The Canadian Urology Fair: a model for minimizing the financial and academic costs of the residency selection process.
Bibliographic record
Abstract
INTRODUCTION: In 1994, the Canadian urology residency training programs designed the "Canadian Urology Fair"--a single-site (Toronto, Ont.), 1-day fair to conduct the personal interview portion of the residency selection process. The objective of the current study was to evaluate the success of the Urology Fair in achieving its original goals of decreasing the financial burden and minimizing time away from medical training for applicants and faculty. METHODS: Both candidates and Canadian urology training programs were surveyed regarding the financial and academic costs (days absent) of attending the 2001 Urology Fair. Data from the 2001 Canadian Resident Matching Service (CaRMS) was used to compare the financial and academic costs of attending personal interviews incurred by candidates declaring urology as their first-choice discipline to candidates interviewing with other surgical specialties throughout Canada. RESULTS: Financial costs incurred by candidates to attend the Urology Fair (mean Can dollar 367) were significantly lower than candidates' estimated costs of attending on-site interviews at the individual programs (mean Can dollar 2065). The financial costs of attending personal interviews by CaRMS applicants declaring urology as their first-choice discipline (mean Can dollar 2002) were significantly lower than the costs incurred by applicants interviewing with other surgical disciplines (mean Can dollar 2744). Financial costs to urology programs attending the fair (mean Can dollar 1931) were not significantly greater than the programs' estimated costs of conducting on-site interviews at their respective program locations (mean Can dollar 1825). Days absent from medical school to attend interviews were significantly lower among CaRMS applicants declaring urology as their first-choice discipline (3 d) compared with applicants who interviewed with other surgical specialties (9.1 d). CONCLUSION: The Canadian Urology Fair represents an innovative and efficient method for residency programs to conduct the personal interview portion of the residency selection process and should serve as a model for making the interview process less expensive and time-consuming for both candidates and faculty.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".