Perspectives on Involving Patients in the Teaching and Assessment of Entrustable Professional Activities in Competence by Design
Bibliographic record
Abstract
BACKGROUND: Involving patients in teaching and assessing entrustable professional activities (EPAs) within competency-based medical education (CBME) enhances the authenticity of postgraduate medical education and helps develop learners' skills. However, CBME, including Competence by Design in Canada, lacks clear guidance on involving patients. This study examines faculty members' perspectives on involving patients in teaching and assessing EPAs. METHODS: We conducted semistructured interviews with 25 faculty members from 14 Canadian medical schools across eight residency specialties. We asked participants how they envisioned patients being involved in teaching and assessing EPAs during EPA creation, how they thought patients could contribute to teaching and assessing EPAs, and what they perceived as barriers. We analysed the data thematically. FINDINGS: Faculty members view patients as subjects for teaching EPAs rather than as teachers of EPAs. However, they noted that patients can teach aspects of EPAs through storytelling, if provided the opportunity. They recognized the value of patients as assessors, particularly in formative assessments of learners' nontechnical skills embedded within EPAs. Nevertheless, they expressed concerns about patient involvement in summative assessments and a lack of EPAs focusing on skills that patients can assess. They noted several barriers to involving patients in teaching and assessing EPAs. CONCLUSION: Our study underscores the pressing need for change and the crucial role of more explicit guidance on involving patients in teaching and assessing EPAs. This is not just a matter of academic interest, but a step towards enhancing the quality of medical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".