Assessing gender trends in Canadian urology
Bibliographic record
Abstract
INTRODUCTION: The number of female medical students and physicians entering the workforce is increasing. Despite this trend, some surgical specialties are still considered male-dominant. Urology has a significant male predominance in both residency and independent practice. This male predominance could have an impact on the physician work force, mentorship opportunities for females pursuing surgery, and on medical student attraction to urology as a specialty. Research conducted in the U.S. has shown that although fewer females enter the field of urology, acceptance rates between the two genders are similar. This study aims to identify if a trend towards gender-specific acceptance into urology residency exists within Canada. We also seek to identify if gender trends in acceptance to urology differ from other surgical specialties in Canada and assess the current workforce trends in Canadian urological practice. METHODS: Logistic regression analyses were used to assess if any significant difference exists between the rates of female and male applicant acceptance into urology. These rates were then compared to the rates of female and male acceptance into surgical residency as a whole and to specific surgical specialties, such as general surgery, orthopedics, and otolaryngology. RESULTS: Within urology applicants, there is no evidence that the success rate over time between males and females differs (p=0.47). Within surgical residency applicants, there is no evidence that the success rate over time differs between male and female applicants (p=0.84). In comparing these two rates, there is also no significant difference between rates of acceptance to urology vs. surgery in general for female applicants (p=0.45). General surgery has a higher growth of females entering into the specialty compared to urology (p=0.026). Conversely, otolaryngology (p=0.123) and orthopedics (p=0.163) did not show a significant difference in the rates of female acceptance as compared to males over time. Our small sample size of 451 applicants over the 10-year time span (122 female, 329 male) could represent a limitation, however, we did ensure to analyze a 10-year sample to attempt to get an accurate representation of any trends. CONCLUSIONS: Our data identifies that there is no significant trend toward male acceptance into urology over female applicants. There is no significant difference related to female acceptance specifically into urology or any difference between rates of females accepted into urology as compared to all other surgical subspecialties combined.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".