Alcohol Policy Relevant Indicators and Alcohol Use Among Adolescents in Latin America and the Caribbean
Bibliographic record
Abstract
OBJECTIVE: This study assessed four alcohol policy indicators and their associations with adolescent alcohol use in Latin America and the Caribbean. METHOD: A secondary data analysis of nationally representative, cross-sectional data sets (years 2007-2013) from 26 Latin American and Caribbean countries was performed (N = 55,248 13- to 15-year-old students). Logistic regression models were used to analyze associations between alcohol policy relevant indicators and alcohol use, adjusting for the country and demographic variables. RESULTS: In all countries, at least 20% of the students were exposed to alcohol advertisements daily or almost daily, which was associated with a twofold increased risk of current alcohol use and at least monthly heavy drinking. Obtaining alcohol from a shop was associated with a nearly threefold increased risk of at least monthly heavy drinking compared with obtaining alcohol from home, which was the most common way to obtain alcohol. Being denied from purchasing retail alcohol was associated with a decreased risk of at least monthly heavy drinking. About 27% to 53% of the students who had tried to purchase alcohol had been denied. One in four students reported exposure to drink driving in the past 30 days. CONCLUSIONS: Deficits in alcohol policy indicators were identified in a number of countries. Improving implementation and enforcement of alcohol policies could reduce alcohol use and related burden among adolescents in a number of Latin American and Caribbean 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".