo F, Boivin A, Denis JL, et al. What do we know about the needs and challenges of health systems? A scoping review of the international literature. BMC Health Serv Res. 2017;17:636. https://doi.org/10.1186/s12913-017-2585-5 .

Cabrera-Barona P, Blaschke T. Kienberger S. Explaining Accessibility and Satisfaction Related to Healthcare: A Mixed-Methods Approach. Soc Indic Res. 2017;133,719–739. https://doi.org/10.1007/s11205-016-1371-9 .

Coombs NC, Meriwether WE, Caringi J, Newcomer SR. Barriers to healthcare access among U.S. adults with mental health challenges: A population-based study. SSM Popul Health. 2021;15:100847. https://doi.org/10.1016/j.ssmph.2021.100847 .

Farietta TP, Lu B, Tumin R. Ohio’s Medicaid Expansion and Unmet Health Needs Among Low-Income Women of Reproductive Age. Matern Child Health. 2018;22(12):1771–9. https://doi.org/10.1007/s10995-018-2575-1 .

Article Google Scholar

Sommers BD, Blendon RJ, Orav EJ, Epstein AM. Changes in Utilization and Health Among Low-Income Adults After Medicaid Expansion or Expanded Private Insurance. JAMA Intern Med. 2016;1;176(10):1501–1509. https://doi.org/10.1001/jamainternmed.2016.4419 .

National Alliance on Mental Illness: The Doctor is Out. Continuing Disparities in Access to Mental and Physical Health Care. 2017. https://www.nami.org/Support-Education/Publications-Reports/Public-Policy-Reports/The-Doctor-is-Out . Accessed 29 Aug 2021.

Pollitz K, Lopes L, Kearney A, Rae M, Cox C, Fehr F, Rousseau D, Kaiser Family Foundation. US Statistics on Surprise Medical Billing. JAMA. 2020;323(6):498. https://doi.org/10.1001/jama.2020.0065 .

Kahraman C, Orobello C, Cirella GT. hanging Dynamics with COVID-19: Future Outlook. In: XX Cirella GT, editor. Human Settlements: Urbanization, Smart Sector Development, and Future Outlook. Singapore: Springer; 2022. p. 235–52.

Book Google Scholar

Blumenthal D, Fowler EJ, Abrams M, Collins SR. Covid-19 — Implications for the Health Care System. N Engl J Med. 2020;383(15):1483–8. https://doi.org/10.1056/NEJMsb2021088 .

Article CAS PubMed Google Scholar

Defêche J, Azarzar S, Mesdagh A, Dellot P, Tytgat A, Bureau F, Gillet L, Belhadj Y, Bontems S, Hayette M-P, Schils R, Rahmouni S, Ernst M, Moutschen M, Darcis G. In-Depth Longitudinal Comparison of Clinical Specimens to Detect SARS-CoV-2. Pathogens. 2021;10(11):1362. https://doi.org/10.3390/pathogens10111362 .

Article CAS PubMed PubMed Central Google Scholar

Rosenblatt RA, Hart LG. Physicians and rural America. West J Med. 2000;173(5):348–51. https://doi.org/10.1136/ewjm.173.5.348 .

Kaiser Family Foundation Analysis of OECD Data. 2020. https://www.healthsystemtracker.org/chart-collection/u-s-health-care-resources-compare-countries/#item-physicians-density-per-1000-population-2000-2018 . Accessed 31 Oct 2021.

Government Accounting Office. Physician Workforce: Locations and Types of Graduate Training Were Largely Unchanged, and Federal Efforts May Not Be Sufficient to Meet Needs. 2017. https://www.gao.gov/assets/gao-17-411.pdf . Accessed 31 Oct 2021.

The Kaiser Family Foundation: The Uninsured in Rural America. 2003. https://www.kff.org/wp-content/uploads/2013/01/the-uninsured-in-rural-america-update-pdf.pdf . Accessed 15 Sept 2021.

Wright B, Potter AJ, Trivedi AN, Mueller KJ. The Relationship Between Rural Health Clinic Use and Potentially Preventable Hospitalizations and Emergency Department Visits Among Medicare Beneficiaries. J Rural Health. 2018;34(4):423–30. https://doi.org/10.1111/jrh.12253 .

Article PubMed Google Scholar

Weichelt B, Bendixsen C, Patrick T. A Model for Assessing Necessary Conditions for Rural Health Care’s Mobile Health Readiness: Qualitative Assessment of Clinician-Perceived Barriers. JMIR Mhealth Uhealth. 2019;7(11): e11915. https://doi.org/10.2196/11915 .

Mangundu M, Roets L, Janse van Rensberg E. Accessibility of healthcare in rural Zimbabwe: The perspective of nurses and healthcare users. Afr J Prim Health Care Fam Med. 2020;12(1):e1-e7. https://doi.org/10.4102/phcfm.v12i1.2245 .

Rahayu YYS, Araki T, Rosleine D. Factors affecting the use of herbal medicines in the universal health coverage system in Indonesia. J Ethnopharmacol. 2020;260: 112974. https://doi.org/10.1016/j.jep.2020.112974 .

Bolin JN, Bellamy GR, Ferdinand AO, et al. Rural Healthy People 2020: New Decade, Same Challenges. Journal of Rural Health. 2015;31(3):326–33.

Reid S. The rural determinants of health: using critical realism as a theoretical framework. Rural Remote Health. 2019;19(3):5184. https://doi.org/10.22605/RRH5184 .

Wynia MK, Osborn CY. Health literacy and communication quality in health care organizations. J Health Commun. 2010;15 Suppl 2(Suppl 2):102–115. https://doi.org/10.1080/10810730.2010.499981 .

Centers for Disease Control and Prevention. What is Health Literacy? 2022. https://www.cdc.gov/healthliteracy/learn/index.html . Accessed 25 Feb 2022.

Davis TC, Arnold CL, Rademaker A, et al. Differences in barriers to mammography between rural and urban women. J Womens Health (Larchmt). 2012;21(7):748–55. https://doi.org/10.1089/jwh.2011.3397 .

Halverson J, Martinez-Donate A, Trentham-Dietz A, et al. Health literacy and urbanicity among cancer patients. J Rural Health. 2013;29(4):392–402. https://doi.org/10.1111/jrh.12018 .

Zahnd WE, Scaife SL, Francis ML. Health literacy skills in rural and urban populations. Am J Health Behav. 2009;33(5):550–7. https://doi.org/10.5993/ajhb.33.5.8 .

Dogba MJ, Dossa AR, Breton E, Gandonou-Migan R. Using information and communication technologies to involve patients and the public in health education in rural and remote areas: a scoping review. BMC Health Serv Res. 2019;19(1):128. https://doi.org/10.1186/s12913-019-3906-7 .

Committee on the Science of Changing Behavioral Health Social Norms, Board on Behavioral, Cognitive, and Sensory Sciences, Division of Behavioral and Social Sciences and Education; National Academies of Sciences, Engineering, and Medicine. In: Ending Discrimination Against People with Mental and Substance Use Disorders: The Evidence for Stigma Change. Washington: National Academics Press; 2016. p. 33–52.

Wu Y, Zhou H, Wang Q, Cao M, Medina A, Rozelle S. Use of maternal health services among women in the ethnic rural areas of western China. BMC Health Serv Res. 2019;19(1):179. https://doi.org/10.1186/s12913-019-3996-2 .

Meyer E, Hennink M, Rochat R, et al. Working Towards Safe Motherhood: Delays and Barriers to Prenatal Care for Women in Rural and Peri-Urban Areas of Georgia. Matern Child Health J. 2016;20(7):1358–65. https://doi.org/10.1007/s10995-016-1997-x .

Heinrich S. Medical science faces the post-truth era: a plea for the grassroot values of science. Curr Opin Anaesthesiol. 2020;33(2):198–202. https://doi.org/10.1097/ACO.0000000000000833 .

Esquivel MM, Chen JC, Woo RK, et al. Why do patients receive care from a short-term medical mission? Survey study from rural Guatemala. J Surg Res. 2017;215:160–6. https://doi.org/10.1016/j.jss.2017.03.056 .

Park LG, Dracup K, Whooley MA, et al. Symptom Diary Use and Improved Survival for Patients With Heart Failure [published correction appears in Circ Heart Fail. 2017 Dec;10(12):]. Circ Heart Fail. 2017;10(11):e003874. https://doi.org/10.1161/CIRCHEARTFAILURE.117.003874 .

Taleb F, Perkins J, Ali NA, et al. Transforming maternal and newborn health social norms and practices to increase utilization of health services in rural Bangladesh: a qualitative review. BMC Pregnancy Childbirth. 2015;15:75. https://doi.org/10.1186/s12884-015-0501-8 .

Billah SM, Hoque DE, Rahman M, et al. Feasibility of engaging “Village Doctors” in the Community-based Integrated Management of Childhood Illness (C-IMCI): experience from rural Bangladesh. J Glob Health. 2018;8(2): 020413. https://doi.org/10.7189/jogh.08.020413 .

Kaiser JL, Fong RM, Hamer DH, et al. How a woman’s interpersonal relationships can delay care-seeking and access during the maternity period in rural Zambia: An intersection of the Social Ecological Model with the Three Delays Framework. Soc Sci Med. 2019;220:312–21. https://doi.org/10.1016/j.socscimed.2018.11.011 .

Rao KD, Sheffel A. Quality of clinical care and bypassing of primary health centers in India. Soc Sci Med. 2018;207:80–8. https://doi.org/10.1016/j.socscimed.2018.04.040 .

Lee YT, Lee YH, Kaplan WA. Is Taiwan’s National Health Insurance a perfect system? Problems related to health care utilization of the aboriginal population in rural townships. Int J Health Plann Manage. 2019;34(1):e6–10. https://doi.org/10.1002/hpm.2653 .

Lyford M, Haigh MM, Baxi S, Cheetham S, Shahid S, Thompson SC. An Exploration of Underrepresentation of Aboriginal Cancer Patients Attending a Regional Radiotherapy Service in Western Australia. Int J Environ Res Public Health. 2018;15(2):337. https://doi.org/10.3390/ijerph15020337 .

Article PubMed Central Google Scholar

Rohr JM, Spears KL, Geske J, Khandalavala B, Lacey MJ. Utilization of Health Care Resources by the Amish of a Rural County in Nebraska. J Community Health. 2019;44(6):1090–7. https://doi.org/10.1007/s10900-019-00696-9 .

Johnston K, Harvey C, Matich P, et al. Increasing access to sexual health care for rural and regional young people: Similarities and differences in the views of young people and service providers. Aust J Rural Health. 2015;23(5):257–64. https://doi.org/10.1111/ajr.12186 .

Legido-Quigley H, Naheed A, de Silva HA, et al. Patients’ experiences on accessing health care services for management of hypertension in rural Bangladesh, Pakistan and Sri Lanka: A qualitative study. PLoS ONE. 2019;14(1): e0211100. https://doi.org/10.1371/journal.pone.0211100 .

Shaw B, Amouzou A, Miller NP, Bryce J, Surkan PJ. A qualitative exploration of care-seeking pathways for sick children in the rural Oromia region of Ethiopia. BMC Health Serv Res. 2017;17(1):184. https://doi.org/10.1186/s12913-017-2123-5 .

Larson E, Vail D, Mbaruku GM, Kimweri A, Freedman LP, Kruk ME. Moving Toward Patient-Centered Care in Africa: A Discrete Choice Experiment of Preferences for Delivery Care among 3,003 Tanzanian Women. PLoS ONE. 2015;10(8): e0135621. https://doi.org/10.1371/journal.pone.0135621 .

Spleen AM, Lengerich EJ, Camacho FT, Vanderpool RC. Health care avoidance among rural populations: results from a nationally representative survey. J Rural Health. 2014;30(1):79–88. https://doi.org/10.1111/jrh.12032 .

Weisgrau S. Issues in rural health: access, hospitals, and reform. Health Care Financ Rev. 1995;17(1):1–14.

CAS PubMed PubMed Central Google Scholar

National Center for Health Statistics. 2018. https://www.cdc.gov/nchs/surveys.htm . Accessed 29 Aug 2021.

Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res. 2017;17(1):88. https://doi.org/10.1186/s12913-017-2031-8 .

Dyer TA, Owens J, Robinson PG. The acceptability of healthcare: from satisfaction to trust. Community Dent Health. 2016;33(4):242–51. https://doi.org/10.1922/CDH_3902Dyer10 .

