Exploring the utility and scalability of entrustable professional activities beyond medical education: A scoping review protocol
Bibliographic record
Abstract
Abstract Competency-based education (CBE) is gaining prominence in post-secondary education due to its effectiveness in optimizing workforce readiness. However, assessing competence and workforce readiness presents a significant challenge. Entrustable professional activities (EPAs) offer an effective solution by translating competencies into observable, measurable, and entrustable tasks. While EPAs have been widely adopted in medical education, their application in non-clinical educational contexts remains minimal. This scoping review aims to map the diffusion of EPAs beyond medicine and assess their scalability to undergraduate non-clinical minor pathways, which are ideal piloting sites for EPA-based assessment frameworks. Guided by Rogers’ Diffusion of Innovations theory, this review will identify the barriers and facilitators influencing the adoption of EPAs in non-clinical educational contexts. Following Arksey and O’Malley’s framework, this review will progress through five stages: identifying the research question, identifying relevant studies, study selection, charting the data, and reporting results. Searches will be conducted across Ovid MEDLINE, CINAHL, Scopus, ERIC, and grey literature. The research question is: “How have EPAs been developed and implemented outside of medical education, and what factors influence their adoption in non-clinical training programs?” The search will include articles describing the development or implementation of EPAs outside medicine and will exclude irrelevant studies. Titles and abstracts will be reviewed first, followed by a full-text examination of relevant articles. Data will be extracted, organized, and summarized to highlight key findings. Two reviewers will ensure quality during screening and data extraction. Findings from this scoping review will inform the rationale for scaling EPAs to non-clinical training programs and offer insights to guide the adaptation, development, and implementation of EPA-based assessment frameworks in undergraduate non-clinical minor pathways, addressing pressing challenges in CBE and creating a replicable model for future program development. Findings from this scoping review will be submitted for publication in a scientific journal.
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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 | 0.112 | 0.143 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".