The Evolution of Racial, Ethnic, and Gender Diversity in US Otolaryngology Residency Programs
Bibliographic record
Abstract
OBJECTIVE: To examine the evolution of racial, ethnic, and gender diversity in US otolaryngology-head and neck surgery residency programs and compare these figures with other residency programs. DESIGN: Retrospective database review. SETTING: US residency programs. METHODS: Information concerning minority and female representation in US residency programs was obtained from annually published graduate medical education reports by the Journal of the American Medical Association from 1975 to 2010. Minority representation among US population and university students was obtained from the US Census Bureau. The racial, ethnic, and gender diversity of otolaryngology residents was then compared with other medical fields (general surgery, family medicine, and internal medicine). RESULTS: Underrepresentation in otolaryngology-head and neck surgery is particularly disconcerting for African Americans (-2.3%/y, P = .09) and Native Americans (1.5%/y, P = .11) given their nonsignificant annual growth rates. Hispanic representation (17.3%/y, P < .0001) is growing in otolaryngology but is half the rate of growth of the Hispanic American population (32.8%/y, P < .0001). There is nonetheless promise for women (70.6%/y, P < .0001) and Asian Americans (63.0%/y, P < .0001), who demonstrated statistically significant growth trends. CONCLUSION: To our knowledge, this is the first study to describe the evolution of female and minority representation among US otolaryngology residents. Despite increasing gender, ethnic, and racial diversity among medical residents in general, female and certain minority group representation in US otolaryngology residency programs is lagging. These findings are in contrast to rising trends of diversity within other residency programs including general surgery.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".