Eight ways to support faculty with Entrustable Professional Activities
Bibliographic record
Abstract
Competency Based Medical Education (CBME) is pushing the medical profession to be more accountable in our standards of assessment. This has led us to focus our efforts at the top of Miller's pyramid, where we aim to see what the trainee 'does' in the clinical environment. In Canadian Royal College specialty training, this has come in the form of workplace-based supervision of trainees performing Entrustable Professional Activities (EPAs). This is unfamiliar territory for many residents and faculty, and implementation of an additional assessment process into already busy clinical practice has been particularly challenging. Because EPA assessments serve as significant contributors in new programs of assessment, failure to collect high quality EPA assessments threaten the validity of this new system. Understanding the barriers to and enablers of EPA acquisition can inform faculty development initiatives to ensure success. Based on our previous work studying early experiences of EPA assessment acquisition in Emergency Medicine, we have identified eight key concepts to guide faculty development initiatives, namely: the rationale for CBME, the 'behind the scenes' of CBME, how to construct rich narrative comments, effective use of supervision scales, the tension of EPA assessments being both formative and summative, the importance of a shared responsibility between residents and faculty for EPA assessment completion, familiarity with the suite of EPAs, and tips and tricks for incorporating EPA assessment completion into busy clinical practice. These key concepts can be integrated into an overall faculty development strategy for building this now essential skill set.
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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 | 0.068 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.031 | 0.032 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".