P.053 Insights from the first eighteen months of CBME implementation across Canadian neurology residency training programs
Bibliographic record
Abstract
Background: Canadian neurology residency programs recently transitioned to Competence Based Medical Education (CBME), designed to provide residents with stage-appropriate learning to develop and demonstrate competence. The successful implementation of CBME requires iterative evaluation as the adoption process may differ from the intended design due to systemic or program-specific factors. This study aims to (1) examine the variability in CBME implementation across Canadian neurology residency programs; (2) determine the barriers toward uptake of CBME; and (3) identify the benefits and pitfalls of CBME in neurology residency programs. Methods: A separate national survey was developed for residents and staff neurologists who participated in CBME for at least six months. Surveys were distributed through email, and responses were anonymized. Quantitative data were analyzed by response frequency and mean, where applicable. Free-form responses were analyzed qualitatively. Results: Staff neurologists felt prepared for CBME, but were divided on its fairness and impact on education quality. Residents experienced frequent but not necessarily timely or high-quality feedback. Barriers to implementation included increased paperwork, dissatisfaction with online platforms used to facilitate CBME, and bidirectional burden of initiating evaluations. Conclusions: Staff and residents have expressed unique perspectives on the first iteration of CBME. There remain opportunities for improvement in subsequent iterations.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".