Distant and Hidden Figures: Foregrounding Patients in the Development, Content, and Implementation of Entrustable Professional Activities
Bibliographic record
Abstract
Entrustable professional activities (EPAs) describe activities that qualified professionals must be able to perform to deliver safe and effective care to patients. The entrustable aspect of EPAs can be used to assess learners through documentation of entrustment decisions, while the professional activity aspect can be used to map curricula. When used as an assessment framework, the entrustment decisions reflect supervisory judgments that combine trainees' relational autonomy and patient safety considerations. Thus, the design of EPAs incorporates the supervisor, trainee, and patient in a way that uniquely offers a link between educational outcomes and patient outcomes. However, achieving a patient-centered approach to education amidst both curricular and assessment obligations, educational and patient outcomes, and a supervisor-trainee-patient triad is not simple nor guaranteed. As medical educators continue to advance EPAs as part of their approach to competency-based medical education, the authors share a critical discussion of how patients are currently positioned in EPAs. In this article, the authors examine EPAs and discuss how their development, content, and implementation can result in emphasizing the trainee and/or supervisor while unintentionally distancing or hiding the patient. They consider creative possibilities for how EPAs might better integrate the patient as finding ways to better foreground the patient in EPAs holds promise for aligning educational outcomes and patient outcomes.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".