Cross-sectional surveys of financial harm associated with others’ drinking in 15 countries: Unequal effects on women?
Bibliographic record
Abstract
INTRODUCTION AND AIMS: That physical, emotional and social problems occur not only to drinkers, but also to others they connect with, is increasingly acknowledged. Financial harms from others' drinking have been seldom studied at the population level, particularly in low- and middle-income countries. Whether financial harm and costs from others' drinking inequitably affect women is little known. The study's aim is to compare estimates and correlates of alcohol's financial harm to others than the drinker in 15 countries. METHODS AND MATERIALS: Cross-sectional surveys of Alcohol's Harm To Others (AHTO) were conducted in Australia, Brazil, Chile, Denmark, India, Ireland, Lao PDR, New Zealand, Nigeria, Sri Lanka, Sweden, Switzerland, Thailand, the US and Vietnam. PARTICIPANTS: 17,670 men and 20,947 women. MEASUREMENT: The prevalence of financial harm in the last year was assessed as financial trouble and/or less money available for household expenses because of someone else's drinking. ANALYSIS: Meta-analysis and country-level logistic regression of financial harm (vs. none), adjusted for gender, age, education, rurality and participant drinking. RESULTS: Under 3.2 % of respondents in most high-income countries reported financial harm due to others' drinking, whereas 12-22 % did in Thailand, Sri Lanka and India. Financial harm from others' drinking was significantly more common among women than men in nine countries. Among men and women, financial harm was significantly more prevalent in low- and middle- than in high-income countries. CONCLUSIONS: Reports of financial harm from others' drinking are more common among women than among men, and in low- and middle-income than in high-income countries.
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".