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Record W4320894699 · doi:10.4300/jgme-d-22-00958.1

A Beginner's Guide to Meta-Ethnography

2023· article· en· W4320894699 on OpenAlexaff
Victoria Luong, Margaret Bearman, Anna MacLeod

Bibliographic record

VenueJournal of Graduate Medical Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEthnographyCLARITYIdentification (biology)Qualitative researchEpistemologyInterpretation (philosophy)Computer scienceEngineering ethicsField (mathematics)Data scienceSociologyManagement scienceSocial scienceEngineering

Abstract

fetched live from OpenAlex

Meta-ethnography offers a rigorous method for synthesizing multiple qualitative studies to advance understanding of a topic. Developed by Noblit and Hare in the field of education,1 meta-ethnography is well established in applied health research.2-5 The goal is to synthesize existing qualitative research to arrive at new insights, interpreting beyond the findings that are currently reported. A meta-ethnographic review is a qualitative interpretation of qualitative interpretations, and researchers must be prepared to embrace the complexity that comes with this approach.In this article, we provide a synopsis of how to read a meta-ethnographic review as well as how to get started if interested in conducting a meta-ethnographic review. We briefly explain the 7 steps of meta-ethnography and follow this with an overview of 3 critical approaches to synthesizing the data (ie, to interpret the interpretations). The Box lists several resources readers may find useful when designing a meta-ethnographic study.While each meta-ethnographic study is unique, the approach can be broken down into 7 distinct, but overlapping, steps or phases.1A review typically begins with the identification of an issue needing further investigation or clarification. Often, an issue well-suited for meta-ethnographic work is one that has been rigorously investigated and well-described but continues to lack clarity or consensus. A team of researchers with relevant and varied expertise in the area of interest should be established.This step is critically important, if somewhat self-evident. Identifying a clear focus will support the review in moving forward effectively. This phase also involves selection of studies to be included in the review, based on criteria negotiated by the research team.The researchers will carefully read each of the selected studies with a focus on identifying notable concepts. This phase shares a similar approach as open coding in qualitative data analysis, by denoting ideas that may be further categorized and elucidated through the review.While phase 3 serves as a type of coding, phase 4 mirrors the act of grouping codes into themes. This broader categorization of themes is done iteratively, and multiple team members can contribute. Various methods to organize data can be used (eg, diagrams or qualitative data analysis software) to purposefully bring together concepts and see how they relate to, or contest, each other.Translation involves exploring the analogies, metaphors, themes, and concepts that can help make sense of the relationships between studies. It is during this phase that the researchers work differently (ie, using reciprocal translation, refutational synthesis, or lines-of-argument synthesis), depending on how the studies relate to each other, as discussed in the next section.During this phase, the researchers work with identified concepts from the reviewed studies to arrive at new interpretations. It involves searching for overarching explanations and identifying gaps, overlaps, and silences.Finally, the meta-ethnographic insights should be reported in a manner that advances understanding on a particular topic. The eMERGe Reporting Guidelines provide useful guidance.4In steps 4 through 6, the researchers grapple with how to conduct the translation and synthesis activities. Noblit and Hare identified 4 ways that qualitative studies can relate to each other.1 If the studies are about different phenomena altogether, then there is no use synthesizing them and meta-ethnography is not the right approach. However, if the studies are addressing the same general phenomenon, then meta-ethnography is a good choice. The studies may be related in 3 different ways. They may say similar things, say contradictory things, or say different things requiring additional sensemaking. These lead to the following types of syntheses:This approach applies when concepts in one study can incorporate those of another because they are very similar in meaning. Reciprocal translation focuses on finding the analogies and explanations that best represent the whole.This approach applies when the concepts in different studies––or the studies themselves––contradict or refute one another. In these types of syntheses, the refutations themselves become units of analysis.This approach applies when the qualitative studies under review identify different aspects of the topic that can be drawn together in a new interpretation. In other words, the synthesis leads to a new storyline emerging. While it is not necessary to identify and adhere to one approach, these synthesis methods serve as useful analytical tools for meta-ethnographic interpretation.Careful consideration of the key concepts and assumptions that underpin meta-ethnography synthesis work, as well as the steps involved in the process, are essential to readers' confidence in the quality of the review as well as for those contemplating performing a meta-ethnographic review. Meta-ethnography, by synthesizing qualitative evidence in a way that extends beyond the meaning of the original studies it interprets, has significant potential to expand understanding in the field of health professions education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.129
GPT teacher head0.454
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations19
Published2023
Admission routes1
Has abstractyes

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