Social Media Use and Alcohol Sipping in Early Adolescents: A Prospective Cohort Study
Bibliographic record
Abstract
Background Social media can influence alcohol initiation behaviors such as sipping, which can lead to future adverse alcohol-related outcomes. Few studies have examined the role of problematic social media use, characterized by addiction, mood modification, tolerance, withdrawal, conflict, and relapse, especially in early adolescence.Objective To examine the prospective association between social media use and sipping alcohol in a nationwide sample of early adolescents, and the extent to which problematic social media use mediates the association.Methods We analyzed prospective data from the Adolescent Brain Cognitive Development Study (N = 7514; ages 9–10 years at baseline; 2016–2018) to estimate associations between social media time (Year 1) and alcohol sipping (Year 3) using modified Poisson regression, adjusting for confounders and testing problematic social media use (Year 2) as a mediator.Results Social media time (Year 1) was prospectively associated with 1.31 (95% confidence interval 1.20–1.43) times higher risk of new-onset sipping (Year 3). The association between social media time and new-onset alcohol sipping was partially mediated by problematic social media use at Year 2 (25.0% reduction in the association between the former two factors after adding problematic social media use, p = 0.002).Conclusions Time spent on social media was associated with a higher risk of alcohol sipping in a diverse national sample of early adolescents, and the association was partially mediated by problematic social media use. Media literacy education and family media use plans could advise early adolescents about exposure to alcohol content on social media and warning signs for problematic use.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".