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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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; both teacher heads agree on what is shown here.
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".