Harm from Known Others' Drinking by Relationship Proximity to the Harmful Drinker and Gender: A Meta‐Analysis Across 10 Countries
Bibliographic record
Abstract
BACKGROUND: Drinking is a common activity with friends or at home but is associated with harms within both close and extended relationships. This study investigates associations between having a close proximity relationship with a harmful drinker and likelihood of experiencing harms from known others' drinking for men and women in 10 countries. METHODS: Data about alcohol's harms to others from national/regional surveys from 10 countries were used. Gender-stratified random-effects meta-analysis compared the likelihood of experiencing each, and at least 1, of 7 types of alcohol-related harm in the last 12 months, between those who identified someone in close proximity to them (a partner, family member, or household member) and those who identified someone from an extended relationship as the most harmful drinker (MHD) in their life in the last 12 months. RESULTS: Women were most likely to report a close male MHD, while men were most likely to report an extended male MHD. Relatedly, women with a close MHD were more likely than women with an extended MHD to report each type of harm, and 1 or more harms, from others' drinking. For men, having a close MHD was associated with increased odds of reporting some but not all types of harm from others' drinking and was not associated with increased odds of experiencing 1 or more harms. CONCLUSIONS: The experience of harm attributable to the drinking of others differs by gender. For preventing harm to women, the primary focus should be on heavy or harmful drinkers in close proximity relationships; for preventing harm to men, a broader approach is needed. This and further work investigating the dynamics among gender, victim-perpetrator relationships, alcohol, and harm to others will help to develop interventions to reduce alcohol-related harm to others which are specific to the contexts within which harms occur.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.050 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".