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

A Beginner's Guide to Meta-Ethnography

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

Notice bibliographique

RevueJournal of Graduate Medical Education · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation and Critical Thinking Development
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésEthnographyCLARITYIdentification (biology)Qualitative researchEpistemologyInterpretation (philosophy)Computer scienceEngineering ethicsField (mathematics)Data scienceSociologyManagement scienceSocial scienceEngineering

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,008
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,791
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,129
Tête enseignante GPT0,454
Écart entre enseignants0,325 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations19
Publié2023
Routes d'admission1
Résumé présentoui

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