Entrustable Professional Activities: An Analysis of Faculty Time, Trainee Perspectives, and Actionability
Bibliographic record
Abstract
The Royal College of Physicians and Surgeons of Canada introduced Competence by Design (CBD) as an educational model along with Entrustable Professional Activities (EPAs) as markers of achievement that could be directly observed on a frequent basis. In 2017, the University of Calgary Internal Medicine (IM) program piloted CBD. The purpose of this study was to (1) assess whether written feedback from EPAs were actionable, valuable, and disruptive to workflow and (2) assess the time required to complete an EPA. Methods Seven Foundations of Discipline EPAs were used with 31 PGY-1 Calgary IM residents. The study used quantitative and qualitative data. Following a discussion on an EPA and completion of both the quantitative and written feedback, residents were asked to comment on the value of the encounter and the degree of disruption to workflow. Assessors provided time to complete an EPA. Data were anonymized. Trainee comments were coded for value and disruption, and assessor's written feedback was coded for actionability. Results One hundred and five EPA encounters were submitted. The majority of the comments provided to trainees were not actionable (94.3%, n = 99/105). While most residents did not comment on value (73.3%, n = 77/105) or disruption (44.8%, n = 47/105) of the encounter, those that did generally found the encounters valuable (25.7%, n = 27/105) and nondisruptive (35.2%, n = 37/105). A minority found the process nonvaluable (1%, n = 1/105) and disruptive (20%, n = 21/105). The mean time to complete an EPA form and provide feedback was 8.6 min. Conclusion Most written feedback was not actionable, suggesting a potential role for faculty development to guide assessors and help them coach trainees on EPAs.
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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 | 0.020 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".