Scoping Review of Entrustable Professional Activities in Undergraduate Medical Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.043 | 0.040 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".