Partner alcohol consumption and intimate partner violence among women in Papua New Guinea: a cross-sectional analysis of Demographic and Health Survey
Bibliographic record
Abstract
OBJECTIVE: We examined the association between partner alcohol consumption and the experience of intimate partner violence among women in Papua New Guinea. DESIGN: We performed a cross-sectional analyses of data extracted from the 2016-2018 Papua New Guinea Demographic and Health Survey. We included 3319 women in sexual unions. Multilevel binary logistic regression analysis was used to examine the association between partner alcohol consumption and intimate partner violence, controlling for the covariates. Results from the regression analysis were presented using the crude odds ratios (cORs) and adjusted odds ratios (aORs), with 95% confidence intervals (CIs). SETTING: Papua New Guinea. PARTICIPANTS: Women aged 15-49 years in sexual unions. OUTCOME MEASURES: Physical, emotional, and sexual violence. RESULTS: The prevalence of physical, emotional and sexual violence among women in sexual unions in Papua New Guinea were 45.9% (42.4 to 47.7), 45.1% (43.4 to 46.8) and 24.3% (22.9 to 25.8), respectively. The level of partner alcohol consumption was 57.3%. Women whose partners consumed alcohol were more likely to experience physical violence (aOR=2.86, 95% CI=2.43 to 3.37), emotional violence (aOR=2.89, 95% CI=2.44 to 3.43) and sexual violence (aOR=2.56, 95% CI=2.08 to 3.16) compared with those whose partners did not consume alcohol. CONCLUSION: This study found a relatively high prevalence of intimate partner violence among women in Papua New Guinea. Most importantly, this study found partner alcohol consumption to be significantly and positively associated with intimate partner violence. The study, therefore, recommends that interventions seeking to reduce intimate partner violence among women in Papua New Guinea should intensify behaviour change and education on reducing or eliminating partner alcohol consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".