Identifying factors influencing specialty choice in urology by female medical students
Bibliographic record
Abstract
INTRODUCTION: As of 2019, females represented 11% of the urology workforce in Canada. Lack of female role models, quality of life, and gender/sex discrimination may be important deterrent factors to female applicants entering surgical specialties. Limited research exists on which factors are important in choosing urology as a specialty by female applicants. In this study, we aimed to determine which factors affect specialty choice in urology by medical school applicants and to identify any disparities by sex. METHODS: From November 2022 to May 2023, a survey was diffused to medical students enrolled in all Canadian medical schools. The questionnaire included 23 factors that may affect specialty choice. A five-point Likert scale was used to assess each factor's influence on the student's interest in urology. Pearson-Chi squared test was used to compare response rates between sexes. RESULTS: A total of 424 Canadian medical students responded to the survey. Common incentivizing factors for choosing urology as a specialty was medical-surgical approach, doctor-patient relationship, and financial benefits. Common deterrent factors were perception that urology is a male-dominated field, lifestyle of surgical residencies, and lack of female role models in urology. Females were more likely to report lower clinical exposure to urology and be deterred by the male predominance in the field. CONCLUSIONS: While female medical students are more likely to be disincentivized to choose urology as a specialty due to it being a male-dominated field, early exposure through research, role models, or shadowing is essential to incentivize interest in urology among female medical students.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".