Gender Differences Among Academic Radiation Oncology National Institutes of Health (NIH) Funding Recipients
Bibliographic record
Abstract
Purpose The purpose of our study was to evaluate National Institutes of Health (NIH) funding recipients between 2016 and 2019 to determine if there was an association between gender, research productivity, academic rank, leadership positions, and post-graduate awards. Materials and Methods The NIH Research Portfolio Online Reporting Tools Expenditure and Results (RePORTER) website was used to retrieve data for grants in Radiation Oncology from 2016-2019. Demographics and profiles of awardees were retrieved from institutional websites, LinkedIn, and Doximity. Publication metrics were collected through the Scopus database. Mann-Whitney U tests and chi-square analyses were performed to compare and determine associations between gender and other variables. Results Three hundred and forty radiation oncology principal investigators (PIs) were included in this study, of whom 76% were men. Of the 776 total NIH grants awarded, 62% of the grants had a sole male PI and 1% had two or more PIs in which the contact PI and co-PI were women. Between the genders of PIs in this sample, there was no significant difference in highest academic rank, leadership positions (i.e., chair, director, founder, president, and other), and post-graduate honors and awards. Total publications, years of active research, h-index, and m-index were higher amongst men in the professor category but were largely similar between genders in the associate and assistant professor categories. Conclusions The results demonstrate that most NIH grants in radiation oncology were awarded to men. Strategies that increase women in radiation oncology (RO), as well as those that increase NIH grants amongst women may also increase the prevalence of women in senior academic ranks and 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".