Analysis of gender gap in North American radiation oncology society committees
Bibliographic record
Abstract
INTRODUCTION: Achieving gender equity in medicine remains elusive. We evaluated the gender distribution within executive roles of North American radiation oncology societies and assessed the relationship between gender, committee rank, academic rank and research productivity. METHODS: 205 committee members were identified from four radiation oncology society webpages. Members were categorised into leadership positions and academic ranks. For each, the Hirsch index (h-index), m-index, publications, citations and years of research were extracted from the Scopus database. This study complies with Sex and Gender Equity in Research (SAGER) guidelines for observational studies. RESULTS: Radiation oncology committees were comprised of significantly more men (72.7%, p<0.0001). Within these committees, men significantly outnumbered women in leadership positions, holding 73.5% of positions (p<0.0001). This trend extended to academic ranks and research productivity, with men occupying 72.7% of positions (p<0.001) and having greater mean (±SE of the mean) research productivity with more publications (171.1±12.9 vs 97.3±18.7, p<0.0001), citations (7785±785.1 vs 44061±1168, p=0.0002), h-index (36.17±2.2 vs 22.9±3.6, p=0.0002) and years of research (29.8±1.2 vs 16.7±1.7, p<0.0001). The m-index showed no significant gender difference among men and women (1.2±0.06 vs 1.2±0.09, p>0.05). CONCLUSION: While men occupy more leadership roles and show higher research productivity as measured by the h-index, accounting for years of active research with the m-index showed no significant difference between genders. This underscores the need for targeted strategies such as mentorship programmes and gender-equity policies to promote greater representation of women in the discipline.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".