Evolution of gender representation among Canadian OTL-HNS residents: A 27-year analysis
Bibliographic record
Abstract
BACKGROUND: The proportion of females enrolling into medical schools has been growing steadily. However, the representation of female residents among individual specialties has shown considerable variation. The purpose of this study was to compare the trends of gender representation in Otolaryngology - Head and Neck Surgery (OTL-HNS) residency programs with other specialty training programs in Canada. In order to contextualize these findings, a second phase of analysis examined the success rate of applicants of different genders to OTL-HNS residency programs. METHOD: Anonymized data were obtained from the Canadian Residency Matching Service (CaRMS) and from the Canadian Post-M.D. Education Registry (CAPER) from 1988 to 2014. The differences in gender growth rates were compared to other subspecialty programs of varying size. Descriptive analysis was used to examine gender representation among OTL-HNS residents across years, and to compare these trends with other specialties. Bayesian hierarchical models were fit to analyze the growth in program rates in OTL-HNS based on gender. RESULTS: CaRMS and CAPER data over a 27 year period demonstrated that OTL-HNS has doubled its female representation from 20% to 40% between 1990 and 1994 and 2010-2014. The difference in annual growth rate of female representation versus male representation in OTL-HNS over this time period was 2.7%, which was similar to other large specialty programs and surgical subspecialties. There was parity in success rates of female and male candidates ranking OTL-HNS as their first choice specialty for most years. CONCLUSIONS: Female representation in Canadian OTL-HNS residency programs is steadily increasing over the last 27 years. Large variation in female applicant acceptance rates was observed across Canadian universities, possibly attributable to differences in student body or applicant demographics. Factors influencing female medical student career selection to OTL-HNS require further study to mitigate disparities in gender representation and identify barriers to prospective female OTL-HNS 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".