Twelve tips to develop entrustable professional activities
Bibliographic record
Abstract
Entrustable professional activities (EPAs), units of professional practice that require proficient integration of multiple competencies and can be entrusted to a sufficiently competent learner, are increasingly being used to define and inform curricula of health care professionals. The process of developing EPAs can be challenging and requires a deep yet pragmatic understanding of the concepts underlying EPA construction. Based on recent literature and the authors' lessons learned, this article provides the following practical and more or less sequential recommendations for developing EPAs: [1] Assemble a core team; [2] Build up expertise; [3] Establish a shared understanding of the purpose of EPAs; [4] Draft preliminary EPAs; [5] Elaborate EPAs; [6] Adopt a framework of supervision; [7] Perform a structured quality check; [8] Use a Delphi approach for refinement and/or consensus; [9] Pilot test EPAs; [10] Attune EPAs to their feasibility in assessment; [11] Map EPAs to existing curriculum; [12] Build a revision plan.
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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 | 0.053 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".