Online interventions for cannabis use among adolescents and young adults: Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Young people present high rates of cannabis use, abuse, and dependence. The United Nations estimates that roughly 3.8% of the global population aged 15-64 years used cannabis at least once in 2017. Cannabis use in young people may impair cognitive skills, interfere with learning, impact relationships, and lead to long term behavioural and psychological consequences. Online cannabis interventions (OCI) are increasingly popular, but their dissemination is not often supported by empirical evidence. AIM: To systematically compile and analyse the effectiveness of OCI for the reduction of cannabis use among adolescents and young adults (AYA). METHODS: Pooled effect sizes of cannabis use between treatment and control groups were estimated. For each comparison, Hedge's g was calculated using a random effects model. RESULTS: The search strategy yielded 4531 articles. Of those, a total of 411 articles were retrieved for detailed evaluation resulting in 17 eligible studies (n = 3525). Analyses revealed that online interventions did not significantly reduce cannabis consumption (Hedge's g = -0.061, 95% CI [-0.363] to [-0.242], p = .695) and high heterogeneity was noted (Q = 191.290). More recent studies using structured interventions, daily feedback, AYA centred designs, and peer support, specifically targeting CU seemed to have positive effects to address CU in this population. CONCLUSIONS: The lack of positive outcomes suggests that more specific and targeted interventions may be necessary to promote cannabis-related behavioural change among young people. These targeted interventions may include structured CU modules, daily feedback, peer support for increased adherence, user-centred design procedures, and input from key stakeholders such as families and service providers.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".