Gender differences in grant and personnel award funding rates at the Canadian Institutes of Health Research based on research content area: A retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Although women at all career stages are more likely to leave academia than men, early-career women are a particularly high-risk group. Research supports that women are less likely than men to receive research funding; however, whether funding success rates vary based on research content is unknown. We addressed gender differences in funding success rates for applications directed to one or more of 13 institutes, representing research communities, over a 15-year period. METHODS AND FINDINGS: We retrospectively reviewed 55,700 grant and 4,087 personnel award applications submitted to the Canadian Institutes of Health Research. We analyzed application success rates according to gender and the primary institute selected by applicants, pooled gender differences in success rates using random effects models, and fitted Poisson regression models to assess the effects of gender, time, and institute. We noted variable success rates among grant applications directed to selected institutes and declining success rates over time. Women submitted 31.1% and 44.7% of grant and personnel award applications, respectively. In the pooled estimate, women had significantly lower grant success (risk ratio [RR] 0.89, 95% confidence interval [CI] 0.84-0.94; p < 0.001; absolute difference 3.2%) compared with men, with substantial heterogeneity (I2 = 58%). Compared with men, women who directed grants to the Institutes of Cancer Research (RR 0.86, 95% CI 0.78-0.96), Circulatory and Respiratory Health (RR 0.74, 95% CI 0.66-0.84), Health Services and Policy Research (RR 0.78, 95% CI 0.68-0.90), and Musculoskeletal Health and Arthritis (RR 0.80, 95% CI 0.69-0.93) were significantly less likely to be funded, and those who directed grants to the Institute of Aboriginal People's Health (RR 1.67, 95% CI 1.0-2.7) were more likely to be funded. Overall, women also had significantly lower personnel award success (RR 0.75, 95% CI 0.65-0.86; p < 0.001; absolute difference 6.6%). Regression modelling identified that the effect of gender on grant success rates differed by institute and not time. Study limitations include use of institutes as a surrogate identifier, variability in designation of primary institute, and lack of access to metrics reflecting applicants, coapplicants, peer reviewers, and the peer-review process. CONCLUSIONS: Gender disparity existed overall in grant and personnel award success rates, especially for grants directed to selected research communities. Funding agencies should monitor for gender differences in grant success rates overall and by research content.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".