Alcohol interventions, alcohol policy and intimate partner violence: a systematic review
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a significant global public health issue. The consistent evidence that alcohol use by one or both partners contributes to the risk and severity of IPV suggests that interventions that reduce alcohol consumption may also reduce IPV. This study sought to review the evidence for effects on IPV of alcohol interventions at the population, community, relationship and individual levels using the World Health Organization ecological framework for violence. METHODS: Eleven databases including Medline, PsycINFO, CINAHL and EMBASE were searched for English-language studies and grey literature published 1 January 1992 - 1 March 2013 investigating whether alcohol interventions/policies were associated with IPV reduction within adult (≥ 18) intimate relationships. Eleven studies meeting design criteria for attributing effects to the intervention and ten studies showing mediation of alcohol consumption were included in the review. The heterogeneity of study designs precluded quantitative meta analysis; therefore, a critical narrative approach was used. RESULTS: Population-level pricing and taxation studies found weak or no evidence for alcohol price changes influencing IPV. Studies of community-level policies or interventions (e.g., hours of sale, alcohol outlet density) showed weak evidence of an association with IPV. Couples-based and individual alcohol treatment studies found a relationship between reductions in alcohol consumption and reductions in IPV but their designs precluded attributing changes to treatment. Randomized controlled trials of combined alcohol and violence treatment programs found some positive effects of brief alcohol intervention as an adjunct to batterer treatment for hazardous drinking IPV perpetrators, and of brief interventions with non-dependent younger populations, but effects were often not sustained. CONCLUSIONS: Despite evidence associating problematic alcohol use with IPV, the potential for alcohol interventions to reduce IPV has not been adequately tested, possibly because studies have not focused on those most at risk of alcohol-related IPV. Research using rigorous designs should target young adult populations among whom IPV and drinking is highly prevalent. Combining alcohol and IPV intervention/policy approaches at the population, community, relationship and individual-level may provide the best opportunity for effective intervention.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".