The Association Between Youth Cannabis Use and Subsequent Health Service Use for Mood, Anxiety, and Psychotic Disorders
Bibliographic record
Abstract
Background: Youth cannabis use may be a risk factor for psychotic, mood, and anxiety disorders (PMADs). However, most previous studies have significant methodological limitations including failing to establish temporality and using older data when cannabis was less potent. Understanding the relationship between youth cannabis use and PMADs is a critical public health issue given recent legalization and Canadian youth being among the heaviest users of cannabis in the world. Methods: Data for Ontario respondents aged 12 to 24 years from the 2009 to 2012 cycles of the Canadian Community Health Survey were pooled and linked to administrative data at ICES (N=12,053). Health service use for 1) MADs and 2) psychotic disorders were examined as separate outcomes. After excluding those with prior MADs, a multivariable Cox proportional hazards model (age as time scale) was used to examine the association between past-year cannabis use frequency (weekly+, <weekly, and never) and MAD-related health service use in the following 3 years, controlling for sociodemographic and substance use confounders. Sex and age were tested as effect modifiers. This analysis was repeated for psychotic disorders, except with a binary exposure and up to 9 years of follow-up. Results: Compared to no past-year cannabis use,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".