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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".