Assessing Canadian medical students’ confidence in undergraduate urologic training and preferences for teaching methods
Bibliographic record
Abstract
INTRODUCTION: Given the aging population, urologic conditions are increasingly prevalent in primary care, necessitating well-prepared medical graduates to recognize and manage essential complaints. This study assessed medical students' confidence in managing common urologic conditions, identified preferred teaching methods, and examined the role of the Canadian Undergraduate Urological Curriculum (CanUUC) in their education. METHODS: A survey was distributed to third- and fourth-year Canadian medical students, assessing their self-confidence in history taking, diagnosis, management planning, and physical examination for 12 urologic conditions. The survey also explored preferred teaching methods and awareness of CanUUC. Statistical analysis included ANOVA and t-tests to determine significant differences in confidence across various factors. RESULTS: A total of 117 medical students and 10 first-year urology residents responded. Students felt equally confident about taking histories (3.51±1.19), proposing diagnoses (3.38±1.19), and performing physical examinations (3.58±1.16) while demonstrating lower confidence (p<0.001) for management planning (3.16±1.25). Confidence was highest for urinary tract infections and lowest for male infertility. Furthermore, students who completed urology rotations reported higher confidence in history taking (3.67±0.69, p=0.003) and management planning (3.35±0.66, p=0.003). Direct clinical exposure, simulations, and case-based discussions were the preferred learning methods. Only seven (6%) students were aware of CanUUC, with five (4.3%) using it. CONCLUSIONS: Medical students have moderate confidence in handling urologic conditions, with higher comfort among those who completed urology rotations. Implementing targeted curriculum enhancements and integrating resources like the CanUUC could address these educational gaps and lead to improved patient outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".