Padgett DK. Qualitative and Mixed Methods in Public Health. Thousand Oaks, California: SAGE Publications Inc.; 2012.

Tolley EE, Ulin PR, Mack N, Robinson ET, Succop SM. Qualitative Methods in Public Health: A Field Guide for Applied Research. 2nd ed. San Francisco, California: John Wiley & Sons, Inc.; 2016.

Ingram DD, Franco SJ. 2013 NCHS urban-rural classification scheme for counties. In: National Center for Health Statistics: Vital Health Statistics. 2014. https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdf . Accessed 31 Oct 2021.

Health Resources & Services Administration. Health Professional Shortage Area Find. 2021. https://data.hrsa.gov/tools/shortage-area/hpsa-find . Accessed 31 Oct 2021.

Shaghaghi A, Bhopal RS, Sheikh A. Approaches to Recruiting “Hard-To-Reach” Populations into Re-search: A Review of the Literature. Health Promot Perspect. 2011;1(2):86–94. https://doi.org/10.5681/hpp.2011.009 .

Sadler GR, Lee HC, Lim RS, Fullerton J. Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nurs Health Sci. 2010;12(3):369–74. https://doi.org/10.1111/j.1442-2018.2010.00541.x .

Deterding NM, Waters MC. Flexible Coding of In-depth Interviews: A Twenty-first-century Approach. Sociological Methods & Research. 2021;50(2):708–39. https://doi.org/10.1177/0049124118799377 .

Schreier M, Stamann C, Janssen M, Dahl T, Whittal A. Qualitative Content Analysis: Conceptualizations and Challenges in Research Practice-Introduction to the FQS Special Issue" Qualitative Content Analysis I". InForum Qualitative Sozialforschung/Forum: Qualitative Social Research. 2019;20:26 DEU.

Guest G, Namey E, Chen M. A simple method to assess and report thematic saturation in qualitative research. PLoS ONE. 2020;15(5): e0232076. https://doi.org/10.1371/journal.pone.0232076 .

Mayring P. Qualitative Content Analysis. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research. 2000;1(2). https://doi.org/10.17169/fqs-1.2.1089 .

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Academic Medicine, Vol. 89, No. 9 / Sept 2014. https://doi.org/10.1097/ACM.0000000000000388 .

Carter N, Bryant-Lukosius D, DiCenso A, Blythe J, Neville AJ. The use of triangulation in qualitative research. Oncol Nurs Forum. 2014;41(5):545–7. https://doi.org/10.1188/14.ONF.545-547 .

Miller S. Cultural humility is the first step to becoming global care providers. J Obstet Gynecol Neonatal Nurs. 2009;38(1):92–3. https://doi.org/10.1111/j.1552-6909.2008.00311.x .

Prasad SJ, Nair P, Gadhvi K, Barai I, Danish HS, Philip AB. Cultural humility: treating the patient, not the illness. Med Educ Online. 2016;21:30908. https://doi.org/10.3402/meo.v21.30908 .

George MS, Davey R, Mohanty I, Upton P. “Everything is provided free, but they are still hesitant to access healthcare services”: why does the indigenous community in Attapadi, Kerala continue to experience poor access to healthcare? Int J Equity Health. 2020;19(1):105. https://doi.org/10.1186/s12939-020-01216-1 .

Romanelli M, Hudson KD. Individual and systemic barriers to health care: Perspectives of lesbian, gay, bisexual, and transgender adults. Am J Orthopsychiatry. 2017;87(6):714–28. https://doi.org/10.1037/ort0000306 .

Bailie J, Schierhout G, Laycock A, et al. Determinants of access to chronic illness care: a mixed-methods evaluation of a national multifaceted chronic disease package for Indigenous Australians. BMJ Open. 2015;5(11): e008103. https://doi.org/10.1136/bmjopen-2015-008103 .

Reeve C, Humphreys J, Wakerman J, Carter M, Carroll V, Reeve D. Strengthening primary health care: achieving health gains in a remote region of Australia. Med J Aust. 2015;202(9):483–7. https://doi.org/10.5694/mja14.00894 .

Swan LET, Auerbach SL, Ely GE, Agbemenu K, Mencia J, Araf NR. Family Planning Practices in Appalachia: Focus Group Perspectives on Service Needs in the Context of Regional Substance Abuse. Int J Environ Res Public Health. 2020;17(4):1198. https://doi.org/10.3390/ijerph17041198 .

Kabia E, Mbau R, Oyando R, et al. “We are called the et cetera”: experiences of the poor with health financing reforms that target them in Kenya. Int J Equity Health. 2019;18(1):98. https://doi.org/10.1186/s12939-019-1006-2 .

Ho JW, Kuluski K, Im J. “It’s a fight to get anything you need” - Accessing care in the community from the perspectives of people with multimorbidity. Health Expect. 2017;20(6):1311–9. https://doi.org/10.1111/hex.12571 .

Latif A, Mandane B, Ali A, Ghumra S, Gulzar N. A Qualitative Exploration to Understand Access to Pharmacy Medication Reviews: Views from Marginalized Patient Groups. Pharmacy (Basel). 2020;8(2):73. https://doi.org/10.3390/pharmacy8020073 .

Tschirhart N, Diaz E, Ottersen T. Accessing public healthcare in Oslo, Norway: the experiences of Thai immigrant masseuses. BMC Health Serv Res. 2019;19(1):722. https://doi.org/10.1186/s12913-019-4560-9 .

Ireland S, Belton S, Doran F. “I didn’t feel judged”: exploring women’s access to telemedicine abortion in rural Australia. J Prim Health Care. 2020;12(1):49–56. https://doi.org/10.1071/HC19050 .

Stokes T, Tumilty E, Latu ATF, et al. Improving access to health care for people with severe chronic obstructive pulmonary disease (COPD) in Southern New Zealand: qualitative study of the views of health professional stakeholders and patients. BMJ Open. 2019;9(11): e033524. https://doi.org/10.1136/bmjopen-2019-033524 .

Jafar TH, Ramakrishnan C, John O, et al. Access to CKD Care in Rural Communities of India: a qualitative study exploring the barriers and potential facilitators. BMC Nephrol. 2020;21(1):26. https://doi.org/10.1186/s12882-020-1702-6 .

Doetsch J, Pilot E, Santana P, Krafft T. Potential barriers in healthcare access of the elderly population influenced by the economic crisis and the troika agreement: a qualitative case study in Lisbon, Portugal. Int J Equity Health. 2017;16(1):184. https://doi.org/10.1186/s12939-017-0679-7 .

Engel GL. The need for a new medical model: a challenge for biomedicine. Science. 1977;196(4286):129–36. https://doi.org/10.1126/science.847460 .

Risberg G, Hamberg K, Johansson E.E. Gender perspective in medicine: a vital part of medical scientific rationality. A useful model for comprehending structures and hierarchies within medical science. BMC Med. 2006;4(1):1.

Montana Census & Economic Information Center. 2021. https://ceic.mt.gov/ . Accessed 18 Sept 2021.

Smith M, Saunders R, Stuckhardt L, McGinnis JM, Committee on the Learning Health Care System in America, Institute of Medicine. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington: National Academies Press; 2013.

O’Daniel M, Rosenstein AH, Professional Communication and Team Collaboration. In: Hughes RG, editor. Chapter 33: Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Agency for Healthcare Research and Quality: Rockville; 2008.

Evans RS. Electronic Health Records: Then, Now, and in the Future. Yearb Med Inform. 2016;Suppl 1(Suppl 1):S48-S61. https://doi.org/10.15265/IYS-2016-s006 .

Tikkanen R, Osborn R, Mossialos E, Djordjevic A, Wharton GA. International Health Care System Profiles: United States. The Commonwealth Fund. 2020. https://www.commonwealthfund.org/international-health-policy-center/countries/united-states . Accessed 19 Sept 2021.

Berchick ER, Barnett JC, Upton RD. Health Insurance Coverage in the United States. Washington: United States Census Bureau; 2018.

Brock DW, Buchanan A. Ethical Issues in For-Profit Health Care. In: Gray BH, editor. For-Profit Enterprise in Health Care. Washington: National Academics Press; 1986.

Lekas HM, Pahl K, Fuller Lewis C. Rethinking Cultural Competence: Shifting to Cultural Humility. Health Serv Insights. 2020;13:1178632920970580. https://doi.org/10.1177/1178632920970580 .

Beaulieu MD. Primary and secondary care: Breaking down barriers for our patients with chronic diseases. Can Fam Physician. 2013;59(2):221.

Crafford L, Jenkins LS. Why seek a second consultation at an emergency centre? A qualitative study. Afr J Prim Health Care Fam Med. 2017;9(1):e1–8. https://doi.org/10.4102/phcfm.v9i1.1397 .

Sorensen MJ, von Recklinghausen FM, Fulton G, et al. Secondary overtriage: The burden of unnecessary interfacility transfers in a rural trauma system. JAMA Surg. 2013;148:763–8.

Sutcliffe CG, Thuma PE, van Dijk JH, et al. Use of mobile phones and text messaging to decrease the turnaround time for early infant HIV diagnosis and notification in rural Zambia: an observational study. BMC Pediatr. 2017;17(1):66. https://doi.org/10.1186/s12887-017-0822-z .

Baldwin LM, Morrison C, Griffin J, et al. Bidirectional Text Messaging to Improve Adherence to Recommended Lipid Testing. J Am Board Fam Med. 2017;30(5):608–14. https://doi.org/10.3122/jabfm.2017.05.170088 .

Albright K, Krantz MJ, Backlund Jarquín P, DeAlleaume L, Coronel-Mockler S, Estacio RO. Health Promotion Text Messaging Preferences and Acceptability Among the Medically Underserved. Health Promot Pract. 2015;16(4):523–32. https://doi.org/10.1177/1524839914566850 .

Cramer ME, Mollard EK, Ford AL, Kupzyk KA, Wilson FA. The feasibility and promise of mobile technology with community health worker reinforcement to reduce rural preterm birth. Public Health Nurs. 2018;35(6):508–16. https://doi.org/10.1111/phn.12543 .

Gbadamosi SO, Eze C, Olawepo JO, et al. A Patient-Held Smartcard With a Unique Identifier and an mHealth Platform to Improve the Availability of Prenatal Test Results in Rural Nigeria: Demonstration Study. J Med Internet Res. 2018;20(1): e18. https://doi.org/10.2196/jmir.8716 .

Trondsen MV, Tjora A, Broom A, Scambler G. The symbolic affordances of a video-mediated gaze in emergency psychiatry. Soc Sci Med. 2018;197:87–94. https://doi.org/10.1016/j.socscimed.2017.11.056 .

Schwitters A, Lederer P, Zilversmit L, et al. Barriers to health care in rural Mozambique: a rapid ethnographic assessment of planned mobile health clinics for ART. Glob Health Sci Pract. 2015;3(1):109–16. https://doi.org/10.9745/GHSP-D-14-00145 .

Kojima N, Krupp K, Ravi K, et al. Implementing and sustaining a mobile medical clinic for prenatal care and sexually transmitted infection prevention in rural Mysore, India. BMC Infect Dis. 2017;17(1):189. https://doi.org/10.1186/s12879-017-2282-3 .

Gorman SE, Martinez JM, Olson J. An assessment of HIV treatment outcomes among utilizers of semi-mobile clinics in rural Kenya. AIDS Care. 2015;27(5):665–8. https://doi.org/10.1080/09540121.2014.986053 .

Lee S, Black D, Held ML. Factors Associated with Telehealth Service Utilization among Rural Populations. J Health Care Poor Underserved. 2019;30(4):1259–72. https://doi.org/10.1353/hpu.2019.0104 .

Agha Z, Schapira RM, Maker AH. Cost effectiveness of telemedicine for the delivery of outpatient pulmonary care to a rural population. Telemed J E Health. 2002;8(3):281–91. https://doi.org/10.1089/15305620260353171 .

Doraiswamy S, Abraham A, Mamtani R, Cheema S. Use of Telehealth During the COVID-19 Pandemic: Scoping Review. J Med Internet Res. 2020;22(12): e24087. https://doi.org/10.2196/24087 .

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Acknowledgements

This research was supported by the Center for Biomedical Research Excellence award (P20GM130418) from the National Institute of General Medical Sciences of the National Institute of Health. The first author was also supported by the University of Montana Burnham Population Health Fellowship. We would like to thank Dr. Christopher Dietrich, Dr. Jennifer Robohm and Dr. Eric Arzubi for their contributions on determining inclusion criteria for the healthcare provider population used for this study.

