The integration of sex and gender considerations in health policymaking: a scoping review
Bibliographic record
Abstract
While the terms 'sex' and 'gender' represent distinct concepts, their influence may intersect as important determinants of health. Despite their influence in shaping individual health outcomes, there is often inaccuracy and inconsistency in the degree to which sex and gender considerations are integrated in the health policymaking process. This primary aim of this paper is to fill the gap in the current understanding of how sex and gender considerations are integrated in this process. A scoping review methodology was used with the objective of assessing the extent to which sex and gender were considered inclusively and comprehensively in established examples of health policy planning and development. One hundred seventy-five documents from the academic and grey literature were found to meet the inclusion criteria for this scoping review. The authors charted the data from these publications, assessing the ways in which sex and gender were incorporated in their policy development process. Five key findings were ascertained from this review: (1) the terms sex and gender are often used interchangeably; (2) the terms sex and gender are often used with a limited and binary scope; (3) the most inclusive and comprehensive documents included transgender and gender diverse populations; (4) there are significant variations in the degree of inclusivity and comprehensivity of these documents based on geographic distribution; and (5) documents published within the last 5 years were more inclusive than older documents. This paper concludes with an acknowledgment of the limitations of the study design, a summary of the findings, future research directions, and implications for policymakers.
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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 | 0.076 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".