Interventions for Adolescent Substance Abuse: An Overview of Systematic Reviews
Bibliographic record
Abstract
Many unhealthy behaviors often begin during adolescence and represent major public health challenges. Substance abuse has a major impact on individuals, families, and communities, as its effects are cumulative, contributing to costly social, physical, and mental health problems. We conducted an overview of systematic reviews to evaluate the effectiveness of interventions to prevent substance abuse among adolescents. We report findings from a total of 46 systematic reviews focusing on interventions for smoking/tobacco use, alcohol use, drug use, and combined substance abuse. Our overview findings suggest that among smoking/tobacco interventions, school-based prevention programs and family-based intensive interventions typically addressing family functioning are effective in reducing smoking. Mass media campaigns are also effective given that these were of reasonable intensity over extensive periods of time. Among interventions for alcohol use, school-based alcohol prevention interventions have been associated with reduced frequency of drinking, while family-based interventions have a small but persistent effect on alcohol misuse among adolescents. For drug abuse, school-based interventions based on a combination of social competence and social influence approaches have shown protective effects against drugs and cannabis use. Among the interventions targeting combined substance abuse, school-based primary prevention programs are effective. Evidence from Internet-based interventions, policy initiatives, and incentives appears to be mixed and needs further research. Future research should focus on evaluating the effectiveness of specific interventions components with standardized intervention and outcome measures. Various delivery platforms, including digital platforms and policy initiative, have the potential to improve substance abuse outcomes among adolescents; however, these require further research.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".