This research did not receive any specific grant from funding agencies in the public, commercial, and not-for-profit sectors.

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Coombs, N.C., Campbell, D.G. & Caringi, J. A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access. BMC Health Serv Res 22 , 438 (2022). https://doi.org/10.1186/s12913-022-07829-2

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Recent advances in mathematical methods for finance

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Giorgia Callegaro 1 ,
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In recent years, Mathematical Finance has witnessed the emergence of new research directions spurred by developments of financial markets, technological advances, and societal challenges. On the one hand, financial markets have seen the introduction of new financial products, regulatory frameworks, and trading infrastructures. On the other hand, artificial intelligence and machine learning techniques are introducing revolutionary changes in numerical methods in finance, overcoming computational challenges considered insurmountable until recently. In addition, new types of risks, such as climate-related and cyber-risks, have gained prominence, significantly impacting financial institutions and society at large.

This special issue on Recent Advances in Mathematical Methods for Finance provides a comprehensive overview of some of the latest developments in Mathematical Finance. We decided to launch this special issue on the occasion of the 10th General AMaMeF Conference, organised by the Guest Editors at the University of Padova and held in a virtual format on June 22–25, 2021. AMaMeF is the acronym for Advanced Mathematical Methods for Finance, and was born as a programme network of the European Science Foundation from 2005 to 2010, under the Sixth Framework Program for research and technological development of the European Union. AMaMeF now represents a European network of research promoting the exchange and diffusion of knowledge in the field of Mathematical Finance, spanning more than 20 countries. The biannual general conference stands as the flagship event of the AMaMeF network. The 10th General AMaMeF Conference spanned a broad range of topics in mathematical finance, including algorithmic trading and financial technologies, asset pricing under market frictions, collateralization and XVA, credit risk and interest rate modeling, energy and commodity markets, equilibrium and principal-agents models, climate risk, green and sustainable finance, machine learning and computational methods in finance, market microstructure, mean-field games and McKean–Vlasov equations, model uncertainty, model risk and robust finance, risk measures, stochastic control and portfolio optimization, stochastic volatility modeling, systemic risk and financial networks. These topics were specifically targeted by the call for papers for the special issue, which was open to the entire scientific community and not restricted to papers presented at the conference.

The special issue contains 44 papers, which underwent a rigorous peer review process under the supervision of the Guest Editors. Coherently with the title of the special issue, in the selection of the submitted papers emphasis was placed on the originality and interest of the mathematical methods employed, alongside the relevance of their financial applications. The selected papers encompass theoretical contributions as well as more applied research, offering a comprehensive view of promising research directions in mathematical finance.

We are thankful to Prof. Endre Boros, Editor-in-Chief of Annals of Operations Research , for giving us the opportunity to edit this special issue and to the Springer staff for their assistance throughout the production process. We are grateful to the referees for their valuable feedback and constructive criticisms, which aided in the selection of the submissions and enhanced the quality of accepted papers. Finally, our most sincere gratitude goes to the authors of the submitted papers, for contributing their work to this special issue. We hope that this collection of papers will stimulate further research on several emerging topics in Mathematical Finance.

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A new method for storing and processing hydrogen chloride

by Japhet Johnstone, Free University of Berlin

Researchers develop new method for storing and processing hydrogen chloride

A research team at Freie Universität Berlin has successfully developed a method for storing and electrolyzing gaseous hydrogen chloride in the form of an ionic liquid. The method allows the hydrogen chloride created as a by-product in traditional chlorination processes to be recovered and recycled in a safer manner.

This development also represents an important contribution toward making the chemical industry more sustainable and offering alternative energy solutions. The results of the study were recently published in the journal Science Advances under the title "Bichloride-Based Ionic Liquids for the Merged Storage, Processing, and Electrolysis of Hydrogen Chloride."

Hydrogen chloride (HCl) is an important by-product of the chemical industry—and can be electrolyzed to generate hydrogen and chlorine . Chlorine is one of the most important base chemicals, required for the production of indispensable polymers such as polyurethanes and polycarbonates, while hydrogen could be the key to the energy of the future.

However, for this processing to occur, HCl must first be transported securely from industrial manufacturing plants to special electrolysis facilities powered by green energy. Due to the technically demanding nature of its secure transportation, such applications for HCl have largely been avoided to date. As such, the potential of this valuable resource has only been partially realized thus far.

Led by inorganic chemistry professor Sebastian Hasenstab-Riedel and in collaboration with partners from Technische Universität Berlin, a team of researchers at Freie Universität Berlin has developed a secure method of storing HCl. By converting it into an ionic liquid, hydrogen chloride can be handled more safely in its anhydrous (water-free) state.

The researchers discovered that HCl gas can be safely bound to the salt triethylmethylammonium chloride to create an ionic liquid called "bichloride" under ambient conditions . It can then also be safely released from this bichloride following transportation or storage. This approach is not only safer than traditional techniques, its electrolysis also promises to be more energy efficient than traditional systems. Moreover, the bichloride can also be used to synthesize other base chemicals that can then be used to manufacture plastics or silicones.

"Chlorine and hydrogen chloride are gaining in importance when it comes to ushering in a more sustainable future for the energy-intensive chemical industry ," says Hasenstab-Riedel. He emphasizes that the technology developed by his team offers ideal conditions for storing and transporting hydrogen chloride in a cost-effective and secure manner.

"This represents an important step toward producing chlorine in a more sustainable way, for example, with electricity produced through renewable energy," says Hasenstab-Riedel. In addition, due to the industrial significance of chlorine, this system could also play an important role in stabilizing the power grid , thus supporting a transition to more sustainable energy practices. The ability to store hydrogen chloride as ionic liquids paves the way for a more sustainable way of performing fundamental chemical processes.

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Quantitative and Qualitative Approaches to Generalization and Replication–A Representationalist View

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se , but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

Introduction

Qualitative and quantitative research strategies have long been treated as opposing paradigms. In recent years, there have been attempts to integrate both strategies. These “mixed methods” approaches treat qualitative and quantitative methodologies as complementary, rather than opposing, strategies (Creswell, 2015 ). However, whilst acknowledging that both strategies have their benefits, this “integration” remains purely pragmatic. Hence, mixed methods methodology does not provide a conceptual unification of the two approaches.

Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007 ). These different standards affect the way researchers handle contradictory empirical findings. For example, many empirical findings in psychology have failed to replicate in recent years (Klein et al., 2014 ; Open Science, Collaboration, 2015 ). This “replication crisis” has been discussed on statistical, theoretical and social grounds and continues to have a wide impact on quantitative research practices like, for example, open science initiatives, pre-registered studies and a re-evaluation of statistical significance testing (Everett and Earp, 2015 ; Maxwell et al., 2015 ; Shrout and Rodgers, 2018 ; Trafimow, 2018 ; Wiggins and Chrisopherson, 2019 ).

However, qualitative research seems to be hardly affected by this discussion. In this paper, we argue that the latter is a direct consequence of how the concept of generalizability is conceived in the two approaches. Whereas most of quantitative psychology is committed to a top-down strategy of generalization based on the idea of random sampling from an abstract population, qualitative studies usually rely on a bottom-up strategy of generalization that is grounded in the successive exploration of the field by means of theoretically sampled cases.

Here, we show that a common methodological framework for qualitative and quantitative research methodologies is possible. We accomplish this by introducing a formal description of quantitative and qualitative models from a representationalist perspective: both approaches can be reconstructed as special kinds of representations for empirical relational structures. We then use this framework to analyze the generalization strategies used in the two approaches. These turn out to be logically independent of the type of model. This has wide implications for psychological research. First, a top-down generalization strategy is compatible with a qualitative modeling approach. This implies that mainstream psychology may benefit from qualitative methods when a numerical representation turns out to be difficult or impossible, without the need to commit to a “qualitative” philosophy of science. Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory.

The Quantitative Strategy–Numbers and Functions

Quantitative science is about finding valid mathematical representations for empirical phenomena. In most cases, these mathematical representations have the form of functional relations between a set of variables. One major challenge of quantitative modeling consists in constructing valid measures for these variables. Formally, to measure a variable means to construct a numerical representation of the underlying empirical relational structure (Krantz et al., 1971 ). For example, take the behaviors of a group of students in a classroom: “to listen,” “to take notes,” and “to ask critical questions.” One may now ask whether is possible to assign numbers to the students, such that the relations between the assigned numbers are of the same kind as the relations between the values of an underlying variable, like e.g., “engagement.” The observed behaviors in the classroom constitute an empirical relational structure, in the sense that for every student-behavior tuple, one can observe whether it is true or not. These observations can be represented in a person × behavior matrix 1 (compare Figure 1 ). Given this relational structure satisfies certain conditions (i.e., the axioms of a measurement model), one can assign numbers to the students and the behaviors, such that the relations between the numbers resemble the corresponding numerical relations. For example, if there is a unique ordering in the empirical observations with regard to which person shows which behavior, the assigned numbers have to constitute a corresponding unique ordering, as well. Such an ordering coincides with the person × behavior matrix forming a triangle shaped relation and is formally represented by a Guttman scale (Guttman, 1944 ). There are various measurement models available for different empirical structures (Suppes et al., 1971 ). In the case of probabilistic relations, Item-Response models may be considered as a special kind of measurement model (Borsboom, 2005 ).

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Constructing a numerical representation from an empirical relational structure; Due to the unique ordering of persons with regard to behaviors (indicated by the triangular shape of the relation), it is possible to construct a Guttman scale by assigning a number to each of the individuals, representing the number of relevant behaviors shown by the individual. The resulting variable (“engagement”) can then be described by means of statistical analyses, like, e.g., plotting the frequency distribution.

Although essential, measurement is only the first step of quantitative modeling. Consider a slightly richer empirical structure, where we observe three additional behaviors: “to doodle,” “to chat,” and “to play.” Like above, one may ask, whether there is a unique ordering of the students with regard to these behaviors that can be represented by an underlying variable (i.e., whether the matrix forms a Guttman scale). If this is the case, we may assign corresponding numbers to the students and call this variable “distraction.” In our example, such a representation is possible. We can thus assign two numbers to each student, one representing his or her “engagement” and one representing his or her “distraction” (compare Figure 2 ). These measurements can now be used to construct a quantitative model by relating the two variables by a mathematical function. In the simplest case, this may be a linear function. This functional relation constitutes a quantitative model of the empirical relational structure under study (like, e.g., linear regression). Given the model equation and the rules for assigning the numbers (i.e., the instrumentations of the two variables), the set of admissible empirical structures is limited from all possible structures to a rather small subset. This constitutes the empirical content of the model 2 (Popper, 1935 ).

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Constructing a numerical model from an empirical relational structure; Since there are two distinct classes of behaviors that each form a Guttman scale, it is possible to assign two numbers to each individual, correspondingly. The resulting variables (“engagement” and “distraction”) can then be related by a mathematical function, which is indicated by the scatterplot and red line on the right hand side.

The Qualitative Strategy–Categories and Typologies

The predominant type of analysis in qualitative research consists in category formation. By constructing descriptive systems for empirical phenomena, it is possible to analyze the underlying empirical structure at a higher level of abstraction. The resulting categories (or types) constitute a conceptual frame for the interpretation of the observations. Qualitative researchers differ considerably in the way they collect and analyze data (Miles et al., 2014 ). However, despite the diverse research strategies followed by different qualitative methodologies, from a formal perspective, most approaches build on some kind of categorization of cases that share some common features. The process of category formation is essential in many qualitative methodologies, like, for example, qualitative content analysis, thematic analysis, grounded theory (see Flick, 2014 for an overview). Sometimes these features are directly observable (like in our classroom example), sometimes they are themselves the result of an interpretative process (e.g., Scheunpflug et al., 2016 ).

In contrast to quantitative methodologies, there have been little attempts to formalize qualitative research strategies (compare, however, Rihoux and Ragin, 2009 ). However, there are several statistical approaches to non-numerical data that deal with constructing abstract categories and establishing relations between these categories (Agresti, 2013 ). Some of these methods are very similar to qualitative category formation on a conceptual level. For example, cluster analysis groups cases into homogenous categories (clusters) based on their similarity on a distance metric.

