Recommendations for Reducing the Risk of Cannabis Use-Related Adverse Psychosis Outcomes: A Public Mental Health-Oriented Evidence Review
Bibliographic record
Abstract
Objective: Cannabis use is increasingly normalized; psychosis is a major adverse health outcome. We reviewed evidence on cannabis use-related risk factors for psychosis outcomes at different stages toward recommendations for risk reduction by individuals involved in cannabis use. Methods: We searched primary databases for pertinent literature/data 2016 onward, principally relying on reviews and high-quality studies which were narratively summarized and quality-graded; recommendations were developed by international expert consensus. Results: Genetic risks, and mental health/substance use problem histories elevate the risks for cannabis-related psychosis. Early age-of-use-onset, frequency-of-use, product composition (i.e., THC potency), use mode and other substance co-use all influence psychosis risks; the protective effects of CBD are uncertain. Continuous cannabis use may adversely affect psychosis-related treatment and medication effects. Risk factor combinations further amplify the odds of adverse psychosis outcomes. Conclusions: Reductions in the identified cannabis-related risks factors—short of abstinence—may decrease risks of related adverse psychosis outcomes, and thereby protect cannabis users’ health.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.001 |
| 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".