P.022 Gender disparity in canadian institutes of health research funding within neurology
Bibliographic record
Abstract
Background: Despite efforts to advance equity, women face gender-based barriers in research, including fewer senior authorship and grant opportunities. We examined gender disparities in Canadian Institutes of Health Research (CIHR) funding for Canadian neurology divisions and departments. Methods: Data on CIHR grant recipients and metrics (duration, quantity, and contribution) within Canadian neurology divisions and departments (2008-2022) were acquired from the CIHR Funding Decisions Database. Gender-based differences in grant prevalence, duration, and contribution amount within neurology were calculated with subgroup analysis for Canadian neurologists and Project Grant awards. Results: 1604 grants were awarded to Canadian neurology divisions and departments between 2008-2022. Women received fewer grants (41.46%), less funding (p<0.0001), and shorter grant durations (p<0.0001) than men annually. Women comprised the minority of recipients (45.47%) and were less likely to be awarded grants (p<0.001) annually relative to men. Differences were consistent in subgroup analyses, except grant durations were equal across genders in Project Grant awards. Conclusions: Gender disparities persist in CIHR grant funding to Canadian neurology divisions and departments. Women receive fewer grants, lower contribution amounts, and are less likely to be recipients compared to men. Future work includes addressing gender differences and continuing to evaluate CIHR funding to provide equitable opportunities for women.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".