Although category formation can be formalized in a mathematically rigorous way (Ganter and Wille, 1999 ), qualitative research hardly acknowledges these approaches. 3 However, in order to find a common ground with quantitative science, it is certainly helpful to provide a formal interpretation of category systems.

Let us reconsider the above example of students in a classroom. The quantitative strategy was to assign numbers to the students with regard to variables and to relate these variables via a mathematical function. We can analyze the same empirical structure by grouping the behaviors to form abstract categories. If the aim is to construct an empirically valid category system, this grouping is subject to constraints, analogous to those used to specify a measurement model. The first and most important constraint is that the behaviors must form equivalence classes, i.e., within categories, behaviors need to be equivalent, and across categories, they need to be distinct (formally, the relational structure must obey the axioms of an equivalence relation). When objects are grouped into equivalence classes, it is essential to specify the criterion for empirical equivalence. In qualitative methodology, this is sometimes referred to as the tertium comparationis (Flick, 2014 ). One possible criterion is to group behaviors such that they constitute a set of specific common attributes of a group of people. In our example, we might group the behaviors “to listen,” “to take notes,” and “to doodle,” because these behaviors are common to the cases B, C, and D, and they are also specific for these cases, because no other person shows this particular combination of behaviors. The set of common behaviors then forms an abstract concept (e.g., “moderate distraction”), while the set of persons that show this configuration form a type (e.g., “the silent dreamer”). Formally, this means to identify the maximal rectangles in the underlying empirical relational structure (see Figure 3 ). This procedure is very similar to the way we constructed a Guttman scale, the only difference being that we now use different aspects of the empirical relational structure. 4 In fact, the set of maximal rectangles can be determined by an automated algorithm (Ganter, 2010 ), just like the dimensionality of an empirical structure can be explored by psychometric scaling methods. Consequently, we can identify the empirical content of a category system or a typology as the set of empirical structures that conforms to it. 5 Whereas the quantitative strategy was to search for scalable sub-matrices and then relate the constructed variables by a mathematical function, the qualitative strategy is to construct an empirical typology by grouping cases based on their specific similarities. These types can then be related to one another by a conceptual model that describes their semantic and empirical overlap (see Figure 3 , right hand side).

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Constructing a conceptual model from an empirical relational structure; Individual behaviors are grouped to form abstract types based on them being shared among a specific subset of the cases. Each type constitutes a set of specific commonalities of a class of individuals (this is indicated by the rectangles on the left hand side). The resulting types (“active learner,” “silent dreamer,” “distracted listener,” and “troublemaker”) can then be related to one another to explicate their semantic and empirical overlap, as indicated by the Venn-diagram on the right hand side.

Variable-Based Models and Case-Based Models

In the previous section, we have argued that qualitative category formation and quantitative measurement can both be characterized as methods to construct abstract representations of empirical relational structures. Instead of focusing on different philosophical approaches to empirical science, we tried to stress the formal similarities between both approaches. However, it is worth also exploring the dissimilarities from a formal perspective.

Following the above analysis, the quantitative approach can be characterized by the use of variable-based models, whereas the qualitative approach is characterized by case-based models (Ragin, 1987 ). Formally, we can identify the rows of an empirical person × behavior matrix with a person-space, and the columns with a corresponding behavior-space. A variable-based model abstracts from the single individuals in a person-space to describe the structure of behaviors on a population level. A case-based model, on the contrary, abstracts from the single behaviors in a behavior-space to describe individual case configurations on the level of abstract categories (see Table 1 ).

Variable-based models and case-based models.

From a representational perspective, there is no a priori reason to favor one type of model over the other. Both approaches provide different analytical tools to construct an abstract representation of an empirical relational structure. However, since the two modeling approaches make use of different information (person-space vs. behavior-space), this comes with some important implications for the researcher employing one of the two strategies. These are concerned with the role of deductive and inductive reasoning.

In variable-based models, empirical structures are represented by functional relations between variables. These are usually stated as scientific laws (Carnap, 1928 ). Formally, these laws correspond to logical expressions of the form

In plain text, this means that y is a function of x for all objects i in the relational structure under consideration. For example, in the above example, one may formulate the following law: for all students in the classroom it holds that “distraction” is a monotone decreasing function of “engagement.” Such a law can be used to derive predictions for single individuals by means of logical deduction: if the above law applies to all students in the classroom, it is possible to calculate the expected distraction from a student's engagement. An empirical observation can now be evaluated against this prediction. If the prediction turns out to be false, the law can be refuted based on the principle of falsification (Popper, 1935 ). If a scientific law repeatedly withstands such empirical tests, it may be considered to be valid with regard to the relational structure under consideration.

In case-based models, there are no laws about a population, because the model does not abstract from the cases but from the observed behaviors. A case-based model describes the underlying structure in terms of existential sentences. Formally, this corresponds to a logical expression of the form

In plain text, this means that there is at least one case i for which the condition XYZ holds. For example, the above category system implies that there is at least one active learner. This is a statement about a singular observation. It is impossible to deduce a statement about another person from an existential sentence like this. Therefore, the strategy of falsification cannot be applied to test the model's validity in a specific context. If one wishes to generalize to other cases, this is accomplished by inductive reasoning, instead. If we observed one person that fulfills the criteria of calling him or her an active learner, we can hypothesize that there may be other persons that are identical to the observed case in this respect. However, we do not arrive at this conclusion by logical deduction, but by induction.

Despite this important distinction, it would be wrong to conclude that variable-based models are intrinsically deductive and case-based models are intrinsically inductive. 6 Both types of reasoning apply to both types of models, but on different levels. Based on a person-space, in a variable-based model one can use deduction to derive statements about individual persons from abstract population laws. There is an analogous way of reasoning for case-based models: because they are based on a behavior space, it is possible to deduce statements about singular behaviors. For example, if we know that Peter is an active learner, we can deduce that he takes notes in the classroom. This kind of deductive reasoning can also be applied on a higher level of abstraction to deduce thematic categories from theoretical assumptions (Braun and Clarke, 2006 ). Similarly, there is an analog for inductive generalization from the perspective of variable-based modeling: since the laws are only quantified over the person-space, generalizations to other behaviors rely on inductive reasoning. For example, it is plausible to assume that highly engaged students tend to do their homework properly–however, in our example this behavior has never been observed. Hence, in variable-based models we usually generalize to other behaviors by means of induction. This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains.

Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases. A variable-based approach implies to draw conclusions about cases by means of logical deduction; a case-based approach implies to draw conclusions about cases by means of inductive reasoning. In the following, we build on this distinction to differentiate between qualitative (bottom-up) and quantitative (top-down) strategies of generalization.

Generalization and the Problem of Replication

We will now extend the formal analysis of quantitative and qualitative approaches to the question of generalization and replicability of empirical findings. For this sake, we have to introduce some concepts of formal logic. Formal logic is concerned with the validity of arguments. It provides conditions to evaluate whether certain sentences (conclusions) can be derived from other sentences (premises). In this context, a theory is nothing but a set of sentences (also called axioms). Formal logic provides tools to derive new sentences that must be true, given the axioms are true (Smith, 2020 ). These derived sentences are called theorems or, in the context of empirical science, predictions or hypotheses . On the syntactic level, the rules of logic only state how to evaluate the truth of a sentence relative to its premises. Whether or not sentences are actually true, is formally specified by logical semantics.

On the semantic level, formal logic is intrinsically linked to set-theory. For example, a logical statement like “all dogs are mammals,” is true if and only if the set of dogs is a subset of the set of mammals. Similarly, the sentence “all chatting students doodle” is true if and only if the set of chatting students is a subset of the set of doodling students (compare Figure 3 ). Whereas, the first sentence is analytically true due to the way we define the words “dog” and “mammal,” the latter can be either true or false, depending on the relational structure we actually observe. We can thus interpret an empirical relational structure as the truth criterion of a scientific theory. From a logical point of view, this corresponds to the semantics of a theory. As shown above, variable-based and case-based models both give a formal representation of the same kinds of empirical structures. Accordingly, both types of models can be stated as formal theories. In the variable-based approach, this corresponds to a set of scientific laws that are quantified over the members of an abstract population (these are the axioms of the theory). In the case-based approach, this corresponds to a set of abstract existential statements about a specific class of individuals.

In contrast to mathematical axiom systems, empirical theories are usually not considered to be necessarily true. This means that even if we find no evidence against a theory, it is still possible that it is actually wrong. We may know that a theory is valid in some contexts, yet it may fail when applied to a new set of behaviors (e.g., if we use a different instrumentation to measure a variable) or a new population (e.g., if we draw a new sample).

From a logical perspective, the possibility that a theory may turn out to be false stems from the problem of contingency . A statement is contingent, if it is both, possibly true and possibly false. Formally, we introduce two modal operators: □ to designate logical necessity, and ◇ to designate logical possibility. Semantically, these operators are very similar to the existential quantifier, ∃, and the universal quantifier, ∀. Whereas ∃ and ∀ refer to the individual objects within one relational structure, the modal operators □ and ◇ range over so-called possible worlds : a statement is possibly true, if and only if it is true in at least one accessible possible world, and a statement is necessarily true if and only if it is true in every accessible possible world (Hughes and Cresswell, 1996 ). Logically, possible worlds are mathematical abstractions, each consisting of a relational structure. Taken together, the relational structures of all accessible possible worlds constitute the formal semantics of necessity, possibility and contingency. 7

In the context of an empirical theory, each possible world may be identified with an empirical relational structure like the above classroom example. Given the set of intended applications of a theory (the scope of the theory, one may say), we can now construct possible world semantics for an empirical theory: each intended application of the theory corresponds to a possible world. For example, a quantified sentence like “all chatting students doodle” may be true in one classroom and false in another one. In terms of possible worlds, this would correspond to a statement of contingency: “it is possible that all chatting students doodle in one classroom, and it is possible that they don't in another classroom.” Note that in the above expression, “all students” refers to the students in only one possible world, whereas “it is possible” refers to the fact that there is at least one possible world for each of the specified cases.

To apply these possible world semantics to quantitative research, let us reconsider how generalization to other cases works in variable-based models. Due to the syntactic structure of quantitative laws, we can deduce predictions for singular observations from an expression of the form ∀ i : y i = f ( x i ). Formally, the logical quantifier ∀ ranges only over the objects of the corresponding empirical relational structure (in our example this would refer to the students in the observed classroom). But what if we want to generalize beyond the empirical structure we actually observed? The standard procedure is to assume an infinitely large, abstract population from which a random sample is drawn. Given the truth of the theory, we can deduce predictions about what we may observe in the sample. Since usually we deal with probabilistic models, we can evaluate our theory by means of the conditional probability of the observations, given the theory holds. This concept of conditional probability is the foundation of statistical significance tests (Hogg et al., 2013 ), as well as Bayesian estimation (Watanabe, 2018 ). In terms of possible world semantics, the random sampling model implies that all possible worlds (i.e., all intended applications) can be conceived as empirical sub-structures from a greater population structure. For example, the empirical relational structure constituted by the observed behaviors in a classroom would be conceived as a sub-matrix of the population person × behavior matrix. It follows that, if a scientific law is true in the population, it will be true in all possible worlds, i.e., it will be necessarily true. Formally, this corresponds to an expression of the form

The statistical generalization model thus constitutes a top-down strategy for dealing with individual contexts that is analogous to the way variable-based models are applied to individual cases (compare Table 1 ). Consequently, if we apply a variable-based model to a new context and find out that it does not fit the data (i.e., there is a statistically significant deviation from the model predictions), we have reason to doubt the validity of the theory. This is what makes the problem of low replicability so important: we observe that the predictions are wrong in a new study; and because we apply a top-down strategy of generalization to contexts beyond the ones we observed, we see our whole theory at stake.

Qualitative research, on the contrary, follows a different strategy of generalization. Since case-based models are formulated by a set of context-specific existential sentences, there is no need for universal truth or necessity. In contrast to statistical generalization to other cases by means of random sampling from an abstract population, the usual strategy in case-based modeling is to employ a bottom-up strategy of generalization that is analogous to the way case-based models are applied to individual cases. Formally, this may be expressed by stating that the observed qualia exist in at least one possible world, i.e., the theory is possibly true:

This statement is analogous to the way we apply case-based models to individual cases (compare Table 1 ). Consequently, the set of intended applications of the theory does not follow from a sampling model, but from theoretical assumptions about which cases may be similar to the observed cases with respect to certain relevant characteristics. For example, if we observe that certain behaviors occur together in one classroom, following a bottom-up strategy of generalization, we will hypothesize why this might be the case. If we do not replicate this finding in another context, this does not question the model itself, since it was a context-specific theory all along. Instead, we will revise our hypothetical assumptions about why the new context is apparently less similar to the first one than we originally thought. Therefore, if an empirical finding does not replicate, we are more concerned about our understanding of the cases than about the validity of our theory.

