Gender Disparity in Grants and Awards at the National Institute of Health
Bibliographic record
Abstract
Objective The National Institute of Health (NIH) supports the academic career of scientists across the United States (U.S.). It promotes and sponsors scientists in conducting wide-ranging clinical and basic science research. Depending on the duration, research type, and budget, there are various types of grants awarded by NIH. Despite considerable advancement in biomedical sciences, female researchers remain underrepresented in obtaining NIH funding. Through this study, we aim to highlight the gender trends in NIH funding and grants. By doing this, we aim to facilitate effective future policymaking to help achieve gender parity in NIH grants and awards. Methods The data were obtained from the NIH Research Portfolio Online Reporting Tool (RePORT). The extracted data by gender were tabulated showing percentages of females as Research Grant Investigators, Research Career Development Award Recipients and Kirschstein-National Research Service Award (NRSA) Trainees and Fellows, recipients of Research Grants, Research Project Grants (RPGs), and R01 equivalent grants including types 1 or 2, over two decades (1999-2019). Absolute percentage change was also calculated and included in the tables. Results The percentage of females as NIH Research Grant Investigators has increased at centers, research centers as well as for RPGs and Small Business Innovation Research and Small Business Technology Transfer (SBIR/STTR) programs. For Research Career Development Award Recipients and Kirschstein-NRSA Trainees and Fellows, the proportion of female pre-doctoral institutional trainees, post-doctoral fellows, post-doctoral institutional trainees, mentored research career awardees, and other research career awardees have steadily increased. However, there was a decrease in the percentage of female pre-doctoral fellow awardees. The percentage of females receiving all RPGs, R01-New (type 1) and R01-Renewal (type 2) grants has also decreased. Conclusion Despite an overall increase in the percentage of female researchers successfully receiving NIH grants and awards, they continue to lag compared to their male counterparts. With the increasing number of female doctoral graduates, it is imperative to address this disparity in NIH funding.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.006 | 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".