Underage drinking in Brazil: findings from a community household survey
Bibliographic record
Abstract
OBJECTIVES: Previous studies have estimated the 30-day prevalence of alcohol use to be approximately 21% among youth in Brazil, despite the legal drinking age of 18 years. The present study aimed to determine the prevalence of underage drinking and its associated factors among adolescents in Brazil. METHODS: The 3rd National Survey on Drug Use by the Brazilian Population (III Levantamento Nacional sobre o Uso de Drogas pela População Brasileira) is a nationwide, multi-stage, probability-sample household survey. Herein, youth between the ages of 12-17 years were included. Lifetime and 12-month alcohol use prevalence were estimated. Factors associated with 12-month alcohol use were evaluated through multivariate analysis considering survey weights and design. RESULTS: Overall, 628 youth were interviewed. Estimated lifetime and 12-month alcohol use were 34.3% (standard error [SE] = 1.9) and 22.2% (SE = 1.7), respectively. Factors associated with 12-month drinking were: other/no religion vs. Christianity; living in rural vs. urban areas; self-reported diagnosis of depression vs. no self-reported depression; lifetime tobacco use vs. no history of tobacco use; and any illicit drug use vs. no history of illicit drug use. CONCLUSION: Considering that alcohol use is a major risk factor for early death among Brazilian youth, our findings highlight the importance of preventative measures to reduce underage drinking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".