Whereas statistical generalization provides us with a formal (and thus somehow more objective) apparatus to evaluate the universal validity of our theories, the bottom-up strategy forces us to think about the class of intended applications on theoretical grounds. This means that we have to ask: what are the boundary conditions of our theory? In the above classroom example, following a bottom-up strategy, we would build on our preliminary understanding of the cases in one context (e.g., a public school) to search for similar and contrasting cases in other contexts (e.g., a private school). We would then re-evaluate our theoretical description of the data and explore what makes cases similar or dissimilar with regard to our theory. This enables us to expand the class of intended applications alongside with the theory.

Of course, none of these strategies is superior per se . Nevertheless, they rely on different assumptions and may thus be more or less adequate in different contexts. The statistical strategy relies on the assumption of a universal population and invariant measurements. This means, we assume that (a) all samples are drawn from the same population and (b) all variables refer to the same behavioral classes. If these assumptions are true, statistical generalization is valid and therefore provides a valuable tool for the testing of empirical theories. The bottom-up strategy of generalization relies on the idea that contexts may be classified as being more or less similar based on characteristics that are not part of the model being evaluated. If such a similarity relation across contexts is feasible, the bottom-up strategy is valid, as well. Depending on the strategy of generalization, replication of empirical research serves two very different purposes. Following the (top-down) principle of generalization by deduction from scientific laws, replications are empirical tests of the theory itself, and failed replications question the theory on a fundamental level. Following the (bottom-up) principle of generalization by induction to similar contexts, replications are a means to explore the boundary conditions of a theory. Consequently, failed replications question the scope of the theory and help to shape the set of intended applications.

We have argued that quantitative and qualitative research are best understood by means of the structure of the employed models. Quantitative science mainly relies on variable-based models and usually employs a top-down strategy of generalization from an abstract population to individual cases. Qualitative science prefers case-based models and usually employs a bottom-up strategy of generalization. We further showed that failed replications have very different implications depending on the underlying strategy of generalization. Whereas in the top-down strategy, replications are used to test the universal validity of a model, in the bottom-up strategy, replications are used to explore the scope of a model. We will now address the implications of this analysis for psychological research with regard to the problem of replicability.

Modern day psychology almost exclusively follows a top-down strategy of generalization. Given the quantitative background of most psychological theories, this is hardly surprising. Following the general structure of variable-based models, the individual case is not the focus of the analysis. Instead, scientific laws are stated on the level of an abstract population. Therefore, when applying the theory to a new context, a statistical sampling model seems to be the natural consequence. However, this is not the only possible strategy. From a logical point of view, there is no reason to assume that a quantitative law like ∀ i : y i = f ( x i ) implies that the law is necessarily true, i.e.,: □(∀ i : y i = f ( x i )). Instead, one might just as well define the scope of the theory following an inductive strategy. 8 Formally, this would correspond to the assumption that the observed law is possibly true, i.e.,: ◇(∀ i : y i = f ( x i )). For example, we may discover a functional relation between “engagement” and “distraction” without referring to an abstract universal population of students. Instead, we may hypothesize under which conditions this functional relation may be valid and use these assumptions to inductively generalize to other cases.

If we take this seriously, this would require us to specify the intended applications of the theory: in which contexts do we expect the theory to hold? Or, equivalently, what are the boundary conditions of the theory? These boundary conditions may be specified either intensionally, i.e., by giving external criteria for contexts being similar enough to the ones already studied to expect a successful application of the theory. Or they may be specified extensionally, by enumerating the contexts where the theory has already been shown to be valid. These boundary conditions need not be restricted to the population we refer to, but include all kinds of contextual factors. Therefore, adopting a bottom-up strategy, we are forced to think about these factors and make them an integral part of our theories.

In fact, there is good reason to believe that bottom-up generalization may be more adequate in many psychological studies. Apart from the pitfalls associated with statistical generalization that have been extensively discussed in recent years (e.g., p-hacking, underpowered studies, publication bias), it is worth reflecting on whether the underlying assumptions are met in a particular context. For example, many samples used in experimental psychology are not randomly drawn from a large population, but are convenience samples. If we use statistical models with non-random samples, we have to assume that the observations vary as if drawn from a random sample. This may indeed be the case for randomized experiments, because all variation between the experimental conditions apart from the independent variable will be random due to the randomization procedure. In this case, a classical significance test may be regarded as an approximation to a randomization test (Edgington and Onghena, 2007 ). However, if we interpret a significance test as an approximate randomization test, we test not for generalization but for internal validity. Hence, even if we use statistical significance tests when assumptions about random sampling are violated, we still have to use a different strategy of generalization. This issue has been discussed in the context of small-N studies, where variable-based models are applied to very small samples, sometimes consisting of only one individual (Dugard et al., 2012 ). The bottom-up strategy of generalization that is employed by qualitative researchers, provides such an alternative.

Another important issue in this context is the question of measurement invariance. If we construct a variable-based model in one context, the variables refer to those behaviors that constitute the underlying empirical relational structure. For example, we may construct an abstract measure of “distraction” using the observed behaviors in a certain context. We will then use the term “distraction” as a theoretical term referring to the variable we have just constructed to represent the underlying empirical relational structure. Let us now imagine we apply this theory to a new context. Even if the individuals in our new context are part of the same population, we may still get into trouble if the observed behaviors differ from those used in the original study. How do we know whether these behaviors constitute the same variable? We have to ensure that in any new context, our measures are valid for the variables in our theory. Without a proper measurement model, this will be hard to achieve (Buntins et al., 2017 ). Again, we are faced with the necessity to think of the boundary conditions of our theories. In which contexts (i.e., for which sets of individuals and behaviors) do we expect our theory to work?

If we follow the rationale of inductive generalization, we can explore the boundary conditions of a theory with every new empirical study. We thus widen the scope of our theory by comparing successful applications in different contexts and unsuccessful applications in similar contexts. This may ultimately lead to a more general theory, maybe even one of universal scope. However, unless we have such a general theory, we might be better off, if we treat unsuccessful replications not as a sign of failure, but as a chance to learn.

Author Contributions

MB conceived the original idea and wrote the first draft of the paper. MS helped to further elaborate and scrutinize the arguments. All authors contributed to the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Annette Scheunpflug for helpful comments on an earlier version of the manuscript.

1 A person × behavior matrix constitutes a very simple relational structure that is common in psychological research. This is why it is chosen here as a minimal example. However, more complex structures are possible, e.g., by relating individuals to behaviors over time, with individuals nested within groups etc. For a systematic overview, compare Coombs ( 1964 ).

2 This notion of empirical content applies only to deterministic models. The empirical content of a probabilistic model consists in the probability distribution over all possible empirical structures.

3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick ( 2014 ) nor the Oxford Handbook of Qualitative Research edited by Leavy ( 2014 ) mention formal approaches to category formation.

4 Note also that the described structure is empirically richer than a nominal scale. Therefore, a reduction of qualitative category formation to be a special (and somehow trivial) kind of measurement is not adequate.

5 It is possible to extend this notion of empirical content to the probabilistic case (this would correspond to applying a latent class analysis). But, since qualitative research usually does not rely on formal algorithms (neither deterministic nor probabilistic), there is currently little practical use of such a concept.

6 We do not elaborate on abductive reasoning here, since, given an empirical relational structure, the concept can be applied to both types of models in the same way (Schurz, 2008 ). One could argue that the underlying relational structure is not given a priori but has to be constructed by the researcher and will itself be influenced by theoretical expectations. Therefore, abductive reasoning may be necessary to establish an empirical relational structure in the first place.

7 We shall not elaborate on the metaphysical meaning of possible worlds here, since we are only concerned with empirical theories [but see Tooley ( 1999 ), for an overview].

8 Of course, this also means that it would be equally reasonable to employ a top-down strategy of generalization using a case-based model by postulating that □(∃ i : XYZ i ). The implications for case-based models are certainly worth exploring, but lie beyond the scope of this article.

Agresti A. (2013). Categorical Data Analysis, 3rd Edn. Wiley Series In Probability And Statistics . Hoboken, NJ: Wiley. [ Google Scholar ]
Borsboom D. (2005). Measuring the Mind: Conceptual Issues in Contemporary Psychometrics . Cambridge: Cambridge University Press; 10.1017/CBO9780511490026 [ CrossRef ] [ Google Scholar ]
Braun V., Clarke V. (2006). Using thematic analysis in psychology . Qual. Res. Psychol . 3 , 77–101. 10.1191/1478088706qp063oa [ CrossRef ] [ Google Scholar ]
Buntins M., Buntins K., Eggert F. (2017). Clarifying the concept of validity: from measurement to everyday language . Theory Psychol. 27 , 703–710. 10.1177/0959354317702256 [ CrossRef ] [ Google Scholar ]
Carnap R. (1928). The Logical Structure of the World . Berkeley, CA: University of California Press. [ Google Scholar ]
Coombs C. H. (1964). A Theory of Data . New York, NY: Wiley. [ Google Scholar ]
Creswell J. W. (2015). A Concise Introduction to Mixed Methods Research . Los Angeles, CA: Sage. [ Google Scholar ]
Dugard P., File P., Todman J. B. (2012). Single-Case and Small-N Experimental Designs: A Practical Guide to Randomization Tests 2nd Edn . New York, NY: Routledge; 10.4324/9780203180938 [ CrossRef ] [ Google Scholar ]
Edgington E., Onghena P. (2007). Randomization Tests, 4th Edn. Statistics. Hoboken, NJ: CRC Press; 10.1201/9781420011814 [ CrossRef ] [ Google Scholar ]
Everett J. A. C., Earp B. D. (2015). A tragedy of the (academic) commons: interpreting the replication crisis in psychology as a social dilemma for early-career researchers . Front. Psychol . 6 :1152. 10.3389/fpsyg.2015.01152 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Flick U. (Ed.). (2014). The Sage Handbook of Qualitative Data Analysis . London: Sage; 10.4135/9781446282243 [ CrossRef ] [ Google Scholar ]
Freeman M., Demarrais K., Preissle J., Roulston K., St. Pierre E. A. (2007). Standards of evidence in qualitative research: an incitement to discourse . Educ. Res. 36 , 25–32. 10.3102/0013189X06298009 [ CrossRef ] [ Google Scholar ]
Ganter B. (2010). Two basic algorithms in concept analysis , in Lecture Notes In Computer Science. Formal Concept Analysis, Vol. 5986 , eds Hutchison D., Kanade T., Kittler J., Kleinberg J. M., Mattern F., Mitchell J. C., et al. (Berlin, Heidelberg: Springer Berlin Heidelberg; ), 312–340. 10.1007/978-3-642-11928-6_22 [ CrossRef ] [ Google Scholar ]
Ganter B., Wille R. (1999). Formal Concept Analysis . Berlin, Heidelberg: Springer Berlin Heidelberg; 10.1007/978-3-642-59830-2 [ CrossRef ] [ Google Scholar ]
Guttman L. (1944). A basis for scaling qualitative data . Am. Sociol. Rev . 9 :139 10.2307/2086306 [ CrossRef ] [ Google Scholar ]
Hogg R. V., Mckean J. W., Craig A. T. (2013). Introduction to Mathematical Statistics, 7th Edn . Boston, MA: Pearson. [ Google Scholar ]
Hughes G. E., Cresswell M. J. (1996). A New Introduction To Modal Logic . London; New York, NY: Routledge; 10.4324/9780203290644 [ CrossRef ] [ Google Scholar ]
Klein R. A., Ratliff K. A., Vianello M., Adams R. B., Bahník Š., Bernstein M. J., et al. (2014). Investigating variation in replicability . Soc. Psychol. 45 , 142–152. 10.1027/1864-9335/a000178 [ CrossRef ] [ Google Scholar ]
Krantz D. H., Luce D., Suppes P., Tversky A. (1971). Foundations of Measurement Volume I: Additive And Polynomial Representations . New York, NY; London: Academic Press; 10.1016/B978-0-12-425401-5.50011-8 [ CrossRef ] [ Google Scholar ]
Leavy P. (2014). The Oxford Handbook of Qualitative Research . New York, NY: Oxford University Press; 10.1093/oxfordhb/9780199811755.001.0001 [ CrossRef ] [ Google Scholar ]
Maxwell S. E., Lau M. Y., Howard G. S. (2015). Is psychology suffering from a replication crisis? what does “failure to replicate” really mean? Am. Psychol. 70 , 487–498. 10.1037/a0039400 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Miles M. B., Huberman A. M., Saldaña J. (2014). Qualitative Data Analysis: A Methods Sourcebook, 3rd Edn . Los Angeles, CA; London; New Delhi; Singapore; Washington, DC: Sage. [ Google Scholar ]
Open Science, Collaboration (2015). Estimating the reproducibility of psychological science . Science 349 :Aac4716. 10.1126/science.aac4716 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Popper K. (1935). Logik Der Forschung . Wien: Springer; 10.1007/978-3-7091-4177-9 [ CrossRef ] [ Google Scholar ]
Ragin C. (1987). The Comparative Method : Moving Beyond Qualitative and Quantitative Strategies . Berkeley, CA: University Of California Press. [ Google Scholar ]
Rihoux B., Ragin C. (2009). Configurational Comparative Methods: Qualitative Comparative Analysis (Qca) And Related Techniques . Thousand Oaks, CA: Sage Publications, Inc; 10.4135/9781452226569 [ CrossRef ] [ Google Scholar ]
Scheunpflug A., Krogull S., Franz J. (2016). Understanding learning in world society: qualitative reconstructive research in global learning and learning for sustainability . Int. Journal Dev. Educ. Glob. Learn. 7 , 6–23. 10.18546/IJDEGL.07.3.02 [ CrossRef ] [ Google Scholar ]
Schurz G. (2008). Patterns of abduction . Synthese 164 , 201–234. 10.1007/s11229-007-9223-4 [ CrossRef ] [ Google Scholar ]
Shrout P. E., Rodgers J. L. (2018). Psychology, science, and knowledge construction: broadening perspectives from the replication crisis . Annu. Rev. Psychol . 69 , 487–510. 10.1146/annurev-psych-122216-011845 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Smith P. (2020). An Introduction To Formal Logic . Cambridge: Cambridge University Press. 10.1017/9781108328999 [ CrossRef ] [ Google Scholar ]
Suppes P., Krantz D. H., Luce D., Tversky A. (1971). Foundations of Measurement Volume II: Geometrical, Threshold, and Probabilistic Representations . New York, NY; London: Academic Press. [ Google Scholar ]
Tooley M. (Ed.). (1999). Necessity and Possibility. The Metaphysics of Modality . New York, NY; London: Garland Publishing. [ Google Scholar ]
Trafimow D. (2018). An a priori solution to the replication crisis . Philos. Psychol . 31 , 1188–1214. 10.1080/09515089.2018.1490707 [ CrossRef ] [ Google Scholar ]
Watanabe S. (2018). Mathematical Foundations of Bayesian Statistics. CRC Monographs On Statistics And Applied Probability . Boca Raton, FL: Chapman And Hall. [ Google Scholar ]
Wiggins B. J., Chrisopherson C. D. (2019). The replication crisis in psychology: an overview for theoretical and philosophical psychology . J. Theor. Philos. Psychol. 39 , 202–217. 10.1037/teo0000137 [ CrossRef ] [ Google Scholar ]

