Adolescent Mental Health Program Components and Behavior Risk Reduction: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Although adolescent mental health interventions are widely implemented, little consensus exists about elements comprising successful models. OBJECTIVE: We aimed to identify effective program components of interventions to promote mental health and prevent mental disorders and risk behaviors during adolescence and to match these components across these key health outcomes to inform future multicomponent intervention development. DATA SOURCES: A total of 14 600 records were identified, and 158 studies were included. STUDY SELECTION: Studies included universally delivered psychosocial interventions administered to adolescents ages 10 to 19. We included studies published between 2000 and 2018, using PubMed, Medline, PsycINFO, Scopus, Embase, and Applied Social Sciences Index Abstracts databases. We included randomized controlled, cluster randomized controlled, factorial, and crossover trials. Outcomes included positive mental health, depressive and anxious symptomatology, violence perpetration and bullying, and alcohol and other substance use. DATA EXTRACTION: Data were extracted by 3 researchers who identified core components and relevant outcomes. Interventions were separated by modality; data were analyzed by using a robust variance estimation meta-analysis model, and we estimated a series of single-predictor meta-regression models using random effects. RESULTS: Universally delivered interventions can improve adolescent mental health and reduce risk behavior. Of 7 components with consistent signals of effectiveness, 3 had significant effects over multiple outcomes (interpersonal skills, emotional regulation, and alcohol and drug education). LIMITATIONS: Most included studies were from high-income settings, limiting the applicability of these findings to low- and middle-income countries. Our sample included only trials. CONCLUSIONS: Three program components emerged as consistently effective across different outcomes, providing a basis for developing future multioutcome intervention programs.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".