Gender Differences in Research Project Grants and R01 Grants at the National Institutes of Health
Bibliographic record
Abstract
Objectives The National Institutes of Health (NIH), which is the world's largest funding source for research, offers various types of competitive grants depending on the duration, research type, and budget. The Research Project Grant (RPG) is the oldest mechanism for grant allocation that is used by the NIH. In this study, we explored the gender trends of NIH RPGs and R01 grants over the last two decades. Methods By utilizing the NIH Research Portfolio Online Reporting Tool (RePORT), data for gender were extracted, and the percentage of women as RPGs Investigators, R01-equivalent grant including R01 type 1 and type 2 grant awardees, from 1998 to 2019 were tabulated. The absolute change was calculated. Results From 1998 to 2019, the percentage of female RPG awardees has increased. However, the success rates for female RPG applicants have decreased during the same period. The funding and success rates for new R01 awards have been similar for both men and women, but women have been less successful at the renewal of R01-equivalent awards. Conclusion Gender disparity exists in awardees of higher RPGs, including the R01 award. This highlights the need for further actions to ensure gender parity in grant allocations at the NIH.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".