Assessing the scholar CanMEDS role in residents using critical appraisal techniques
Bibliographic record
Abstract
BACKGROUND: In this brief report, we describe two ways in which we assessed the Scholar CanMEDS role using a method to measure residents' ability to complete a critical appraisal. These were incorporated into a modified OSCE format where two stations consisted of 1) critically appraising an article and 2) critiquing an abstract. METHOD: Residents were invited to participate in the CanMEDS In-Training Exam (CITE) through the Office of Postgraduate Medical Education. Mean scores for the two Scholar stations were calculated using the number of correct responses out of 10. The global score represented the examiner's overall impression of the resident's knowledge and effort. Correlations between scores are also presented between the two Scholar stations and a paired sample t-test comparing the global mean scores of the two stations was also performed. RESULTS: Sixty-three of the 64 residents registered to complete the CanMEDS In-Training Exam including the two Scholar stations. There were no significant differences between the global scores of the Scholar stations showing that the overall knowledge and effort of the residents was similar across both stations (3.8 vs. 3.5, p = 0.13). The correlation between the total mean scores of both stations (inter-station reliability) was also non-significant (r = 0.05, p = 0.67). No significant differences between senior residents and junior residents were detected or between internal medicine residents and non-internal medicine residents. CONCLUSION: Further testing of these stations is needed and other novel ways of assessing the Scholar role competencies should also be investigated.
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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 | 0.042 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".