Policy‐relevant behaviours predict heavier drinking and mediate the relationship with age, gender and education status: Analysis from the International Alcohol Control Study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: To investigate behaviours related to four alcohol policy variables (policy-relevant behaviours) and demographic variables in relation to typical quantities of alcohol consumed on-premise in six International Alcohol Control study countries. DESIGN AND METHODS: General population surveys with drinkers using a comparable survey instrument and data analysed using path analysis in an overall model and for each country. MEASURES: typical quantities per occasion consumed on-premise; gender, age; years of education, prices paid, time of purchase, time to access alcohol and liking for alcohol advertisements. RESULTS: In the overall model younger people, males and those with fewer years of education consumed larger typical quantities. Overall lower prices paid, later time of purchase and liking for alcohol ads predicted consuming larger typical quantities; this was found in the high-income countries, less consistently in the high-middle-income countries and not in the low middle-income country. Three policy-relevant behaviours (prices paid, time of purchase, liking for alcohol ads) mediated the relationships between age, gender, education and consumption in high-income countries. DISCUSSION AND CONCLUSIONS: International Alcohol Control survey data showed a relationship between policy-relevant behaviours and typical quantities consumed and support the likely effect of policy change (trading hours, price and restrictions on marketing) on heavier drinking. The path analysis also revealed policy-relevant behaviours were significant mediating variables between the effect of age, gender and educational status on consumption. However, this relationship is clearest in high-income countries. Further research is required to understand better how circumstances in low-middle-income countries impact effects of policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".