Does cannabis use predict aggressive or violent behavior in psychiatric populations? A systematic review
Bibliographic record
Abstract
Background: Despite an increase in information evaluating the therapeutic and adverse effects of cannabinoids, many potentially important clinical correlates, including violence or aggression, have not been adequately investigated.Objectives: In this systematic review, we examine the published evidence for the relationship between cannabis and aggression or violence in individuals with psychiatric disorders.Methods: Following PRISMA guidelines, articles in English were searched on PubMed, Google Scholar, MEDLINE, and PsycINFO from database inception to January 2022. Data for aggression and violence in people with psychiatric diagnoses were identified during the searches.Results: Of 391 papers identified within the initial search, 15 studies met inclusion criteria. Cross-sectional associations between cannabis use and aggression or violence in samples with post-traumatic stress disorder (PTSD) were found. Moreover, a longitudinal association between cannabis use and violence and aggression was observed in psychotic-spectrum disorders. However, the presence of uncontrolled confounding factors in the majority of included studies precludes any causal conclusions.Conclusion: Although cannabis use is associated with aggression or violence in individuals with PTSD or psychotic-spectrum disorders, causal conclusions cannot be drawn due to methodological limitations observed in the current literature. Well-controlled, longitudinal studies are needed to ascertain whether cannabis plays a causal role on subsequent violence or aggression in mental health disorders.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".