The logic behind entrustable professional activity frameworks: A scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: Entrustable professional activities (EPAs), discrete profession-specific tasks requiring integration of multiple competencies, are increasingly used to help define and inform curricula of specialty training programmes. Although guidelines exist to help guide the developmental process, deciding what logic to use to draft a preliminary EPA framework poses a crucial but often difficult first step. The logic of an EPA framework can be defined as the perspective used by its developers to break down the practice of a profession into units of professional work. This study aimed to map dominant logics and their rationales across postgraduate medical education and fellowship programmes. METHODS: A scoping review using systematic searches within five electronic databases (Medline, Embase, Google Scholar, Scopus and Web of Science) was performed. Dominant logics of included papers were identified using inductive coding and iterative analysis. RESULTS: In total, 42 studies were included. Most studies were conducted in the United States (n = 22; 52%), Canada (n = 6; 14%) and the Netherlands (n = 4; 10%). Across the reported range of specialties, family medicine (n = 4; 10%), internal medicine (n = 4; 10%), paediatrics (n = 3; 7%) and psychiatry (n = 3; 7%) were the most common. Three dominant logics could be identified, namely, 'service provision', 'procedures' and/or 'disease or patient categories'. The majority of papers (n = 37; 88%) used two or more logics when developing EPA frameworks (median = 3, range = 1-4). Disease or patient groups and service provision were the most common logics used (39% and 37%, respectively). CONCLUSIONS: Most programmes used a combination of logics when trying to capture the essential tasks of a profession in EPAs. For each of the three dominant logics, the authors arrived at a definition and identified benefits, limitations and examples. These findings may potentially inform best practice guidelines for EPA development.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".