EQual Rubric Evaluation of the Association of American Medical Colleges’ Core Entrustable Professional Activities for Entering Residency
Bibliographic record
Abstract
PURPOSE: To have subject matter experts evaluate the Core Entrustable Professional Activities for Entering Residency (Core EPAs) with the EQual rubric to determine if revisions were required and, if applicable, how to focus revision efforts. METHOD: Ten entrustable professional activity (EPA) experts were invited to evaluate the 13 Core EPAs. Experts had a 6-month window (December 2018-May 2019) to complete the evaluation, which contained the complete EQual rubric and 3 additional prompts, one of which-"Do you think this EPA requires revision?"-was limited to a "yes/no" response. Descriptive statistics for overall and domain-specific EQual rubric scores for each of the 13 Core EPAs were calculated. Free-text responses to why and/or how a Core EPA should be revised were summarized for any Core EPA that scored below a cutoff or for which the majority of experts recommended revision. RESULTS: Six experts completed the evaluation. Most Core EPAs' (9/13) overall score was above the cutoff, indicating that they align with the key domains of the EPA construct. The remaining 4 Core EPAs (2, 7, 9, and 13) scored below the overall cutoff, suggesting that they may require revision. A majority of experts felt that Core EPAs 6, 7, 9, and 13 required revision. With regard to domain-specific scores, Core EPAs 2, 3, 7, 9, and 13 were below the discrete units of work cutoff; Core EPAs 7, 9, and 13 were below the entrustable, essential, and important tasks of the profession of medicine cutoff; and Core EPA 9 was below the curricular role cutoff. CONCLUSIONS: The Core EPAs represent a promising initial framework of EPAs for undergraduate medical education. Some Core EPAs might benefit from revision. The process of improving the Core EPAs must continue if they are to standardize outcomes for medical school graduates.
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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 | 0.023 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".