Gender relations and health research: a review of current practices
Bibliographic record
Abstract
INTRODUCTION: The importance of gender in understanding health practices and illness experiences is increasingly recognized, and key to this work is a better understanding of the application of gender relations. The influence of masculinities and femininities, and the interplay within and between them manifests within relations and interactions among couples, family members and peers to influence health behaviours and outcomes. METHODS: To explore how conceptualizations of gender relations have been integrated in health research a scoping review of the existing literature was conducted. The key terms gender relations, gender interactions, relations gender, partner communication, femininities and masculinities were used to search online databases. RESULTS: Through analysis of this literature we identified two main ways gender relations were integrated in health research: a) as emergent findings; and b) as a basis for research design. In the latter, gender relations are included in conceptual frameworks, guide data collection and are used to direct data analysis. CONCLUSIONS: Current uses of gender relations are typically positioned within intimate heterosexual couples whereby single narratives (i.e., either men or women) are used to explore the influence and/or impact of intimate partner gender relations on health and illness issues. Recommendations for advancing gender relations and health research are discussed. This research has the potential to reduce gender inequities in health.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".