Occupational segregation by gender in veterinary specialties: Who we are choosing, or who is choosing us
Bibliographic record
Abstract
OBJECTIVE: Gender demographics vary across specialties including surgery, internal medicine, cardiology, neurology, and oncology. Our objective was to determine whether residency selection or the decision to apply for training drives these differences. STUDY DESIGN: Retrospective cohort study. SAMPLE POPULATION: Matched and unmatched residents lists from Veterinary Internship and Residency Matching Program (VIRMP) from 2011 to 2020. Comparative Data Reports from the American Association of Veterinary Medical Colleges from 2010 to 2019. METHODS: Names for matched and unmatched residents with addresses in the United States or Canada were coded for gender for seven programs: large and small animal surgery, large and small animal medicine, cardiology, neurology, and oncology. Match rate by gender was compared using chi-square tests. Gender demographics of applicants were compared to demographics of graduates using tests of two proportions. RESULTS: No differences were observed between genders for the likelihood of successfully matching into each residency program evaluated except in large animal internal medicine. Women (44.2%) were slightly more likely to match, overall, than men (39.0%, p = .003). The proportions of women applying for residencies overall (70.7%), in large and small animal surgery (66.1%, 62.2%), cardiology (70.2%), and neurology (70.7%) were lower than the proportion of female graduates (79%; p's < .001). CONCLUSION: No evidence for gender bias was detected in the VIRMP resident selection process. Female veterinary graduates seemed less likely to apply for residencies than their male counterparts. IMPACT: Occupational segregation seems to stem from the decision to apply for residency. Interventions aimed at altering gender demographics in specialized medicine should target potential applicants.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".