Using the Entrustable Professional Activities Framework in the Assessment of Procedural Skills
Bibliographic record
Abstract
BACKGROUND: The entrustable professional activity (EPA) framework has been identified as a useful approach to assessment in competency-based education. To apply an EPA framework for assessment, essential skills necessary for entrustment to occur must first be identified. OBJECTIVE: Using an EPA framework, our study sought to (1) define the essential skills required for entrustment for 7 bedside procedures expected of graduates of Canadian internal medicine (IM) residency programs, and (2) develop rubrics for the assessment of these procedural skills. METHODS: An initial list of essential skills was defined for each procedural EPA by focus groups of experts at 4 academic centers using the nominal group technique. These lists were subsequently vetted by representatives from all Canadian IM training programs through a web-based survey. Consensus (more than 80% agreement) about inclusion of each item was sought using a modified Delphi exercise. Qualitative survey data were analyzed using a framework approach to inform final assessment rubrics for each procedure. RESULTS: Initial lists of essential skills for procedural EPAs ranged from 10 to 24 items. A total of 111 experts completed the national survey. After 2 iterations, consensus was reached on all items. Following qualitative analysis, final rubrics were created, which included 6 to 10 items per procedure. CONCLUSIONS: These EPA-based assessment rubrics represent a national consensus by Canadian IM clinician educators. They provide a practical guide for the assessment of procedural skills in a competency-based education model, and a robust foundation for future research on their implementation and evaluation.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".