What traits do urology programs value in elective students?
Bibliographic record
Abstract
INTRODUCTION: Electives strongly influence residency selection. While the CanMEDS framework outlines core competencies, the specific traits most valued by urology programs during electives remain unclear. METHODS: We surveyed selection committee members in Canadian urology residency programs. Using the CanMEDS framework, we developed 21 competencies and designed a best-worst scaling survey, where respondents selected the most and least important items from 21-question sets. A hierarchical Bayes model was used to calculate probability scores to rank each item and CanMEDS role. Scores reflect the likelihood of being chosen as most important. RESULTS: Thirteen respondents completed the survey (34% response rate). Traits related to professionalism, initiative, and reliability ranked highest. Specifically, "Demonstrating appropriate behavior through honesty, integrity, punctuality, and respect" (12.4% [95% credible interval (CI) 11.9, 13.0]) and "Seeking out responsibilities by helping with rounds, doing consults, and organizing patient handover and discharge" (9.7% [8.5, 10.9]) were top ranked. In contrast, "Incorporating evidence on health disparities in urology when presenting cases or research findings" (0.1% [0.0, 0.2]) and "Discussing barriers to care, such as cost and access, and proposing solutions during rounds when appropriate" (0.0% [0.0, 0.1]) were lowest ranked. Among CanMEDS roles, Professional (9.9%) and Leader (8.1%) ranked highest, while Health Advocate (0.2%) and Scholar (1.8%) ranked lowest. CONCLUSIONS: Canadian urology programs prioritize professionalism, initiative, and reliability during electives. In contrast, scholarly and advocacy competencies are viewed as less critical, possibly due to the short duration and clinical focus of electives. Our findings can guide students and programs in aligning expectations during urology electives.
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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 | 0.004 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".