The Integration of Sex and Gender Considerations Into Biomedical Research: Lessons From International Funding Agencies
Bibliographic record
Abstract
To improve the outcomes of research and medicine, government-based international research funding agencies have implemented various types of policies and mechanisms with respect to sex as a biological variable and gender as a sociocultural factor. After the 1990s, the US National Institutes of Health (NIH), the Canadian Institutes of Health Research (CIHR), and the European Commission (EC) began requesting that applicants address sex and gender considerations in grant proposals, and offering resources to help the scientific community integrate sex and gender into biomedical research. Although it is too early to analyze data on the success of all of the policies and mechanisms implemented, here we review the use both of carrots (incentives) and sticks (requirements) developed to motivate researchers and the entire scientific research enterprise to consider sex and gender influences on health and in science. The NIH focused on sex as a biological variable (SABV) aligned with an initiative to enhance reproducibility through rigor and transparency; CIHR instituted a sex- and gender-based analysis (SGBA) policy; and the EC required the integration of the "gender dimension," which incorporates sex, gender, and intersectional analysis into research and innovation. Other global efforts are briefly summarized. Although we are still learning what works, we share lessons learned to improve the integration of sex and gender considerations into research. In conjunction with refining and expanding the policies of funding agencies and mechanisms, private funders/philanthropic groups, editors of peer-reviewed journals, academic institutions, professional organizations, ethics boards, health care systems, and industry also need to make concerted efforts to integrate sex and gender into research, and we all must bridge across silos to promote systemwide solutions throughout the biomedical enterprise. For example, policies that encourage researchers to disaggregate data by sex and gender, the development of tools to better measure gender effects, or policies similar to SABV and/or SGBA adopted by private funders would accelerate progress. Uptake, accountability for, and a critical appraisal of sex and gender throughout the biomedical enterprise will be crucial to achieving the goal of relevant, reproducible, replicable, and responsible science that will lead to better evidence-based, personalized care for all, but especially 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 | 0.235 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".