Do school-based prevention programs impact co-occurring alcohol use and psychological distress during adolescence?
Bibliographic record
Abstract
BACKGROUND: Adolescence is a critical period for preventing substance use and mental health concerns, often targeted through separate school-based programs. However, co-occurrence is common and is related to worse outcomes. This study explores prevention effects of leading school-based prevention programs on co-occurring alcohol use and psychological distress. METHODS: Data from two Australian cluster randomized trials involving 8576 students in 97 schools were harmonized for analysis. Students received either health education (control) or one of five prevention programs (e.g. Climate Schools, PreVenture) with assessments at baseline and 6, 12, 24, and 30 or 36 months (from ages ~13-16). Multilevel multinomial regressions were used to predict the relative risk ratios (RRs) of students reporting co-occurring early alcohol use and psychological distress, alcohol use only, distress only, or neither (reference) across programs. RESULTS: = 0.22). Other evaluated programs (excluding Climate Schools: Mental Health) only appeared effective for reducing the risk of alcohol use that occurred without distress. CONCLUSIONS: Evidence-based programs exist that reduce the risk of early alcohol use with and without co-occurring psychological distress, though preventing psychological distress alone requires further exploration. Prevention programs appear to have different effects depending on whether alcohol use and distress present on their own or together, thus suggesting the need for tailored prevention strategies.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".