The relative importance of perceived substance misuse use by different peers on smoking, alcohol and illicit drug use in adolescence
Bibliographic record
Abstract
BACKGROUND: Substance use by young people is strongly associated with that of their peers. Little is known about the influence of different types of peers. We tested the relationship between perceived substance use by five types of peers and adolescents' use of illicit drugs, smoking, and alcohol consumption. METHODS: We used data collected from 1285 students aged 12-13 as part of a pilot cluster randomized controlled trial (United Kingdom, 2014-2016). The exposures were the perceived use of illicit drugs, smoking and alcohol consumption by best friends, boy or girlfriends, brothers or sisters, friends outside of school and online. Outcomes were self-reported lifetime use of illicit drugs, smoking and alcohol consumption assessed 18-months later. RESULTS: The lifetime prevalence of illicit drug use, smoking and alcohol consumption at the 18-month follow-up were 14.3%, 24.9% and 54.1%, respectively. In the fully adjusted models, perceived substance use by friends outside of school, brothers or sisters, and online had the most consistent associations with outcomes. Perceived use by friends online was associated with an increased risk of ever having used illicit drugs (odds ratio [OR] = 2.43, 95% confidence interval [CI] = 1.26, 4.69), smoking (OR = 1.61, 95% CI 0.96, 2.70) and alcohol consumption (OR = 2.98, 95% CI = 1.71, 5.18). CONCLUSIONS: Perceived substance use by friends outside of school, brothers and sisters and online could be viable sources of peer influence. If these findings are replicated, a greater emphasis should be made in interventions to mitigate the influence of these peers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".