The social location of harm from others’ drinking in 10 societies
Bibliographic record
Abstract
AIMS: Survey data from 10 diverse countries were used to analyse the social location of harms from others' drinking: which segments of the population are more likely to be adversely affected by such harm, and how does this differ between societies? METHODS: General-population surveys in Australia, Chile, India, Laos, New Zealand, Nigeria, Sri Lanka, Thailand, United States and Vietnam, with a primary focus on the social location of the harmed person by gender, age groups, rural/urban residence and drinking status. Harms from known drinkers were analysed separately from harms from strangers. RESULTS: In all sites, risky or moderate drinkers were more likely than abstainers to report harm from the drinking of known drinkers, with risky drinkers the most likely to report harm. This was also generally true for harm from strangers' drinking, although the patterns were more mixed in Vietnam and Thailand. Harm from strangers' drinking was more often reported by males, while gender disparity in harm from known drinkers varied between sites. Younger adults were more likely to experience harm both from known drinkers and from strangers in some, but not all, societies. Only a few sites showed significant urban/rural differences, with disparities varying in direction. In multivariate analyses, most relationships remained, although some were no longer significant. CONCLUSION: The social location of harms from others' drinking, whether known or a stranger, varies considerably between societies. One near-commonality among the societies is that those who are themselves risky drinkers are more likely to suffer harm from others' drinking.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".