Doing better: eleven ways to improve the integration of sex and gender in health research proposals
Bibliographic record
Abstract
BACKGROUND: Integrating a sex and gender lens is increasingly recognized as important in health research studies. Past failures to adequately consider sex in drug development, for example, led to medications that were metabolized differently, proved harmful, or ineffective, for females. Including both males and females in study populations is important but not sufficient; health, access to healthcare, and treatment provided are also influenced by gender, the socially mediated roles, responsibilities, and behaviors of boys, girls, women and men. Despite understanding the relevance of sex and gender to health research, integrating this lens into study designs can still be challenging. Identified here, are nine opportunities to address sex and gender and thereby strengthen research proposals. METHODS: Ontario investigators were invited to submit a draft of their health research proposal to the Sex and Gender Research Support Service (SGRSS) at Women's College Hospital in Toronto, Ontario. The service works to build capacity on the integration of sex, gender, and other identity factors, in health research. Using the SAGER Guidelines and the METRICS for the Study of Sex and Gender in Human Participants as guides, proposals were reviewed to enhance their sex and gender considerations. Content analysis of the feedback provided these investigators was subsequently completed. RESULTS: Nearly 100 hundred study proposals were reviewed and investigators provided with suggestions on how to enhance their proposal. Analyzing the feedback provided across the reviewed studies revealed commonly overlooked opportunities to elevate consideration of sex and gender. These were organized into nine suggestions to mirror the sections of a research proposal. CONCLUSION: Health researchers are often challenged on how to integrate a sex and gender lens into their work. Reviews completed across a range of health research studies show there are several commonly overlooked opportunities to do better in this regard. Nine ways to improve the integration of a sex and gender lens in health research proposals have been identified.
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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: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.582 | 0.678 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.025 | 0.043 |
| Scholarly communication | 0.044 | 0.055 |
| Open science | 0.012 | 0.046 |
| Research integrity | 0.026 | 0.035 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".