Assessing the goals of urology residency training: perceptions of practicing urologists in British Columbia.
Bibliographic record
Abstract
PURPOSE: In an effort to evaluate the perceived utility of specific Royal College of Physicians and Surgeons of Canada (RCPSC) urology residency training objectives we conducted a survey of the practicing urologists of British Columbia (BC). MATERIALS AND METHODS: A two page semi-structured survey was designed. Validity was evaluated for clarity, content and ease of completion. The survey was mailed-out to all 61 practicing urologists in BC. The survey population was divided into urban, rural, and academic according to location of practice. RESULTS: Survey response rate was 79% with varying subgroup rates: urban-69% (20/29), rural-94% (17/18) and academic 86% (12/14). Specific clinical components of training were rated as "useful" by the majority of all respondents: pediatric urology (93%), laparoscopy (88%), TRUS (77%), percutaneous renal access (74%), urethral surgery (72%), microsurgery (62%). Renal transplantation was rated "not useful" by 74% of respondents. TRUS, percutaneous renal access and adrenal surgery were perceived as useful by the majority of those practicing in rural and non-academic urban centers compared to those in academic centers where the majority rated these skills as "not useful". Virtually all non-clinical components of training were rated as "useful". The majority of respondents felt that residency training prepared them for the following challenges: accepting responsibility for patient care, assessing scientific literature, ethical decision-making and communication. The majority of respondents felt that residency did not prepare them for the following challenges: time and office management, hospital administration and providing care within a constrained system. CONCLUSION: Specific clinical and non-clinical areas of training have high perceived utility in all settings of practice. Certain clinical components of training have high perceived utility only in specific settings of practice. There are many non-clinical components of practice, which are perceived to be important, but for which BC urologists feel inadequately prepared for by their residency training programs. If consistent across Canada, these findings may facilitate a rational approach to the modification of the objectives for urology residency training.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".