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Record W2927350975 · doi:10.1097/acm.0000000000002735

Scoping Review of Entrustable Professional Activities in Undergraduate Medical Education

2019· article· en· W2927350975 on OpenAlexaboutno aff
Eric G. Meyer, H. Carrie Chen, Sebastian Uijtdehaage, Steven J. Durning, Lauren A. Maggio

Bibliographic record

VenueAcademic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationMEDLINEInclusion (mineral)PsychologyMedicineEducational measurementCurriculumPedagogySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: Entrustable professional activities (EPAs) are a hot topic in undergraduate medical education (UME); however, the usefulness of EPAs as an assessment approach remains unclear. The authors sought to better understand the literature on EPAs in UME through the lens of the 2010 Ottawa Conference Criteria for Good Assessment. METHOD: The authors conducted a scoping review of the health professions literature (search updated February 2018), mapping publications to the Ottawa Criteria using a collaboratively designed charting tool. RESULTS: Of the 1,089 publications found, 71 (6.5%) met inclusion criteria. All were published after 2013. Forty-five (63.4%) referenced the 13 Core Entrustable Professional Activities for Entering Residency developed by the Association of American Medical Colleges (AAMC). Forty (56.3%) were perspectives, 5 (7.0%) were reviews, and 26 (36.6%) were prospective empirical studies. The publications mapped to the Ottawa Criteria 158 times. Perspectives mapped more positively (83.7%) than empirical studies (76.7%). Reproducibility did not appear to be a strength of EPAs in UME; however, reproducibility, equivalence, educational effect, and catalytic effect all require further study. Inconsistent use of the term "EPA" and conflation of concepts (activity vs assessment vs advancement decision vs curricular framework) limited interpretation of published results. Overgeneralization of the AAMC's work on EPAs has influenced the literature. CONCLUSIONS: Much has been published on EPAs in UME in a short time. Now is the time to move beyond opinion, clarify terms, and delineate topics so that well-designed empirical studies can demonstrate if and how EPAs should be implemented in UME.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.419
Teacher spread0.398 · 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 designSystematic review
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

Citations127
Published2019
Admission routes1
Has abstractyes

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