Analytical Methods

Establishing new methodology for determining water absorbability of cellulose-derived materials via vapor-monitoring headspace strategy.

In this research, for the first time, we introduce a vapor-monitoring headspace strategy to establish a new methodology for determining water absorbability of cellulose-derived materials. The method involves detecting the water in the gas phase from various cellulose-derived materials after achieving the equilibrium state. By utilizing the headspace technique to monitor the change in water vapor pressure from bound water to free water, a change point indicating the water absorbability can be identified through fitting procedures. The study showed that the newly-established method possesses high precision (relative standard deviation ≤ 2.92%) and accuracy (relative differences ≤ 5.74%) for water absorbability analysis. The present method emerges as a facile and reliable tool for measuring water absorbability, and the introduction of the vapor-monitoring headspace strategy is anticipated to inspire the development of a new type of analytical method.

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Use of Abortion Pills Has Risen Significantly Post Roe, Research Shows

By Pam Belluck

Pam Belluck has been reporting about reproductive health for over a decade.

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On the eve of oral arguments in a Supreme Court case that could affect future access to abortion pills, new research shows the fast-growing use of medication abortion nationally and the many ways women have obtained access to the method since Roe v. Wade was overturned in June 2022.

The Details

A person pours pills out of a bottle into a gloved hand.

A study, published on Monday in the medical journal JAMA , found that the number of abortions using pills obtained outside the formal health system soared in the six months after the national right to abortion was overturned. Another report, published last week by the Guttmacher Institute , a research organization that supports abortion rights, found that medication abortions now account for nearly two-thirds of all abortions provided by the country’s formal health system, which includes clinics and telemedicine abortion services.

The JAMA study evaluated data from overseas telemedicine organizations, online vendors and networks of community volunteers that generally obtain pills from outside the United States. Before Roe was overturned, these avenues provided abortion pills to about 1,400 women per month, but in the six months afterward, the average jumped to 5,900 per month, the study reported.

Overall, the study found that while abortions in the formal health care system declined by about 32,000 from July through December 2022, much of that decline was offset by about 26,000 medication abortions from pills provided by sources outside the formal health system.

“We see what we see elsewhere in the world in the U.S. — that when anti-abortion laws go into effect, oftentimes outside of the formal health care setting is where people look, and the locus of care gets shifted,” said Dr. Abigail Aiken, who is an associate professor at the University of Texas at Austin and the lead author of the JAMA study.

The co-authors were a statistics professor at the university; the founder of Aid Access, a Europe-based organization that helped pioneer telemedicine abortion in the United States; and a leader of Plan C, an organization that provides consumers with information about medication abortion. Before publication, the study went through the rigorous peer review process required by a major medical journal.

The telemedicine organizations in the study evaluated prospective patients using written medical questionnaires, issued prescriptions from doctors who were typically in Europe and had pills shipped from pharmacies in India, generally charging about $100. Community networks typically asked for some information about the pregnancy and either delivered or mailed pills with detailed instructions, often for free.

Online vendors, which supplied a small percentage of the pills in the study and charged between $39 and $470, generally did not ask for women’s medical history and shipped the pills with the least detailed instructions. Vendors in the study were vetted by Plan C and found to be providing genuine abortion pills, Dr. Aiken said.

The Guttmacher report, focusing on the formal health care system, included data from clinics and telemedicine abortion services within the United States that provided abortion to patients who lived in or traveled to states with legal abortion between January and December 2023.

It found that pills accounted for 63 percent of those abortions, up from 53 percent in 2020. The total number of abortions in the report was over a million for the first time in more than a decade.

Why This Matters

Overall, the new reports suggest how rapidly the provision of abortion has adjusted amid post-Roe abortion bans in 14 states and tight restrictions in others.

The numbers may be an undercount and do not reflect the most recent shift: shield laws in six states allowing abortion providers to prescribe and mail pills to tens of thousands of women in states with bans without requiring them to travel. Since last summer, for example, Aid Access has stopped shipping medication from overseas and operating outside the formal health system; it is instead mailing pills to states with bans from within the United States with the protection of shield laws.

What’s Next

In the case that will be argued before the Supreme Court on Tuesday, the plaintiffs, who oppose abortion, are suing the Food and Drug Administration, seeking to block or drastically limit the availability of mifepristone, the first pill in the two-drug medication abortion regimen.

The JAMA study suggests that such a ruling could prompt more women to use avenues outside the formal American health care system, such as pills from other countries.

“There’s so many unknowns about what will happen with the decision,” Dr. Aiken said.

She added: “It’s possible that a decision by the Supreme Court in favor of the plaintiffs could have a knock-on effect where more people are looking to access outside the formal health care setting, either because they’re worried that access is going away or they’re having more trouble accessing the medications.”

Pam Belluck is a health and science reporter, covering a range of subjects, including reproductive health, long Covid, brain science, neurological disorders, mental health and genetics. More about Pam Belluck

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Volume 30, Number 5—May 2024

Identifying Contact Time Required for Secondary Transmission of Clostridioides difficile Infections by Using Real-Time Locating System

Ethics Statement

This study was approved by the Institutional Review Board of the Yonsei University Health System Clinical Trial Centre, and the study protocol adhered to the tenets of the Declaration of Helsinki (approval no. 9–2022–0209; approved on February 24, 2023). Because this was a retrospective study, the Institutional Review Board waived the requirement for written informed consent from the study participants.

Study Design and Participants

We conducted a retrospective cohort study involving hospitalized patients who had direct or indirect contact with index patients who had a CDI diagnosis during September–December 2021. The study concluded on July 29, 2022, when information from the last enrolled patient was acquired. CDI was diagnosed by using PCR, which detected the C. difficile toxin B gene, and by identifying C. difficile in fecal culture samples obtained from patients experiencing diarrhea ( Appendix 1 ). Diarrhea was defined as new-onset bowel movements >3 times per day. Yongin Severance Hospital is a university-affiliated hospital that has 560 beds; 46.7% (86/184) of rooms have 4–5 beds. Patients in the same room shared toilets, except in the intensive care unit, where most patient beds were isolated, eliminating the need for shared toilets. After discharge, the rooms were cleaned with nonsporicidal disinfectants. Although patients with CDI were not placed under specific contact isolation, the hospital used enhanced standard infection control measures throughout the hospital because of the COVID-19 pandemic, which included encouraging regular handwashing with soap and water and mask use. CDI index patients were not isolated as a contact precaution during hospitalization; their baseline characteristics were recorded ( Appendix 1 Table 1 ).

We tracked CDI contact cases by using 3 different methods. First, we investigated patients who came in direct contact with CDI index patients by using RTLS. We considered patients within a 1-meter radius of index patients to have had direct contact, regardless of the duration. Second, we collected data for patients who came in indirect contact with CDI index patients via healthcare personnel. We used RTLS to identify contact cases where patients interacted with healthcare personnel who had attended to an index patients for >24 hours. We assumed the disease could be potentially transmitted through healthcare workers’ hands or through fomites, such as blood pressure cuffs. We systematically calculated contact duration for the entire hospitalization period, irrespective of the presumed contagiousness of the index patient. We adopted this approach to ensure the comprehensive inclusion of patients susceptible to transmission during the asymptomatic phase of the index patient. Third, we identified CDI cases arising from indirect contact through environmental contaminants. We enrolled patients who were hospitalized in the same rooms as index patients within 3 months after the index patient’s discharge. The patients were followed up until their last outpatient visit or hospitalization. We tracked diarrhea symptoms and obtained results for C. difficile toxin B gene PCR and for the fecal C. difficile culture tests from electronic medical records.

The primary outcome was secondary CDI transmission, identified by whole-genome sequencing. We performed PCR ribotyping for all C. difficile isolates obtained from the patients with a CDI diagnosis. Among the designated contacts, we sequenced whole genomes of paired C. difficile samples from patients harboring identical ribotypes. We determined person-to-person transmission by examining the genetic relatedness of isolates to reveal consistent core genome sequence types and substantial allelic homogeneity. We excluded index patients with a history of CDI within 3 months before the study period, contact case-patients with a history of diarrhea but without laboratory tests to confirm CDI, and contact case-patients who had a short follow-up period of <7 days.

PCR Ribotyping

We performed PCR ribotyping by using the primers CD1–CD1445 ( 14 , 15 ). We compared PCR ribotyping patterns with those of known standard C. difficile strains (VPI10463, UK078, 48489ATCC9689, ATCC43598, and ATCC70057). We considered ribotype patterns with > 1 band difference to be different ribotypes.

Whole-Genome Sequencing

We generated sequencing libraries for C. difficile genomic DNA by using Twist Library Preparation EF 2.0 Kit and Twist UDI Primers (Twist Bioscience, https://www.twistbioscience.com ) according to the manufacturer’s instructions. We extracted genomic DNA by using the chemagic 360 extraction instrument and chemagic DNA Tissue Kit (both PerkinElmer, https://www.perkinelmer.com ). We assessed the quantity of DNA in the libraries by using Qubit 3.0 and the Qubit dsDNA HS Assay Kit (ThermoFisher Scientific, https://www.thermofisher.com ) and assessed quality by using the 4200 TapeStation and DNA1000 ScreenTape (Agilent, https://www.agilent.com ). We used the quantified final library products for cluster generation and performed next-generation sequencing on an Illumina NovaSeq 6000 sequencer system (Illumina, https://www.illumina.com ) in 300-bp paired-end format according to the Illumina paired-end sequencing protocol. We performed de novo assembly of sequences by using Unicycler version 0.4.8 ( https://github.com/rrwick/Unicycler ) and analyzed core genomic multilocus sequence typing by using EnteroBase ( https://enterobase.warwick.ac.uk ).

