Is There a Dose–Response Relationship Between Cannabis Use and Violence? A Longitudinal Study in Individuals with Severe Mental Disorders
Bibliographic record
Abstract
Introduction: Recent longitudinal studies point toward the existence of a positive relationship between cannabis use and violence in people with severe mental disorders (SMD). However, the existence of a dose–response relationship between the frequency/severity of cannabis use and violence has seldom been investigated. Therefore, this study aims to determine if such a relationship exists in a psychiatric population. Methods: To do so, a total of 98 outpatients (81 males and 17 females, all over 18 years of age) with SMD were recruited at the Institut universitaire de santé mentale de Montréal (Montréal, Canada) and included in the analyses. Clinical evaluations were conducted every 3 months for a year. Substance use, violent behaviors, and potential covariables were assessed through self-reported assessments, urinary testing, as well as clinical, criminal, and police records. Using generalized estimating equations, the association between cannabis use frequency (nonusers, occasional, regular, and frequent users) and violence was investigated, as well as the association between the severity of cannabis use and violent behaviors. Results: It was found that cannabis use frequency and severity were significant predictors of violent behaviors. After adjustment for time, age, sex, ethnicity, diagnoses, impulsivity, and use of alcohol and stimulants, odds ratios were of 1.91 ( p <0.001) between each frequency profile and 1.040 ( p <0.001) for each increase of one point of the severity of cannabis use score (ranging from 0 to 79). Conclusions: Despite the high attrition rate, these findings may have important implications for clinicians as cannabis use may have serious consequences in psychiatric populations. Nevertheless, the mechanisms underlying this association remain unclear.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".