Core competencies in surgery: evaluating the goals of urology residency training in Canada.
Bibliographic record
Abstract
BACKGROUND: In Canada and the United States, the relevance and utility of training objectives as perceived by practising surgeons is rarely examined. We sought to determine if urology residency training objectives reflect the broad realities of urologic practice. METHODS: A survey, based on the Royal College of Physicians and Surgeons of Canada training objectives for urology, was designed and validated. All 418 full-time practising members of the Canadian Urological Association were surveyed. RESULTS: The overall response rate was 63%. Many specialized clinical areas of urology that receive little emphasis in the training objectives were rated as useful by the majority: laparoscopic surgery (92%), percutaneous renal access (86%), transrectal ultrasonography (84%), pediatric urology (81%), extracorporeal shockwave lithotripsy (70%), urethral reconstruction (66%) and adrenal surgery (62%). Microsurgery and transplantation were perceived as less useful (54% and 22% respectively). Virtually all nonsurgical training objectives were regarded as useful components of training; however, in the opinion of the majority of respondents residency did not prepare them for many of these: the challenges of office and hospital administration (91% and 89% not prepared [NP]), building a referral base (67% NP), time management (60% NP) and providing care under financial constraints (60% NP). CONCLUSION: The study results support the current training objectives and indicate areas requiring increased emphasis.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".