Assessment of the Intrinsic CanMEDS Roles in Diagnostic Radiology Residents using an Objective Structured Clinical Assessment (OSCE)
Bibliographic record
Abstract
This article was migrated. The article was not marked as recommended. Purpose: Non-Medical Expert or Intrinsic CanMEDS Roles, as outlined by the Royal College of Physicians and Surgeons of Canada, can be difficult to evaluate in an objective and specialty-specific manner. This study investigates an Objective Structured Clinical Examination (OSCE) evaluation tool to assess these competencies for Diagnostic Radiology Residents. Methods: A five station CanMEDS OSCE was developed for postgraduate year 3 and 4 Residents to evaluate the Communicator, Collaborator, Manager, Health Advocate, Scholar and Professional Roles. Performance was assessed by postgraduate year 5 Residents using standardized scoring rubrics. CanMEDS OSCE scores were correlated with American College of Radiology (ACR) scores and Medical Expert OSCEs. Results: Seventy Residents in three separate cohorts (n=21, 26, 23) participated in the CanMEDS OSCE. Mean station scores were consistent across cohorts. In general, one-way ANOVAs showed no effect of postgraduate year on station scores. There were no significant correlations between CanMEDS OSCE scores and ACR exam scores or CanMEDS OSCE scores and Medical Expert OSCE scores, demonstrating divergent construct validity. In turn, this indicates construct validity for the CanMEDS OSCE by demonstrating that unique competencies are being measured. In contrast, there was a correlation between ACR exam scores and Medical Expert OSCE scores, confirming that these both assess the same construct. Conclusions: An OSCE can be a useful assessment tool to assess Intrinsic CanMEDS Roles in a specialty-specific manner. Correlational analyses indicate that unique competencies are being evaluated that are not captured by other traditional assessment means.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".