Women’s health research funding in Canada across 15 years suggests low funding levels with a narrow focus
Bibliographic record
Abstract
BACKGROUND: Females have been underrepresented in preclinical and clinical research. Research on females is important for conditions that directly affect women, disproportionately impact women, and manifest differently in women. Sex and gender mandates were introduced, in part, to increase women's health research. This study aimed to understand how much of women's health research is being funded in open grant competitions in Canada that fall under the top burden and/or death of disease for women globally. METHODS: Publicly available funded Canadian Institute of Health Research (CIHR) project grant abstracts from 2009 to 2023 were coded for the mention of female-specific research to assess what percentage of grant abstracts focused on the top 11 areas of global disease burden and/or death that disproportionately affect females. We also examined changes from 2020 to 2023 in the representation of grant abstracts that mentioned sex, gender, or two-spirit, lesbian, gay, bisexual, trans, queer, intersex (2S/LGBTQI). RESULTS: The percentage of abstracts mentioning sex or gender doubled whereas the percentage of abstracts mentioning 2S/LGBTQI quadrupled from 2020 to 2023, but remained at under 10% of overall funded abstracts. In contrast, female-specific research representation remained at ~ 7% of all research. Under 5% of the total funded grant abstracts mentioned studying one of the top 11 global burdens of disease and/or death for women over 15 years. Of the 681 female-specific grants, cancer research accounted for 35% of funding (or 2.25% of overall grants), whereas the other top 10 collectively accounted for 37% of female-specific funding (or 2.35% overall) across 15 years. The percentage of overall funding towards understanding female-specific contributions to cardiovascular disease was 0.70% followed by diabetes (0.34%), HIV/AIDS (0.54%), depression (0.32%), anxiety (0.17%), musculoskeletal disorders (0.13%), dementia (0.09%), respiratory disorders (0.06%), headache disorders (0.002%) and low back pain (0.01%). CONCLUSIONS: Research acknowledging the sex and gender population in CIHR abstracts is increasing but remains at under 10% while the percentage of funding for women's health remains unchanged at 7% of funded grants across 15 years.
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.
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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".