Addressing gender inequalities in health: a comprehensive framework for policy and practice
Bibliographic record
Abstract
Background Despite evidence on the role of gender as a social determinant of health, there is a lack of frameworks that focus on the factors contributing to gender inequalities in health (GIeH) and their specific characteristics. We aim to present the GIeH framework, specifically designed to illustrate the processes, determinants, and consequences of GIeH that support improved measurement of gender equality in health and inform policymaking. Methods A ‘best-fit’ framework synthesis was utilized. We identified the existing frameworks to generate an a priori framework, followed by systematically identifying relevant studies and coding their results against it. We searched bibliographic databases including Web of Science, MEDLINE, PubMed, ScienceDirect, Scopus, and grey literature and relevant websites, using predefined inclusion and exclusion criteria. No restrictions were imposed on document type, setting, date, or language and we used the JBI critical appraisal checklist to assess the quality of included studies. Thematic analysis and team discussions were applied to finalize the sub-themes in our proposed conceptual framework. Results Based on the synthesis of identified evidence, we categorized eight themes and 48 sub-themes into determinants and consequences of GIeH. These determinants were classified into four layers: context, health system, community-household, and individual factors. We provided a schematic to illustrate the interaction process between these layers, contributing to gender inequalities in health outcomes such as health status, life expectancy, health- related quality of life, mortality, and morbidity. Conclusion This framework provides a broad perspective on gender inequalities in health. It is a practical tool for health policymakers and professionals, providing a systematic approach to measure, monitor, and address these inequalities. By highlighting the interactions of various factors from Micro to Macro level and their effects on health outcomes, the framework facilitates strategic policy interventions and empirical solutions to promoting health equity. Systematic review registration: PROSPERO; CRD42022366765.
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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 | 0.231 | 0.120 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.014 | 0.061 |
| Scholarly communication | 0.039 | 0.035 |
| Open science | 0.012 | 0.029 |
| Research integrity | 0.024 | 0.022 |
| 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; 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".