Brief Interventions for Cannabis Use in Healthcare Settings: Systematic Review and Meta-analyses of Randomized Trials
Bibliographic record
Abstract
OBJECTIVES: The efficacy of brief interventions for cannabis use was assessed in a systematic review and meta-analyses. METHODS: Systematic searches in academic databases were conducted, and reference lists of included studies were reviewed. Randomized trials were included that compared brief interventions with minimal control interventions for improving cannabis-specific outcomes among participants recruited from healthcare settings. Mean differences (MDs) based on change-from-baseline measurements were pooled using random-effects meta-analyses, with stratification by short term (≤3 months) and long term (>3 months). RESULTS: Ten reports from 9 studies were included. Most studies were conducted in the United States, including participants who were adults and were recruited from primary care or emergency departments. There were no significant effects of brief interventions on cannabis-specific Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) scores in the short term (MD -1.27 points; 95% confidence interval [CI] -3.75, 1.21; I 84.40%). The null pattern of findings was also observed for number of days of cannabis use in the past 30 days in the short term (MD -0.22 days; 95% CI -2.27, 1.82; I 60.30%) and long term (MD -0.28 days; 95% CI -2.42, 1.86; I 60.50%). The evidence base for other outcomes not subjected to meta-analyses was limited and mixed. CONCLUSIONS: Brief interventions did not result in reductions in cannabis-specific ASSIST scores or number of days of cannabis use, whereas the evidence base for other outcomes was limited and mixed. As such, brief interventions in healthcare settings may not be efficacious for cannabis use.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".