Gender disparities in National Institute of Health funding for hematologic malignancies, hematopoietic stem cell transplantation, and cellular therapeutics.
Bibliographic record
Abstract
11020 Background: Gender inequality in research funding has been studied extensively; however, the literature lacks evidence in Hematology. We investigated trends in National Institutes of Health (NIH) funding for hematologic malignancies (HM), hematopoietic stem cell transplantation (HSCT), and cellular therapeutics (CT). Methods: The data on Hematology funding was retrieved from NIH Research Portfolio Online Reporting Tools (RePORT) Categorical Spending for fiscal years 2018 and 2019. A total of 6351 entries were reported. Only grants (n=1834) that were related to HM, HSCT, and CT were included. After excluding non-relevant, 975 principal investigators (PIs) were included in the analysis. Additional data regarding PIs was ascertained from the Scopus database, LinkedIn, Doximity, and departmental websites, including the number of publications, number of years of active research, H-index, highest degree, gender, and institution. Data were analyzed using SPSS version 21. Bivariate analyses, using chi-square and t-test, and linear regression analyses were performed. Results: In 2018 and 2019, 1834 grants totaling $799.4 million were awarded by the NIH for malignant hematology research (men 1301, 71% vs women 533, 29%). Of 975 PIs, 680 (70%) were men and 295 (30%) were women. Table highlights gender disparities in NIH funding and associated factors. Most of the grant recipients were Ph.D. or M.D./Ph.D. About 70% of total funding was awarded to male PIs. There were no gender differences in the mean number of grants and mean grant amount. Women had significantly lower years of active research and academic productivity. Conclusions: Although the gender gap in academic hematology has decreased in recent years, the latest trend suggests significant gender inequality in NIH funding for malignant hematology, transplantation, and cellular therapy.[Table: see text]
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.010 | 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".