A cross-sectional study of the relationship between frequency of cannabis use and psychiatric symptoms among people seeking mental health and addiction services in Nova Scotia (2019–21)
Bibliographic record
Abstract
BACKGROUND: Cannabis use may be a modifiable risk factor for mental health problems; however, the role of cannabis use frequency in population seeking mental health and addiction services remains unclear. This study aimed to: 1) compare the prevalence and functional impact of psychiatric symptoms among frequent, infrequent, and non-users of cannabis; and 2) evaluate the associations between cannabis use frequency and functional impact of psychiatric symptoms in help-seeking individuals. METHODS: Data from the Mental Health and Addictions (MHA) Central Intake system in Nova Scotia, Canada was used. Participants aged 19-64 who received MHA Intake assessments from September 2019 to December 2021 with complete information about substance use were included (N = 20,611). Cannabis use frequency over past 30 days was categorized into frequent (>4 times a month), infrequent (≤4 times a month), and non-use. Psychiatric symptomatology consists of five domains: mood, anxiety, psychosis, cognition, and externalizing behaviors. Multivariate ordinal logistic regression was used to examine the associations between cannabis use frequency and functional impact of psychiatric symptoms. RESULTS: Frequent and infrequent cannabis users had a higher prevalence of psychiatric symptoms in each domain than non-users, while no significant differences were found between frequent and infrequent users. Frequent cannabis use was associated with greater functional impact of psychiatric symptoms in each domain compared to non-users, while infrequent use was only associated with greater functional impact of externalizing behaviors. CONCLUSION: Frequent cannabis use is associated with increased prevalence and functional impact of psychiatric symptoms among adults seeking mental health services.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".