Sex and gender in health research: Intersectionality matters
Bibliographic record
Abstract
Research policies aiming to integrate sex and gender in scientific studies are receiving increased attention in academia. Incorporating these policies into health research is essential for improving targeted and equitable healthcare outcomes, by considering both disparities and similarities between individuals relating to sex and gender. Although these efforts are both urgent and critical, only an intersectional approach, which considers broad and multidimensional aspects of an individual's identity, can provide a complete understanding of the factors that impact health. In this commentary, we emphasize that it is crucial to examine how sex and gender intersect with factors such as culture, ethnicity, minority status, and socioeconomic conditions to influence health outcomes. To approach health equity, we must consider disparities linked to both biological and environmental factors, in order to facilitate evidence-based health interventions with tangible impact.
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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 | 0.093 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.012 | 0.016 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".