The Harms That Drinkers Cause: Regional Variations Within Countries
Bibliographic record
Abstract
AIMS: Multinational studies of drinking and the harms it may cause typically treat countries as homogeneous. Neglecting variation within countries may lead to inaccurate conclusions about drinking behavior, and particularly about harms drinking causes for people other than the drinkers. This study is the first to examine whether drinkers' self-reported harms to others from drinking vary regionally within multiple countries. DESIGN SETTING AND PARTICIPANTS: Analyses draw on survey data from 12,356 drinkers in 46 regions (governmental subunits) within 10 countries, collected as part of the GENACIS project (Wilsnack et al., 2009). MEASURES: Drinkers reported on eight harms they may have caused others in the past 12 months because of their drinking. The likelihood of reporting one or more of these eight harms was evaluated by multilevel modeling (respondents nested within regions nested within countries), estimating random effects of country and region and fixed effects of gender, age, and regional prevalence of drinking. FINDINGS: Reports of causing one or more drinking-related harms to others differed significantly by gender and age (but not by regional prevalence of drinking), but also differed significantly by regions within countries. CONCLUSIONS: National and multinational evaluations of adverse effects of drinking on persons other than the drinkers should give more attention to how those effects may vary regionally within countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".