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 towards the existence of a positive relationship between cannabis use and violence in people with severe mental disorders. However, the existence of a dose-response relationship between the frequency and/or the severity of cannabis use and violence has seldom been investigated. Objectives This study aimed to determine if a dose-response relationship between cannabis use and violence exists in a psychiatric population. Methods This observational study was conducted at the Institut universitaire de santé mentale de Montréal (Montréal, Canada). A total of 98 outpatients (81 males and 17 females, all over 18 years of age) with severe mental disorders were 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 (non-users, occasional, regular, and frequent users, assessed using the Time-Line Follow-Back and confirmed with urinary testing) and violence was investigated, as well as the association between the severity of cannabis use (measured using the Cannabis Use Problems Identification Test – CUPIT) and violent behaviors. Results Cannabis use frequency and severity were significant predictors of violent behaviors. After adjustment for time, age, sex, ethnicity, psychiatric 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 (0 to 79). Image: Conclusions These findings have important implications for clinicians, demonstrating that cannabis use may have serious adverse consequences in a psychiatric population. Nevertheless, the mechanisms underlying this association remain unclear. Disclosure of Interest None Declared
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".