National institutes of health: Analysis of gender differences in anesthesiology research funding
Bibliographic record
Abstract
Although studies have shown an increase in the representation of women in academic anesthesiology, it remains one of the medical specialties most dominated by men. While gender disparities have been identified in academic anesthesiology, literature on discrepancies in funding allocated by the National Institutes of Health (NIH) is scarce. The objective of the present study was to explore these discrepancies from 2017 to 2020 and assess potential changes in funding trends over time. Publicly available funding data was retrospectively obtained from the NIH Research Portfolio Online Reporting Tools Expenditure and Results (RePORTER) database for fiscal years 2017 to 2020. Information regarding each principal investigator (PI) was obtained from the Scopus database and institutional websites. For statistical comparison of continuous variables, Mann-Whitney U tests were performed. Simple linear regression analyses assessed the relationship between fiscal year and number of NIH grants awarded to PIs. Median NIH amount per grant [interquartile range (IQR)] was determined to be $359,038 ($233,947–$476,933) for PIs that were men, greater than that of $330,865 ($164,268–$458,785) for PIs that were women (p < .05). Similarly, men received a greater median NIH grant amount per PI, with a value of $348,751 ($222,043–$442,075), compared to women who received $268,634 ($161,159–$414,384) (p < .05). When stratified by terminal degree, significantly higher median grant amounts (p < .05) were awarded to MD and PhD holders who were men versus their women counterparts. Lastly, an increasing trend in obtaining NIH grants between 2017 and 2020 was observed for PIs that were men overall, including PIs holding MD/PhD degrees (p < .05). No such trend was observed for PIs who were women. This study demonstrates a significantly greater number of NIH grants and higher award values allocated to researchers who were men than researchers who were women in academic anesthesiology over the past four years. Moreover, an increase in the number of grants secured by PIs who were women from 2017–2020 was not observed. In the future, longitudinal trends in NIH funding for principal investigators (PIs) of both genders in anesthesiology should be investigated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".