Examining the Association and Directionality between Mental Health Disorders and Substance Use among Adolescents and Young Adults in the U.S. and Canada—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The purpose of this systematic review was to examine the association and directionality between mental health disorders and substance use among adolescents and young adults in the U.S. and Canada. METHODS: The following databases were used: Medline, PubMed, Embase, PsycINFO, and Cochrane Library. Meta-analysis used odds ratios as the pooled measure of effect. RESULTS: A total of 3656 studies were screened and 36 were selected. Pooled results showed a positive association between depression and use of alcohol (odds ratio (OR) = 1.50, 95% confidence interval (CI): 1.24⁻1.83), cannabis (OR = 1.29, 95% CI: 1.10⁻1.51), and tobacco (OR = 1.65, 95% CI: 1.43⁻1.92). Significant associations were also found between anxiety and use of alcohol (OR = 1.54, 95% CI: 1.19⁻2.00), cannabis (OR = 1.36, 95% CI: 1.02⁻1.81), and tobacco (OR = 2.21, 95% CI: 1.54⁻3.17). A bidirectional relationship was observed with tobacco use at baseline leading to depression at follow-up (OR = 1.87, CI = 1.23⁻2.85) and depression at baseline leading to tobacco use at follow-up (OR = 1.22, CI = 1.09⁻1.37). A unidirectional relationship was also observed with cannabis use leading to depression (OR = 1.33, CI = 1.19⁻1.49). CONCLUSION: This study offers insights into the association and directionality between mental health disorders and substance use among adolescents and young adults. Our findings can help guide key stakeholders in making recommendations for interventions, policy and programming.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".