Temporal Changes in the Cross-Sectional Associations between Cannabis Use, Suicidal Ideation, and Depression in a Nationally Representative Sample of Canadian Adults in 2012 Compared to 2002
Bibliographic record
Abstract
Background:With the recent legalization of nonmedical cannabis in Canada, it is important to document previous associations between cannabis use and major depressive episode and suicidal ideation, as well as the extent to which these associations have changed over time.Methods:This study uses pooled data from the 2002 and 2012 Canadian Community Health Survey’s Mental Health Component, which are repeated cross-sectional surveys of nationally representative samples of Canadians 15 to 60 years of age (n = 43,466). Binary logistic regression was performed, applying weighting and bootstrapping, to examine the association between at least monthly use of cannabis and past 12-month suicidal ideation and major depressive episode (MDE).Results:At least monthly nonmedical cannabis use was associated with an increased odds of MDE and suicidal ideation, and both associations strengthened in 2012 compared to 2002. Canadians using cannabis at least once a month in 2012 had 1.59 (95% confidence interval [CI], 1.11 to 2.27) times the odds of experiencing suicidal ideation and 1.55 (95% CI, 1.12 to 2.13) times the odds of experiencing MDE compared to those who used cannabis at least once a month in 2002. This temporal change remained after controlling for other substance use.Conclusions:Monthly cannabis use was consistently related to both suicidal ideation and MDE, and these associations were stronger in 2012 compared to 2002. The findings of this study provide a baseline for the association between cannabis use and suicide and depression in the Canadian population that should be reevaluated now that nonmedical cannabis has been legalized.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".