Does drinking modify the relationship between men's gender‐inequitable attitudes and their perpetration of intimate partner violence? A meta‐analysis of surveys of men from seven countries in the Asia Pacific region
Bibliographic record
Abstract
BACKGROUND AND AIMS: Although men's alcohol misuse and less gender-equitable attitudes have been identified as risks for perpetration of intimate partner violence (IPV), less is known about how men's gender-equitable attitudes and drinking act together to increase risk of IPV. This study aimed to assess the independent relationships of lower gender-equitable attitudes and drinking to perpetration of IPV and their interaction among men in seven countries. DESIGN: Secondary analysis of the United Nations Multi-Country Study on Men and Violence (UNMCS) and Nabilan Study databases consisting of (1) unadjusted and adjusted logistic regression to measure the association of perpetration of IPV with gender-equitable men (GEM) scale score and regular heavy episodic drinking (RHED) and (2) meta-analyses of prevalence and effect estimates adjusted for country-level sites and countries. SETTING AND PARTICIPANTS: A total of 9148 ever-partnered 18-49-year-old men surveyed in 2011-15 from 18 sites in Bangladesh, Cambodia, China, Indonesia, Papua New Guinea, Sri Lanka and Timor Leste. MEASUREMENTS: The outcome variable is reported perpetration of physical or sexual IPV in the previous year. INDEPENDENT VARIABLES: GEM scale scores; RHED, defined as six or more drinks in one session at least monthly (compared with other drinkers and abstainers). FINDINGS: Pooled past-year prevalence of perpetration of IPV was 13% [95% confidence interval (CI) = 9-16%]. GEM scores and RHED were independently associated with perpetration of IPV overall and in most sites. Pooled odds ratios (ORs) for perpetration of IPV with less equitable GEM scores were 1.07 (95% CI = 1.04, 1.09) and with RHED were 3.42 (95% CI = 2.43, 4.81). A significant interaction between GEM score and RHED (P = 0.001) indicated that RHED increased the relationship of less gender-equitable attitudes and perpetration of IPV. CONCLUSION: Both gender-inequitable attitudes and drinking appear to be associated with perpetration of intimate partner violence by men, with regular heavy episodic drinking increasing the likelihood of intimate partner violence among men with less equitable gender attitudes.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.026 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".