Among isolate pairs with the same ribotype, 2 pairs of identical core genomic sequence types had allelic differences of 9 and 13. We distinguished between secondary and nonsecondary transmission according to the distribution of allelic differences among pairs of identical ribotypes (104 [interquartile range 27–1,709] differences). The probability of genetic homogeneity was statistically significant for the same core genomic sequence types with allelic differences < 13 (p = 0.010).

Contact Tracing with Real-Time Locating System

The hospital used RTLS sensors designed to detect signals within a 2-meter radius in bedrooms and within a 10-meter radius in open spaces throughout the facility. Hospital staff and inpatients were required to always wear the RTLS tags. The tags emitted signals every 1–3 seconds, confirming the presence of the person in a specific room. The distance between persons was calculated through a tag-to-tag signal interaction. When 2 persons were at a particular distance from each other, the contact time between them was counted, enabling data collection for the cumulative contact time between the 2 persons.

Statistical Analysis

We used a generalized linear mixed model and a logit link function to model CDI occurrence. The fixed effects in the model encompassed various factors, including age, prior hospitalization, recent antimicrobial use, the elapsed time before treatment of the CDI index patient, comorbidities (diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, hypertension, chronic kidney disease, malignancy, inflammatory bowel disease, cerebrovascular accident, and hematopoietic stem cell transplantation), Charlson comorbidity index scores, categorized leukocyte counts, serum levels of C-reactive protein and albumin, presence of ileostomy, insertion of enteral tube, and contact type. In addition, the model incorporated random intercepts for time and an unstructured covariance matrix. For the generalized linear mixed model, only variables demonstrating an effect on CDI occurrence were selected as fixed effects from baseline data. We conducted a univariate logistic regression to determine the influence of each variable on secondary transmission within the group that developed subsequent CDI. For analysis of categorical variables, we used frequencies and percentages for descriptions; for continuous variables, we used means and SDs. We performed statistical analyses and created graphs by using both SPSS Statistics 26.0 (IBM Corp, https://www.ibm.com ) and R version 4.2.2 (The R Project for Statistical Computing, https://www.r-project.org ). We conducted all statistical tests with a significance level set at 0.05.

Patient Characteristics

Adherence to wearing the RTLS tags was 91.3% (interquartile range 90.5%–92.6%) during the study. We identified 4,196 contact cases for 26 index patients, of which 490 were excluded because of short follow-up periods and 86 were excluded because of a lack of laboratory results, despite a history of diarrhea. A disparity emerged between the number of contact cases and number of contact patients because 744 contact patients experienced >2 episodes of contact with separate index patients. Consequently, we defined instances of contact as contact cases and persons who experienced contact episodes as patients. Among the remaining 3,620 contact cases (comprising 2,520 patients), 2,587 (71.5%) cases were followed up for >30 days. The number of contact cases attributed to direct contact was 909/3,620 (25.1%); 2,711 contact cases resulted from indirect contact occurring either through healthcare personnel (421/3,620 [11.6%]) or through environmental exposure (2,290/3,620 [63.3%]) ( Appendix 1 Figure 1). Within the subset of 909 direct contact cases, 181 (19.9%) instances involved patients who shared a bedroom with an index patient for >24 hours; 728 (80.1%) contact cases involved diverse encounters, such as during radiologic exams, rehabilitation, physiotherapy, or brief encounters occurring within the confines of the same bedroom. Furthermore, 316 (34.8%) direct contact cases were identified when the index patients exhibited symptoms of diarrhea, whereas 593 (65.2%) contact cases were identified during an index patient’s asymptomatic phase ( Table 1 ).

The mean age ( + SD) of the 2,520 contact patients was 60.37 ( + 19.76) years; 53.3% (1,343) were men and 46.7% (1,177) women. We identified a history of hospitalization in 15.1% and recent antimicrobial use in 66.9% of all contact patients. Among contact patients, 4.1% (104) received hematopoietic stem cell transplantation, whereas 25.4% (640) had a history of malignancies. Only 1 patient with a history of inflammatory bowel disease was included in the study. All index patients underwent treatment for CDI, which was initiated ≈1 day after identifying the infection. The median contact time was 4,320 (interquartile range 131.5–8,640) minutes. Among the 2,520 patients that had follow-up, CDI was diagnosed in 58 patients. Recent antimicrobial use was greater (96.4%) for patients with a subsequent CDI diagnosis than for those without a subsequent CDI diagnosis (66.2%; p = 0.047) ( Table 1 ; Appendix 1 Table 2). We identified ribotypes of C. difficile isolates from index patients and from contact patients who had a subsequent CDI diagnosis ( Appendix 1 Figure 2). Ribotype 014/016 had the highest (23.1%) prevalence, whereas ribotype 018 had a lower (8.9%) prevalence than previously described ( 16 ).

Identifying Secondary Transmission of C . difficile Infection

Of 126 contact cases involving 58 patients with a subsequent CDI diagnosis, 13 contact cases (11 patients) had the same C. difficile ribotype as their index patient. Two patients had secondary transmission of C. difficile ; each was associated with a distinct index patient. One secondary transmission occurred through direct contact, whereas the other occurred via exposure to environmental sources (2 of 3,620 cases; 0.05% incidence rate). The mean age of patients with secondary transmission (81.50 + 2.12 years) was greater than that of patients with nonsecondary transmission (73.38 + 11.58 years; p<0.001) ( Table 1 ).

The patient who had secondary C. difficile transmission through direct contact with an index patient did not cohabit in the same room. The contact duration was 113 minutes and occurred during the asymptomatic phase of the index patient. The patient with indirect environmental contact was hospitalized 36 days after discharge of the index patient; the contact time was 11 days ( Table 2 ). Neither patient had a hospitalization history; however, they both had a history of recent antimicrobial use and insertion of an enteral tube. Ribotype 018 was associated with both instances of secondary transmission ( Tables 1 , 2 ). We defined the secondary transmission rate as the ratio of the cumulative number of secondary transmissions to the total number of contact cases per unit of contact time ( Appendix 1 Figure 3). The rapid decrease in transmission rate after the initial surge (1 of 948 cases; 0.001% at 113 minutes), followed by a plateau was attributed to the brief contact time necessary for secondary transmission ( Appendix 1 Figure 3).

Our findings demonstrate a low contribution of patient contact to CDI transmission. However, we found that a low level of direct contact time was required for secondary transmission of CDI. In-hospital transmission rates observed in previous studies have varied according to the surveillance methods used ( 17 – 19 ). Most studies have focused on finding the sources of hospital-acquired CDI, which has led to analyses of only confirmed cases, and susceptible patients at risk of contracting the infection have not been extensively evaluated. A precise rate estimation can be made by using the correct choice of susceptible patients in the denominator. In this study, the transmission rate estimations were made by using RTLS. The comprehensive detection capability of RTLS in contact tracing was exemplified by the substantial percentage of contact cases identified beyond shared bedrooms ( Table 1 ). The overall CDI transmission rate (0.05%) observed in this study was lower than that identified in a prospective study conducted at a tertiary hospital in Switzerland ( 17 ). That study used stringent standard precautions instead of patient isolation, and the subsequent secondary transmission was investigated among patients who had contact with CDI patients. The number of secondary transmission cases in that study, even without including cases of asymptomatic transmission, was comparable to the number in our study. Nevertheless, RTLS identified both direct and indirect contact cases, which have been previously overlooked. In addition, contact cases in our study were distinguished from contact patients; some patients had multiple episodes of contact, mirroring real-world dynamics.

The duration of person-to-person contact required for CDI transmission in our study was as brief as 113 minutes. Infection dose of C. difficile is known to be low in a laboratory setting, but those results have not yet been supported in vivo ( 20 , 21 ). This study investigated the association between contact time and secondary transmission of CDI. A low contact time required for CDI transmission might help explain the absence of differences in CDI incidence rates for genetically related and genetically distant strains, despite the use of contact precautions, as previously described ( 18 ). Short infection periods for multiple C. difficile spreaders have been reported, emphasizing that organism density is more crucial for transmitting the disease than longer contact time ( 22 ). Patients can spread spores, which can be taken up by susceptible patients within hours, depending on organism density. Therefore, once a patient starts showing symptoms, intervention would be considered delayed. Furthermore, multiple CDI cases identified in this study were categorized as asymptomatic transmission, which is a subject of concern ( 4 , 6 , 23 ). Because of adherence to augmented standard precautions in our hospital throughout the study period and considering the role of indirect contact through environmental CDI transmission ( 24 ), it might be more pragmatic to adopt exhaustive standard preventive measures rather than opting for contact isolation of symptomatic patients with CDI. A comprehensive strategy should encompass additional preventive measures, such as careful excrement management and environmental decontamination.

The overall incidence of CDI in the study institution was ≈19.6 cases/10,000 patient-days in 2021, signifying a notable increase compared with 5.9 cases/10,000 patient-days reported in tertiary hospitals within South Korea during 2020–2021 ( 25 ). This study was conducted in an environment marked by substantial transitions from long-term care facilities, resulting in a high incidence of imported cases, which contributed to the elevated overall incidence rate. Despite the high CDI incidence in this study compared with previous research, the effect of secondary transmission via direct or indirect contact on CDI incidence was found to be low. Consequently, factors contributing to disease occurrence that are distinct from CDI patient contact warrant investigation. Previous studies have highlighted the significance of prudent antimicrobial use to diminish spontaneous sporulation of toxigenic C. difficile ( 26 – 30 ). Therefore, this precautionary measure should be prioritized, particularly in a setting where a high percentage of patients might experience dysbiosis because of immobility.

The first limitation of our study is that we could have underestimated the secondary transmission rate by not accounting for asymptomatic carriers who could potentially harbor Clostridioides spores. However, the optimal timing for collecting rectal swab samples to detect secondary transmission in low-risk patients remains uncertain ( 31 ). Therefore, the best approach for ascertaining the secondary transmission rate involves estimation of identified symptomatic patients. Second, RTLS serves as a surrogate metric for contact identification; however, RTLS performance evaluation was precluded in this study because of challenges in identifying a suitable counterpart. Nevertheless, RTLS is characterized by its high sensitivity ( 32 ) and proves advantageous for investigating CDIs when the mode of transmission remains incompletely elucidated ( 5 , 18 ). Our findings retain importance by revealing only 2 instances of secondary transmission after a comprehensive investigation. Third, this study was conducted in an environment where highly contagious strains, such as ribotype 027 and ribotype 018, were infrequently identified. Of note, both instances of secondary transmission observed in this study were linked to ribotype 018, which is well known for its multidrug resistance and transmission capabilities ( 33 , 34 ). We acknowledge that different study outcomes might vary according to the predominant ribotypes, emphasizing the importance of incorporating ribotyping results in outbreak investigations. Fourth, the timely identification of CDI cases by following hospital policy and immediate treatment of CDI-confirmed patients could have contributed to the low transmission incidence observed in this study. We recommend exercising caution in extrapolating our results to other environments.

In conclusion, our study showed a low incidence of secondary CDI transmission within a short period of direct contact. Thus, our findings support prioritizing the comprehensive use of standard preventive measures in healthcare facilities, including environmental decontamination, as a more viable approach to prevent C. difficile infection than relying on symptom-based contact isolation of patients in nonoutbreak settings.

Dr. Kim is as an assistant professor at Hallym University College of Medicine in South Korea. Her research interests focus on infection control and prevention.

Acknowledgment

Raw data supporting the conclusions of this study are included in Appendix 2 .

