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Record W4362453031 · doi:10.1002/aet2.10862

Curated collections for educators: Nine key articles and article series for teaching qualitative research methods

2023· article· en· W4362453031 on OpenAlexaff
Sophia Lin, Elise Zimmerman, Suchismita Datta, Maurice Selby, Teresa M. Chan, Abra Fant

Bibliographic record

VenueAEM Education and Training · 2023
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsQualitative researchDelphi methodContext (archaeology)CurriculumMedical educationQualitative propertyDelphiPsychologyComputer scienceMedicineSociologyPedagogySocial science

Abstract

fetched live from OpenAlex

Background: Qualitative research explains observations, focusing on how and why phenomena and experiences occur. Qualitative methods go beyond quantitative data and provide critical information inaccessible through quantitative methods. However, at all levels of medical education, there is insufficient exposure to qualitative research. As a result, residents and fellows complete training ill-equipped to appraise and conduct qualitative studies. As a first step to increasing education in qualitative methods, we sought to create a curated collection of papers for faculty to use in teaching qualitative research at the graduate medical education (GME) level. Methods: We conducted literature searches on the topic of teaching qualitative research to residents and fellows and queried virtual medical education and qualitative research communities for relevant articles. We searched the reference lists of all articles found through the literature searches and online queries for additional articles. We then conducted a three-round modified Delphi process to select papers most relevant to faculty teaching qualitative research. Results: We found no articles describing qualitative research curricula at the GME level. We identified 74 articles on the topic of qualitative research methods. The modified Delphi process identified the top nine articles or article series most relevant for faculty teaching qualitative research. Several articles explain qualitative methods in the context of medical education, clinical care, or emergency care research. Two articles describe standards of high-quality qualitative studies, and one article discusses how to conduct the individual qualitative interview to collect data for a qualitative study. Conclusions: While we identified no articles reporting already existing qualitative research curricula for residents and fellows, we were able to create a collection of papers on qualitative research relevant to faculty seeking to teach qualitative methods. These papers describe key qualitative research concepts important in instructing trainees as they appraise and begin to develop their own qualitative studies.

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)0.008
metaresearch head score (Gemma)0.075
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.075
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.616
GPT teacher head0.698
Teacher spread0.081 · 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 designQualitative
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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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