The recommended description of an entrustable professional activity: AMEE Guide No. 140
Bibliographic record
Abstract
Entrustable professional activities (EPAs) have received much attention in the literature since they were first proposed in 2005. Useful guidelines, workshops, courses, and conferences have supported faculty in developing programs and designing assessment procedures using EPAs and entrustment decision-making. Yet, the need for clarification remains, particularly as more programs make the step from design to implementation.Well-written EPAs provide a natural construct to establish the outcome of training. To be useful, EPAs require more than a suitable title. This AMEE Guide elaborates eight sections of a full EPA description, and provides explanations and justifications for each. These sections are: title; specification and limitations; risks in case of failure; most relevant competency domains; knowledge, skills, attitudes and experiences; information sources to assess progress and support summative entrustment; entrustment/supervision level expected at which stage of training; and time period to expiration if not practiced.Constructing fully elaborated EPAs creates a shared mental model amongst learners and programs, informs competency-based curriculum design, directs ad-hoc and formal entrustment decision-making, and provides standards for certifying bodies and boundaries for scope of practice. The framework intends to support curricular leaders looking to adopt new EPAs, or revise and define established EPAs for competency-based education.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.190 | 0.177 |
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".