Raising the bar: Sex and Gender-Based Analysis and Reporting (SGBAR) in health research
Bibliographic record
Abstract
Abstract Issue For decades, research has been male dominated: research led by men with male participants. Two-thirds of heart disease and stroke clinical research is based on men. When research is led by women, sex and gender are more likely to be incorporated into the research itself, and the levels of sex reporting also increases. Unfortunately, the low involvement of women in research around the world - as both researchers and participants - has led to findings that are not always applicable to women, resulting in gaps in treatment, care and recovery. The results are worse health outcomes for women in most countries. Background Applying sex and gender methods and analysis in research leads to higher quality results. A review of the literature and research landscape showed that sex and gender analysis was more common in public health, but not in clinical, biomedical or health systems research. Results The Heart and Stroke Foundation (Heart & Stroke), as both a funder of research and advocate for systems change recognized the research system perpetuated the inequities in women's health. The solution was to restructure the organization's research funding enterprise and also push for change among the health community. H&S put SGBAR requirements into its research funding program. To build on the change, H&S is now working across all levels of government and research institutions to secure SGBAR as a standard of practice. To date, this advocacy pursuit has created substantial systems change. Lessons Due to the complex research landscape, making SGBAR a priority across all research institutions is a massive undertaking. There is a need for both top down and bottom up approaches to ensure wide scale change. Key messages Incorporating sex and gender-based analysis and reporting in health research will improve health equity. Health research Funding agencies have an opportunity to raise the bar and shift the research environment.
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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 | 0.656 | 0.744 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.014 | 0.020 |
| 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".