Five years of competency-based medical education in Canadian urology
Bibliographic record
Abstract
INTRODUCTION: In 2018, competency-based medical education (CBME) was introduced to Canadian urology residency training. We examined learner and faculty experiences with CBME five years post-implementation. METHODS: Two online surveys were developed from a scoping review of CBME literature and expert consultation. They covered aspects including unintended consequences, satisfaction, and challenges. They were distributed to Canadian urology residency program directors, faculty, and senior residents from January to June 2023. Respondents rated agreement/satisfaction using a five-point Likert scale. Descriptive analyses considered scores of 4-5 as agreement/satisfaction and 1-2 as disagreement/dissatisfaction. RESULTS: Twenty-nine faculty members (including 10/13 [77%] program directors) and 33/63 (53%) senior residents responded. Overall, 69% of respondents are unsatisfied with CBME, 19% are neutral, and 11% are satisfied. Anxiety and/or fatigue with CBME are reported by 76% of faculty and 66% of residents. CBME is seen as burdensome: 61% of residents frequently trigger assessment requests, while 66% of faculty feel overwhelmed by the volume of requested assessments. Faculty members (83%) and residents (73%) find CBME time-consuming. Over 50% of respondents believe CBME failed to de-emphasize time-based learning, individualize progression, rapidly identify struggling residents, or improve feedback quality. Over 60% agree that CBME has clarified learning expectations and training stages. CONCLUSIONS: There is prevailing dissatisfaction with CBME within Canadian urology training programs, impacting the well-being of both faculty and residents while falling short of delivering personalized training; however, CBME has provided a structured and transparent framework for trainee advancement. Improvements to CBME are needed beyond its initial five years.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".