Exploring Diversity in North American Academic Pediatric Otolaryngology
Bibliographic record
Abstract
OBJECTIVE: Representation of women and minority groups is traditionally low in Otolaryngology-Head & Neck Surgery (OHNS). This cross-sectional study aims to assess the difference in gender and racial/ethnic representation within Academic North American pediatric OHNS. STUDY DESIGN: Cross-sectional study of North American pediatric OHNS faculty websites. SETTING: North America. METHODS: Canadian and American residency program registries were searched for accredited OHNS programs. Pediatric OHNS faculty were identified through program websites. Information regarding gender, race/ethnicity, time in practice, research productivity, academic title, and leadership positions was extracted from public profiles and Scopus. Demographic and academic data was also extracted for OHNS and pediatric OHNS department/division chairs. RESULTS: North American academic pediatric OHNS websites listed 516 surgeons, of whom 39.9% were women. Most surgeons were perceived as White (69.0%), followed by Asian (24.0%), Hispanic (3.7%), and Black (3.3%). Women surgeons had lower h-indices, less publications, and less citations than men (P < .001). Despite women surgeons having fewer years in practice (median 8.0 vs 13.0, P < .001), gender-differences in h-index persisted when controlling for years in practice (P < .05). Men surgeons had higher academic titles (P < .001), but there was no gender difference in leadership roles while accounting for years in practice (P = .559). White surgeons had higher academic titles than non-White surgeons (P = .018). There was no racial/ethnic difference in leadership roles (P = .392). CONCLUSION: Most pediatric OHNS surgeons are men and/or White. Significant gender-differences in research productivity and academic title exist, however surgeons of racial/ethnic minority have similar research productivity as their racial/ethnic majority counterparts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".