Demographic and Academic Productivity Trends Among American Head & Neck Society Fellows Over a 20-Year Period
Bibliographic record
Abstract
Importance: Historical data reveal that, compared with women, men are more likely to pursue a head and neck surgical oncology fellowship, but little is known about possible gender differences in academic productivity. Objective: To assess demographic trends and academic productivity among American Head & Neck Society (AHNS) fellowship graduates. Design, Setting, and Participants: This cross-sectional study used electronically published data from the AHNS on fellowship graduates in the US and Canada from July 1, 1997, to June 30, 2022. Scopus was used to extract h-indices for each graduate. Exposure: Scholarly activity. Main Outcomes and Measures: Main outcomes were changes in demographic characteristics and academic productivity among AHNS graduates over time. Data analysis included effect size, η2, and 95% CIs. Results: A total of 691 AHNS fellowship graduates (525 men [76%] and 166 women [24%]) were included. Over the study period, there was an increase in the number of programs offering a fellowship (η2, 0.84; 95% CI, 0.68-0.89) and an increase in the absolute number of women who completed training (η2, 0.66; 95% CI, 0.38-0.78). Among early-career graduates pursuing an academic career, there was a small difference in the median h-index scores between men and women (median difference, 1.0; 95% CI, -1.1 to 3.1); however, among midcareer and late-career graduates, there was a large difference in the median h-index scores (midcareer graduates: median difference, 4.0; 95% CI, 1.2-6.8; late-career graduates: median difference, 6.0; 95% CI, 1.0-10.9). A higher percentage of women pursued academic positions compared with men (106 of 162 [65.4%] vs 293 of 525 [55.8%]; difference, 9.6%; 95% CI, -5.3% to 12.3%). Conclusions and Relevance: This cross-sectional study suggests that women in head and neck surgery begin their careers with high levels of academic productivity. However, over time, a divergence in academic productivity between men and women begins to develop. These data argue for research to identify possible reasons for this observed divergence in academic productivity and, where possible, develop enhanced early faculty development opportunities for women to promote their academic productivity, promotion, and advancement into leadership positions.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".