Monitoring Competency-Based Medical Education Uptake: Analysis of Entrustable Professional Activity Submission, Expiration, and Assessment Scores
Bibliographic record
Abstract
ABSTRACT Background Program directors need concrete indicators to monitor uptake of competency-based medical education (CBME). Entrustable professional activity (EPA) observation completion rates offer practical measures of CBME adoption. Objective In this study, we used residents’ EPA observation data in clinical departments, specifically the submission and expiration of EPA observation forms and assessment scores, to explore the uptake of CBME practices across departments. Our research question asked: What are the patterns and contributing factors (department group, resident year, calendar year, program size) associated with EPA observation submission rates, expiration rates, and assessment scores? Methods We conducted exploratory analysis of de-identified EPA observation data (n=233 176) from residents’ electronic portfolios (n=2110) across 45 programs in 12 departments at one Canadian institution from 2018 to 2023. Descriptive statistics summarized submission, expiration, and score distributions. Spearman correlations and logistic regression examined 4 predictors: department group, resident year, calendar year, and program size. Results EPA submission rates (81.0%), expiration rates (7.7%), and assessment O-scores (M=4.4 out of 5) did not differ significantly by training department. Calendar year increased odds of an independent or full score by 26.3% per year (OR, 1.263; 95% CI, 1.259-1.267) while resident year (OR, 0.818; 95% CI, 0.813-0.825) and program size (OR, 0.995; 95% CI, 0.994-0.996) decreased those odds. Conclusions EPA submission, expiration, and scoring patterns are consistent across departments and correlate with implementation year, resident training stage, and program size.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".