A 10-year longitudinal evaluation of science policy interventions to promote sex and gender in health research
Bibliographic record
Abstract
BACKGROUND: Over the past decade, the Canadian Institutes of Health Research (CIHR) has implemented multicomponent interventions to increase the uptake of sex and gender in grant applications. Interventions included mandatory reporting on applicant forms, development of resources for applicants and evaluators, and grant review requirements. Here, we aim to inform science policy implementation by describing the 10-year outcomes and lessons learned from these interventions. METHODS: This is a prospective longitudinal study. The population is all applicants across 15 investigator-initiated CIHR competitions from 2011 to 2019 and grant evaluators from 2018 to 2019. Quantitative data were derived from applicants' and grant evaluators' mandatory reporting of sex and gender integration in the grants management database. The application was the unit of analysis. Trends in sex and gender uptake in applications were plotted over time, stratified by research area. Univariate logistic regression was used to assess associations between the sex of the applicant and the uptake of sex and gender, and the latter with funding success. Qualitative review of the quality and appropriateness of evaluators' comments informed the development of discipline-specific training to peer review committee members. Feedback was compiled from a subset of evaluators on the perceived usefulness of the educational materials using a brief questionnaire. RESULTS: Since 2011, 39,390 applications were submitted. The proportion that reported integration of sex rose from 22 to 83%, and gender from 12 to 33%. Population health research applications paid the greatest attention to gender (82%). Across every competition, applications with female principal investigators were more likely to integrate sex (odds ratio [OR] 1.60, 95% confidence interval [CI] 1.50-1.63) and gender (OR 2.40, 95% CI 2.29-2.51) than those who identified as male. Since 2018, applications that scored highly for the integration of sex (OR 1.92, 95% CI 1.50-2.50) and gender (OR 2.53, 95% CI 1.83-3.50) were more likely to be funded. Qualitative observations revealed persistent conflation of the terms sex and gender. Eighty-six percent of evaluators appreciated the tailored discipline-specific coaching. CONCLUSIONS: A number of policy interventions improved sex and gender uptake in grant applications, with higher success rates observed over time for applications that integrated sex and gender. Other funders' action plans around sex and gender integration may be informed from our experiences of the timing, type and targets of the different interventions, specifically those directed at evaluators.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.076 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".