Prevention of substance use in children/adolescents with mental disorders: a systematic review.
Bibliographic record
Abstract
OBJECTIVE: WE CONDUCTED A SYSTEMATIC REVIEW TO ANSWER THE QUESTION: Among youth ≤18 years of age with a mental disorder, does substance use prevention compared to no prevention result in reduced rates of substance use/abuse/disorder (SUD)? The review was requested by the Ontario Ministry of Health and Long-term Care through the Canadian Institutes for Health Research Evidence on Tap program. METHODS: A four-step search process was used: Search 1 and 2: Randomized controlled trials (RCTs) that evaluated a SUD prevention intervention in individuals with a mental disorder who were: 1) ≤18 years; or, 2) any age. Search 3: Observational studies of an intervention to prevent SUD in those with mental disorder. Search 4: RCTs that evaluated a SUD primary prevention skills-based intervention in high-risk youth ≤18 years. RESULTS: Searches 1 and 2: one RCT conducted in youth was found; Search 3: two observational studies were found. All three studies reported statistically significant reductions in substance use. Search 4: five RCTs were found with mixed results. Methodological weaknesses including inadequate study power may explain the results. CONCLUSIONS: Little is known about effective interventions to prevent SUD in youth with a mental disorder. Effective SUD primary prevention programs exist and should be evaluated in this high-risk group.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".