Multi-stakeholder validation of Entrustable Professional Activities in FM-Care of the Elderly and RCPSC Geriatrics
Bibliographic record
Abstract
Entrustable Professional Activities (EPAs) have become widely used within Competency-Based Medical Education (CBME) for the training and evaluation of residents. Little is known about the effectiveness of incorporating multiple stakeholder groups in the validation of EPAs. Through online focus groups consisting of five distinct stakeholder groups, we seek to validate two EPA frameworks: one for the University of Manitoba Care of the Elderly (CoE) Enhanced Skills program, and one for Canadian Geriatrics Specialty Programs. Participants were recruited to take part in one of five online focus groups, one for each stakeholder group (physician faculty, residents, non-physician healthcare professionals, administrators/managers, and patients). Each group met one time for 90 minutes over ZOOM. Meeting transcripts were coded using NVivo using codes that were formulated iteratively by the research team. The themes arising from stakeholder feedback suggest that successful EPAs must neither be too specific nor too expansive in scope, clearly delineate appropriate means of evaluation, and indicate specific clinical settings in which each EPA should be evaluated. Cross-cutting themes included requiring trainees to collaborate with other professionals when it would optimize patient care, and preparing trainees to advocate for their patients' health (Advocacy). Stakeholders also brought forth a variety of ideas that could be used to formulate new CoE and Geriatrics EPAs, and reflected on how the frameworks contrasted the two disciplines. The present study demonstrates that multi-stakeholder analysis yields diverse feedback that can help make EPAs clearer, easier to use in evaluation, and more socially accountable.
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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 | 0.137 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".