Preventive interventions targeting cannabis use and related harms in people with psychosis: A systematic review
Bibliographic record
Abstract
AIM: While most users will not experience severe adverse health outcomes from cannabis, it can be associated with negative outcomes in people with psychosis. People with psychosis who use cannabis have more severe psychiatric symptoms, higher rates of hospitalization, and diminished psychosocial functioning compared to those who do not use cannabis. Most studies of people with psychotic disorders have focused on cannabis use treatments and only a few on preventive interventions for cannabis. This systematic review aims to evaluate the effectiveness of preventive interventions focusing on cannabis use for people with psychosis. METHODS: We searched CINAHL Plus, EBM reviews, EMBASE, MEDLINE, PsycInfo and PubMed databases for controlled studies assessing the effects of preventive interventions on cannabis use and related harms in people with psychosis. We conducted the search using a combination of the following concepts: cannabis, psychosis, intervention and prevention. Risk of bias was assessed. RESULTS: The search yielded 11 460 unique studies. Of these, five studies met our eligibility criteria. None of the studies demonstrated clear efficacy of prevention interventions in reducing cannabis use, and none measured cannabis-related harms. All studies had high risk of bias. CONCLUSION: The small number of studies and the considerable risk of bias made it difficult to conclude whether any of the existing interventions were promising. With increased acceptance and accessibility of cannabis due to liberalizing cannabis policies, it is imperative to improve the evidence base for preventive interventions, in particular their effectiveness in decreasing the risk of cannabis-related harms in people with psychosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".