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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".