Women's attitudes towards intimate partner violence in Guyana: a population-based study
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) against women is a global health issue and a breach of human rights. However, the literature lacks understanding of how socioeconomic and geographic disparities influence women's attitudes toward IPV in Guyana over time. This study aimed to assess trends in women's attitudes about IPV in Guyana. Methods: Data from three nationally representative surveys from 2009, 2014 to 2019 were analysed. The prevalence of women's attitudes about IPV was assessed, specifically in response to going out without telling their partners, neglecting their children, arguing with their partner, refusing sex with their partner, or burning food prepared for family meals. A series of stratified subgroup analyses were also completed. We assessed trends in IPV using the slope index of inequality (SII) and the concentration index of inequality (CIX). We used multilevel mixed-effects logistic regression to assess factors associated with women's attitudes justifying IPV. Findings: The prevalence of women's attitudes justifying IPV for any of the five reasons declined from 16.4% (95% CI: 15.1-17.8) in 2009 to 10.8% (95% CI: 9.7-12.0) in 2019. Marked geographic and socioeconomic inequalities were observed among subgroups. The SII for any of the five reasons decreased from -20.02 to -14.28, while the CIX remained constant over time. Key factors associated with women's attitudes about IPV were area of residence, sex of the household head, marital status, respondent's level of education, wealth index quintile, and the frequency of reading newspapers/magazines. Interpretation: From 2009 to 2019, Guyana was able to reduce women's attitudes justifying IPV against women by 34.1% and shortened subgroup inequalities. However, the prevalence remained high in 2019, with persisted inequalities among subgroups. Effective strategies, including the use of media to raise awareness, promotion of community-based approaches, and educational campaigns focusing on geographic and socioeconomic disparities, are essential for continuing to reduce the prevalence of IPV and associated inequalities. Funding: The study was funded in part by the National Institutes of Health, Fogarty International Center grant number D43TW012189.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".