Sex, Gender and Health: Mapping the Landscape of Research and Policy
Bibliographic record
Abstract
Including sex and gender considerations in health research is considered essential by many funders and is very useful for policy makers, program developers, clinicians, consumers and other end users. While longstanding confusions and conflations of terminology in the sex and gender field are well documented, newer conceptual confusions and conflations continue to emerge. Contemporary social demands for improved health and equity, as well as increased interest in precision healthcare and medicine, have made obvious the need for sex and gender science, sex and gender-based analyses (SGBA+), considerations of intersectionality, and equity, diversity and inclusion initiatives (EDI) to broaden representation among participants and diversify research agendas. But without a shared and precise understanding of these conceptual areas, fields of study, and approaches and their inter-relationships, more conflation and confusion can occur. This article sets out these areas and argues for more precise operationalization of sex- and gender-related factors in health research and policy initiatives in order to advance these varied agendas in mutually supportive ways.
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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 | 0.058 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.011 | 0.055 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".