Gender disparity in Canadian Institutes of Health Research funding within neurology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Despite efforts to advance equity, diversity and inclusion, women face gender-based barriers in research, including in neurology. Compared with men, women are less likely to hold leadership positions and be senior authors. Gender disparities in grant funding within neurology have yet to be investigated. We examine gender disparities in Canadian Institutes of Health Research (CIHR) funding for Canadian neurology divisions and departments. METHODS: Data on CIHR grant recipients and metrics (grant contribution, duration and quantity) within Canadian neurology divisions and departments between 2008 and 2022 were acquired from the CIHR Funding Decisions Database. Gender identity was determined by a validated application programming interface. Gender-based differences in CIHR grant contribution amount, duration and prevalence within neurology were calculated. Subgroup analysis was conducted for Canadian-licensed neurologists and Project Grant awards. RESULTS: 1604 grants were awarded to Canadian neurology divisions and departments between 2008 and 2022. Compared with men, women received less funding (p<0.0001), shorter grant durations (p<0.0001) and fewer grants (41.5%) annually. Women comprised the minority of recipients (45.5%) and were less likely to be awarded grants (p<0.001) annually relative to men. Differences were consistent in subgroup analyses, except for equal grant durations observed across genders in Project Grant awards. CONCLUSION: We report gender disparities in CIHR grant funding to Canadian neurology divisions and departments. Women receive lower contribution amounts, shorter grant durations and fewer grants than men. Future recommendations include addressing gender differences and continuing to evaluate CIHR funding to provide equal opportunities for women in research and funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it