Sahrmann JM , Olsen MA , Stwalley D , Yu H , Dubberke ER . Costs attributable to Clostridioides difficile infection based on the setting of onset. Clin Infect Dis . 2023 ; 76 : 809 – 15 . DOI PubMed Google Scholar
Lessa FC , Mu Y , Bamberg WM , Beldavs ZG , Dumyati GK , Dunn JR , et al. Burden of Clostridium difficile infection in the United States. N Engl J Med . 2015 ; 372 : 825 – 34 . DOI PubMed Google Scholar
Guh AY , Mu Y , Winston LG , Johnston H , Olson D , Farley MM , et al. ; Emerging Infections Program Clostridioides difficile Infection Working Group . Emerging Infections Program Clostridioides difficile Infection Working Group. Trends in U.S. burden of Clostridioides difficile infection and outcomes. N Engl J Med . 2020 ; 382 : 1320 – 30 . DOI PubMed Google Scholar
Durovic A , Widmer AF , Tschudin-Sutter S . New insights into transmission of Clostridium difficile infection-narrative review. Clin Microbiol Infect . 2018 ; 24 : 483 – 92 . DOI PubMed Google Scholar
Zhou Y , Zhou W , Xiao T , Chen Y , Lv T , Wang Y , et al. Comparative genomic and transmission analysis of Clostridioides difficile between environmental, animal, and clinical sources in China. Emerg Microbes Infect . 2021 ; 10 : 2244 – 55 . DOI PubMed Google Scholar
Durham DP , Olsen MA , Dubberke ER , Galvani AP , Townsend JP . Quantifying Transmission of Clostridium difficile within and outside Healthcare Settings. Emerg Infect Dis . 2016 ; 22 : 608 – 16 . DOI PubMed Google Scholar
Her M . Repurposing and reshaping of hospitals during the COVID-19 outbreak in South Korea. One Health . 2020 ; 10 : 100137 . DOI PubMed Google Scholar
Jeong H , Hyun J , Lee YK . Epidemiological characteristics of carbapenemase-producing Enterobacteriaceae outbreaks in the Republic of Korea between 2017 and 2022. Osong Public Health Res Perspect . 2023 ; 14 : 312 – 20 . DOI PubMed Google Scholar
Patel B , Vilendrer S , Kling SMR , Brown I , Ribeira R , Eisenberg M , et al. Using a real-time locating system to evaluate the impact of telemedicine in an emergency department during COVID-19: observational study. J Med Internet Res . 2021 ; 23 : e29240 . DOI PubMed Google Scholar
Ho HJ , Zhang ZX , Huang Z , Aung AH , Lim W-Y , Chow A . Use of a real-time locating system for contact tracing of health care workers during the COVID-19 pandemic at an infectious disease center in Singapore: validation study. J Med Internet Res . 2020 ; 22 : e19437 . DOI PubMed Google Scholar
Huang Z , Guo H , Lee Y-M , Ho EC , Ang H , Chow A . Performance of digital contact tracing tools for COVID-19 response in Singapore: cross-sectional study. JMIR Mhealth Uhealth . 2020 ; 8 : e23148 . DOI PubMed Google Scholar
Camacho-Cogollo JE , Bonet I , Iadanza E . RFID technology in health care. In: Iadanza E, editor. Clinical engineering handbook, 2nd ed. London: Elsevier/Academic Press; 2020 . p. 33–41.
Overmann KM , Wu DTY , Xu CT , Bindhu SS , Barrick L . Real-time locating systems to improve healthcare delivery: A systematic review. J Am Med Inform Assoc . 2021 ; 28 : 1308 – 17 . DOI PubMed Google Scholar
O’Neill GL , Ogunsola FT , Brazier JS , Duerden BI . Modification of a PCR ribotyping method for application as a routine typing scheme for Clostridium difficile. Anaerobe . 1996 ; 2 : 205 – 9 . DOI Google Scholar
Stubbs SL , Brazier JS , O’Neill GL , Duerden BI . PCR targeted to the 16S-23S rRNA gene intergenic spacer region of Clostridium difficile and construction of a library consisting of 116 different PCR ribotypes. J Clin Microbiol . 1999 ; 37 : 461 – 3 . DOI PubMed Google Scholar
Byun J-H , Kim H , Kim JL , Kim D , Jeong SH , Shin JH , et al. A nationwide study of molecular epidemiology and antimicrobial susceptibility of Clostridioides difficile in South Korea. Anaerobe . 2019 ; 60 : 102106 . DOI PubMed Google Scholar
Widmer AF , Frei R , Erb S , Stranden A , Kuijper EJ , Knetsch CW , et al. Transmissibility of Clostridium difficile without contact isolation: results from a prospective observational study with 451 patients. Clin Infect Dis . 2017 ; 64 : 393 – 400 . PubMed Google Scholar
Eyre DW , Cule ML , Wilson DJ , Griffiths D , Vaughan A , O’Connor L , et al. Diverse sources of C. difficile infection identified on whole-genome sequencing. N Engl J Med . 2013 ; 369 : 1195 – 205 . DOI PubMed Google Scholar
Barbut F , Gariazzo B , Bonné L , Lalande V , Burghoffer B , Luiuz R , et al. Clinical features of Clostridium difficile -associated infections and molecular characterization of strains: results of a retrospective study, 2000-2004. Infect Control Hosp Epidemiol . 2007 ; 28 : 131 – 9 . DOI PubMed Google Scholar
Otter JA , French GL . Survival of nosocomial bacteria and spores on surfaces and inactivation by hydrogen peroxide vapor. J Clin Microbiol . 2009 ; 47 : 205 – 7 . DOI PubMed Google Scholar
Humphreys PN . Testing standards for sporicides. J Hosp Infect . 2011 ; 77 : 193 – 8 . DOI PubMed Google Scholar
Kumar N , Miyajima F , He M , Roberts P , Swale A , Ellison L , et al. Genome-based infection tracking reveals dynamics of Clostridium difficile transmission and disease recurrence. Clin Infect Dis . 2016 ; 62 : 746 – 52 . DOI PubMed Google Scholar
Curry SR , Muto CA , Schlackman JL , Pasculle AW , Shutt KA , Marsh JW , et al. Use of multilocus variable number of tandem repeats analysis genotyping to determine the role of asymptomatic carriers in Clostridium difficile transmission. Clin Infect Dis . 2013 ; 57 : 1094 – 102 . DOI PubMed Google Scholar
McFarland LV , Mulligan ME , Kwok RY , Stamm WE . Nosocomial acquisition of Clostridium difficile infection. N Engl J Med . 1989 ; 320 : 204 – 10 . DOI PubMed Google Scholar
Kim S-H , Wi YM . Current strategy and perspective view for preventing Clostridioides difficile infection in acute care facilities. Korean J Healthc Assoc Infect Control Prev. 2021 ; 26 : 70 – 82 . DOI Google Scholar
Shim JK , Johnson S , Samore MH , Bliss DZ , Gerding DN . Primary symptomless colonisation by Clostridium difficile and decreased risk of subsequent diarrhoea. Lancet . 1998 ; 351 : 633 – 6 . DOI PubMed Google Scholar
Chang JY , Antonopoulos DA , Kalra A , Tonelli A , Khalife WT , Schmidt TM , et al. Decreased diversity of the fecal Microbiome in recurrent Clostridium difficile -associated diarrhea. J Infect Dis . 2008 ; 197 : 435 – 8 . DOI PubMed Google Scholar
Johnson S . Recurrent Clostridium difficile infection: a review of risk factors, treatments, and outcomes. J Infect . 2009 ; 58 : 403 – 10 . DOI PubMed Google Scholar
Starr JM , Campbell A , Renshaw E , Poxton IR , Gibson GJ . Spatio-temporal stochastic modelling of Clostridium difficile. J Hosp Infect . 2009 ; 71 : 49 – 56 . DOI PubMed Google Scholar
Kyne L , Warny M , Qamar A , Kelly CP . Association between antibody response to toxin A and protection against recurrent Clostridium difficile diarrhoea. Lancet . 2001 ; 357 : 189 – 93 . DOI PubMed Google Scholar
Jazmati N , Kirpal E , Piepenbrock E , Stelzer Y , Vehreschild MJGT , Seifert H . Evaluation of the use of rectal swabs for laboratory diagnosis of Clostridium difficile infection. J Clin Microbiol . 2018 ; 56 : e00426 – 18 . DOI PubMed Google Scholar
Koenig KR , Pasupathy KS , Hellmich TR , Hawthorne HJ , Karalius VP , Sir M , et al. Measuring sensitivity and precision of real-time location systems (RTLS): definition, protocol and demonstration for clinical relevance. J Med Syst . 2021 ; 45 : 15 . DOI PubMed Google Scholar
Spigaglia P , Barbanti F , Dionisi AM , Mastrantonio P . Clostridium difficile isolates resistant to fluoroquinolones in Italy: emergence of PCR ribotype 018. J Clin Microbiol . 2010 ; 48 : 2892 – 6 . DOI PubMed Google Scholar
Baldan R , Trovato A , Bianchini V , Biancardi A , Cichero P , Mazzotti M , et al. Clostridium difficile PCR ribotype 018, a successful epidemic genotype. J Clin Microbiol . 2015 ; 53 : 2575 – 80 . DOI PubMed Google Scholar
Appendix 1 .
Appendix 2 .
Table 1 . Baseline characteristics of patients in study identifying contact time required for secondary transmission of Clostridioides difficile infections by using real-time locating system in South Korea
Table 2 . Patients manifesting secondary transmission in study identifying contact time required for secondary transmission of Clostridioides difficile infections by using real-time locating system conducted in South Korea

Suggested citation for this article : Kim MH, Kim J, Ra H, Jeong S, Park YS, Won D, et al. Identifying contact time required for secondary transmission of Clostridioides difficile infections by using real-time locating system. Emerg Infect Dis. 2024 May [ date cited ]. https://doi.org/10.3201/eid3005.231588

DOI: 10.3201/eid3005.231588

Original Publication Date: April 04, 2024

1 These authors contributed equally to this article.

Table of Contents – Volume 30, Number 5—May 2024

Please use the form below to submit correspondence to the authors or contact them at the following address:

Heejung Kim, Department of Laboratory Medicine, Yongin Severance Hospital, Yonsei University School of Medicine, 363 Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do 16995, South Korea

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A new method for storing and processing hydrogen chloride
DOI: 10.1126/sciadv.adn5353. A research team at Freie Universität Berlin has successfully developed a method for storing and electrolyzing gaseous hydrogen chloride in the form of an ionic liquid ...
Quantitative and Qualitative Approaches to Generalization and
Hence, mixed methods methodology does not provide a conceptual unification of the two approaches. Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007). These different standards affect ...
Establishing New Methodology for Determining Water Absorbability of
In this research, for the first time, we introduce a vapor-monitoring headspace strategy to establish a new methodology for determining water absorbability of cellulose-derived materials. The method involves detecting the water in the gas phase from various cellulose-derived materials after achieving the equilibrium state.
Use of Abortion Pills Has Risen Significantly Post Roe, Research Shows
The News. On the eve of oral arguments in a Supreme Court case that could affect future access to abortion pills, new research shows the fast-growing use of medication abortion nationally and the ...
Identifying Contact Time Required for Secondary Transmission of
Mitigating the incidence of Clostridioides difficile infections (CDIs), particularly those acquired in healthcare settings, has received increased attention because of the notable prevalence of this contagion (1,2).Although the incidence of hospital-acquired CDI has declined because effective infection control measures have been used (), the effectiveness of specific interventions used to curb ...

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Research Methods | Definitions, Types, Examples

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Qualitative vs. quantitative data

Primary vs. secondary research

Descriptive vs. experimental data

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Qualitative analysis methods

Quantitative analysis methods

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How to write the methods section of a research paper

Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research

Conclusions

The example systematic methods overview on sampling in qualitative research

Organization of the guidance into principles and strategies

Results and discussion

Delimiting a manageable set of publications

Principle #1:

Strategy #1:

Searching beyond standard bibliographic databases

Principle #2:

Strategy #2:

Searching without relevant metadata

Principle #3:

Strategy #3:

Purposefully selecting literature on conceptual grounds

Principle #4:

Strategy #4:

Data abstraction

Iteratively defining conceptual information to abstract

Principle #5:

Strategy #5:

Accounting for inconsistent terminology

Principle #6:

Strategy #6:

Generating credible and verifiable analytic interpretations

Principle #7:

Strategy #7:

Integrative versus interpretive methods overviews

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Systematic Reviews

Research Methodology – Types, Examples and writing Guide

Research Methodology

Structure of Research Methodology

Research Methodology Types

Quantitative Research Methodology

Qualitative Research Methodology

Mixed-Methods Research Methodology

Case Study Research Methodology

Action Research Methodology

Experimental Research Methodology