“When in Doubt, Ask the Patient”: A Quantitative, Patient-Oriented Approach to Formative Assessment of CanMEDS Roles
Bibliographic record
Abstract
Introduction: Since the introduction of competency-based frameworks into postgraduate medical curricula, educators have struggled to implement robust assessment tools that document the progression of necessary skills. The global movement towards competency-based medical education demands validated assessment tools. Our objective was to provide validity evidence for the Ottawa CanMEDS Competency Assessment Tool (OCCAT), designed to assess clinical performance in the communicator, professional, and health advocate CanMEDS roles. Methods: We developed the OCCAT, a 29-item questionnaire informed by specialty-specific Entrustable Professional Activities and consultation with stakeholders, including patients. Our sample included nine neonatal-perinatal medicine and maternal fetal medicine fellows rotating through antenatal high-risk clinics at the Ottawa Hospital. Following 70 unique encounters, the OCCAT was completed by patients and learners. Generalizability theory was used to determine overall reliability of scores. Differences in self and patient ratings were assessed using analyses of variance. Results: = .007. Variability analysis demonstrated that trainee scores varied across all competencies, suggesting both groups were able to recognize competencies as distinct and discriminate favorable behaviors belonging to each. Discussion: Our findings lend support to the movement to integrate self-assessment and patient feedback in formal evaluations for the purpose of enriched learner experiences and improved patient outcomes. We anticipate that the OCCAT will facilitate bridging to competency-based medical education.
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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 | 0.073 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".