Better science with sex and gender: Facilitating the use of a sex and gender-based analysis in health research
Bibliographic record
Abstract
Much work has been done to promote sex and gender-based analyses in health research and to think critically about the influence of sex and gender on health behaviours and outcomes. However, despite this increased attention on sex and gender, there remain obstacles to effectively applying and measuring these concepts in health research. Some health researchers continue to ignore the concepts of sex and gender or incorrectly conflate their meanings. We report on a primer that was developed by the authors to help researchers understand and use the concepts of sex and gender in their work. We provide detailed definitions of sex and gender, discuss a sex and gender-based analysis (SGBA), and suggest three approaches for incorporating sex and gender in health research at various stages of the research process. We discuss our knowledge translation process and share some of the challenges we faced in disseminating our primer with key stakeholders. In conclusion, we stress the need for continued attention to sex and gender in health research.
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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 | 0.329 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.018 | 0.062 |
| Scholarly communication | 0.024 | 0.042 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".