Do you want to be a urologist? Gender differences for medical student perception of urology
Bibliographic record
Abstract
INTRODUCTION: Gender inequality has been prevalent in the history of medicine, specifically within surgical specialties. Though there have been advances, urology has remained overwhelmingly male-dominant, with slow growth in female recruitment. This survey study evaluated whether gender-related differences in the perception of urology are present among future applicants that could account for gender disparity seen in recruitment. METHODS: An anonymized, online survey was distributed to medical students enrolled at the Max Rady College of Medicine during the 2022-2023 semester. Attracting and deterring survey statements were created using current literature to guide topics of interest. Participants rated each statement using a five-point Likert scale with optional supplemental qualitative responses. Likert ratings were compared using a Mann-U-Whitney calculation between self-identifying male and female participants. RESULTS: We received 90 responses over six weeks, achieving a response rate of 23%. Female students, compared to their male peers, were deterred by factors such as working in a male-dominated specialty (p<0.001) and working with primarily male patients (p<0.001). There were no significant gender-related differences for statements pertaining to interest in surgery, work-life balance, or exposure to urology. CONCLUSIONS: In this survey study, the biggest deterrents reported by female medical students to entering urology were working in a male-dominated profession and seeing primarily male patients. There were no significant gender-related differences for questions relating to interest in surgery, work-life balance, and exposure to urology.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".