Associations Between Cannabis Use and Mental Health in Patients Accessing Treatment for Substance Use Disorders: An Exploratory Cross-Sectional Study
Bibliographic record
Abstract
Background: Cannabis use is common among individuals with substance use disorders (SUDs), yet its relationship with mental health characteristics in treatment-seeking populations remains unclear. Objectives: This study examined associations between cannabis use and mental health in patients seeking SUD treatment, to understand whether cannabis use relates to clinical characteristics relevant to SUD care. Methods: A cross-sectional online survey was completed by 544 patients in Ontario, Canada seeking treatment for any SUD (including cannabis use disorder). Participants were grouped by cannabis use: any past-year use (current use; n = 363), lifetime use but no past-year use (past use; n = 109), and no lifetime use (never use; n = 72). Anxiety, depression, sleep quality, and disability were assessed with the Generalized Anxiety Disorder scale (GAD-7), Patient Health Questionnaire (PHQ-9), Pittsburgh Sleep Quality Index (PSQI), and World Health Organization Disability Assessment Schedule (WHODAS). Psychiatric diagnoses, trauma exposures, and suicidality were also assessed. Results: Cannabis use group was significantly associated with trauma history and several psychiatric diagnoses (e.g., anxiety, depression), with the highest prevalence in the current use group (p < 0.05). Many associations between cannabis use and psychiatric diagnoses were no longer significant after controlling for trauma history. GAD-7, PHQ-9, WHODAS, and PSQI scores significantly differed between groups (p < 0.001); the past use group had the highest scores (p < 0.05), and these associations persisted when controlling for trauma history. Conclusions: Lifetime cannabis use was associated with poorer mental health characteristics among patients seeking treatment for SUDs, possibly due to greater incidence of trauma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".