Cross-Sectional Associations of Screen Time Activities With Alcohol and Tobacco Consumption Among Brazilian Adolescents
Bibliographic record
Abstract
Objectives: Little is known about the association between specific types of screen time and adolescents’ substance use. Thus, this study aimed to investigate the associations between screen time for studying, working, watching movies, playing games, and using social media and frequency of alcohol and tobacco use. Methods: In this cross-sectional study, Brazilian adolescents answered survey questions related to frequency of tobacco and alcohol consumption, and reported their daily volume of five types of screen time. Multilevel ordered logistic regression models were performed. Results: Each 1-hour increase in ST for studying was associated with 26% lower odds of smoking (OR = 0.74; 95% CI: 0.61–0.90) and 17% lower odds of drinking alcohol (OR = 0.83; 95% CI: 0.76–0.91) in the past 30 days. The increase of 1 hour of social media use was associated with 10% greater odds of smoking (OR = 1.10; 95% CI: 1.02–1.18) and a 13% greater chance of consuming alcohol (OR = 1.13; 95% CI: 1.08–1.18) in the past 30 days. Conclusion: The association between screen time and substance use appears to be type-specific. Future longitudinal research is needed to explore causal relationships.
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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 | 0.001 |
| 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".