Heterogeneity in urology teaching curricula among Canadian urology residency programs
Bibliographic record
Abstract
INTRODUCTION: Postgraduate education is transitioning to a competency-based curriculum in an effort to standardize the quality of graduating trainees. The learning experiences and opportunities in each institution are likely variable, as no standard exists regarding the teaching curriculum offered through residency. The objective of this study is to examine the various teaching curricula among different Canadian urology residency programs and to identify which teaching modalities are prioritized by program directors. METHODS: A 10-question anonymous survey was sent electronically to program directors at all 12 urology residency programs across Canada. Questions were designed to quantify the time allotted for teaching and to assess the various teaching session types prioritized by programs to ensure the successful training of their graduates. We assessed each program's perceived value of written exams, oral exams, didactic teaching session, and simulation sessions. Responses were assessed using a Likert-scale and a ranking format. Descriptive statistics were performed. RESULTS: Overall survey response rate from residency program directors was 75% (9/12). Sixty-seven percent of programs designated one day of teaching per week, whereas 33% split resident teaching over two days. Review of chapters directly from Campbell-Walsh Urology textbook were deemed the most valuable teaching session. Practice oral exams were also prioritized, whereas most programs felt that simulation labs contributed the least to residency education. All programs included review of the core urology textbook in their weekly teaching, while only 67% of programs included faculty-led didactic sessions and case presentations. Forty-four percent of programs included resident-led didactic sessions. Practice oral exams and simulation labs were the least commonly included teaching modalities. CONCLUSIONS: Although most program directors prioritize the review of chapters in the core urology textbook, we found significant heterogeneity in the teaching sessions prioritized and offered in current urology residency curricula. As we move to standardize the quality of graduating trainees, understanding the impact of variable educational opportunities on residency training may become increasingly important.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".