EPAs for the Ambulatory Internist in Translation: Findings from a Canadian Multi-Center Survey
Bibliographic record
Abstract
Background Increased demand for outpatient care has made defining the role of ambulatory general internists an educational priority. Canadian residency programs are transitioning towards competency-based education, where learning goals are articulated as entrustable professional activities (EPAs). Engaging frontline internists in the validation of context-specific EPAs is important for implementation. Objective This study describes a consensus approach for developing EPAs for ambulatory general internal medicine (GIM) training and results of a Canada-wide survey seeking feedback from academic internists. Methods In 2016, we reviewed Royal College of Physicians and Surgeons of Canada GIM accreditation documents, and systematic literature search results for internal medicine ambulatory training objectives, to draft EPAs. EPAs were revised via expert consensus at the University of Toronto. A survey was distributed to Canadian academic internists to determine level of agreement on proposed EPAs. Consensus was defined as greater than 80% inter-rater agreement. Open-ended questions explored reasons for disagreements, which were reviewed independently by authors and iteratively organized into categories. Results Eight EPAs were generated. The survey response rate was 24.9% (63/253). Consensus was achieved on all EPAs except obstetrical medicine (49/63, 77.8%). Reasons for disagreements reflected a variable understanding of EPA concepts by respondents. Where understood well, disagreements fell into 3 main categories: (1) further training required, (2) not within internal medicine scope, and (3) implementation barriers. Conclusions Frontline academic physicians are pivotal in validating proposed EPAs. Disagreements were either content or concept related and recognizing these diverse perspectives can help clinician-educators predict and prepare for challenges with EPA implementation.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".