In EPAs we trust, is quality and safety a must? A cross-specialty analysis of entrustable professional activity guides
Bibliographic record
Abstract
PURPOSE: The inclusion of quality improvement (QI) and patient safety (PS) into CanMEDS reflects an expectation that graduating physicians are competent in these areas upon training completion. To ensure that Canadian postgraduate specialty training achieves this, the translation of QI/PS competencies into training standards as part of the implementation of competency-based medical education requires special attention. METHODS: We conducted a cross-specialty, multi-method analysis to examine how QI/PS was incorporated into the EPA Guides across 11 postgraduate specialties in Canada. RESULTS: We identify cross-specialty variability in how QI/PS is incorporated, positioned, and emphasized in EPAs and milestones. QI/PS was primarily referenced alongside clinical activities rather than as a sole competency or discrete activity. Patterns were characterized in how QI/PS became incorporated into milestones through repetition and customization. QI/PS was also decoupled, conceptualized, and emphasized differently across specialties. CONCLUSIONS: Variability in the inclusion of QI/PS in EPAs and milestones has important implications considering the visibility and influence of EPA Guides in practice. As specialties revisit and revise EPA Guides, there is a need to balance the standardization of foundational QI/PS concepts to foster shared understanding while simultaneously ensuring context-sensitive applications across specialties. Beyond QI/PS, this study illuminates the challenges and opportunities that lie in bridging theoretical frameworks with practical implementation in medical education, prompting broader consideration of how intrinsic roles and emergent areas are effectively incorporated into competency-based medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".