Second‐hand drinking may increase support for alcohol policies: New results from the 2010 <scp>N</scp>ational <scp>A</scp>lcohol <scp>S</scp>urvey
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The harms of second-hand smoke motivated tobacco control legislation. Documenting the effects of harms from others' drinking might increase popular and political will for enacting alcohol policies. We investigated the individual-level relationship between having experienced such harms and favouring alcohol policy measures, adjusting for other influences. DESIGN AND METHODS: We used the landline sample (n = 6957) of the 2010 National Alcohol Survey, a computer-assisted telephone interview survey based on a random household sample in the USA. Multivariable regression models adjusted for personal characteristics, including drinking pattern (volume and heavy drinking), were used to investigate the ability of six harms from others' drinking to predict a three-item measure of favour for stronger alcohol policies. RESULTS: Adjusting for demographics and drinking pattern, number of harms from others' drinking predicted support for alcohol policies (P < 0.001). In a similar model, family- and aggression-related harms, riding with a drink driver and being concerned about another's drinking all significantly influenced favour for stronger alcohol policy. DISCUSSION: Although cross-sectional data cannot prove a causal influence or directionality, the association found is consistent with the hypothesis that experiencing harms from others' drinking (experienced by a majority) makes one more likely to favour alcohol policies. Other things equal, women, racial/ethnic minorities, lower-income individuals and lighter drinkers tend to be more supportive of alcohol controls and policies. CONCLUSIONS: Studies that estimate the impact of harms from other drinkers on those victimised are important and now beginning. Next we need to learn how such information could affect decision makers and legislators.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".