Beyond sex and gender difference in funding and reporting of health research
Bibliographic record
Abstract
BACKGROUND: Understanding sex and gender in health research can improve the quality of scholarship and enhance health outcomes. Funding agencies and academic journals are two key gatekeepers of knowledge production and dissemination, including whether and how sex/gender is incorporated into health research. Though attention has been paid to key issues and practices in accounting for sex/gender in health funding agencies and academic journals, to date, there has been no systematic analysis documenting whether and how agencies and journals require attention to sex/gender, what conceptual explanations and practical guidance are given for such inclusion, and whether existing practices reflect the reality that sex/gender cannot be separated from other axes of inequality. METHODS: Our research systematically examines official statements about sex/gender inclusion from 45 national-level funding agencies that fund health research across 36 countries (covering the regions of the EU and associated countries, North America, and Australia) and from ten top-ranking general health (the top five in "science" and the top five in "social science") and ten sex- and/or gender-related health journals. We explore the extent to which agencies and journals require inclusion of sex/gender considerations and to what extent existing strategies reflect state of the art understandings of sex/gender, including intersectional perspectives. RESULTS: The research highlights the following: (a) there is no consistency in whether sex/gender are mentioned in funding and publishing guidelines; (b) there is wide variation in how sex/gender are conceptualized and how researchers are asked to address the inclusion/exclusion of sex/gender in research; (c) funding agencies tend to prioritize male/female equality in research teams and funding outcomes over considerations of sex/gender in research content and knowledge production; and (d) with very few exceptions, agency and journal criteria fail to recognize the complexity of sex/gender, including the intersection of sex/gender with other key factors that shape health. CONCLUSIONS: The conceptualization and integration of sex/gender needs to better capture the interacting and complex factors that shape health-an imperative that can be informed by an intersectional approach. This can strengthen current efforts to advance scientific excellence in the production and reporting of research. We provide recommendations and supporting questions to strengthen consideration of sex/gender in policies and practices of health journals and funding agencies.
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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 | 0.295 | 0.577 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".