Illicit Drug Use in Canada and Implications for Suicidal Behaviors, and Household Food Insecurity: Findings from a Large, Nationally Representative Survey
Bibliographic record
Abstract
Background and Aims: Illicit drug use is an ongoing health and social issue in Canada. This study aimed to investigate the prevalence of illicit drug use and its implications for suicidal behaviors, and household food insecurity in Canada. Design: Cross-sectional population survey. Setting: Canada, using the 2015–2016 Canadian Community Health Survey, a nationally representative sample selected by stratified multi-stage probability sampling. Participants: A total of 106,850 respondents aged ≥ 12 years who had completed information on illicit drug use. Measurements: Illicit drug use was assessed through a series of questions about illicit drug use methods. Respondents who reported lifetime illicit drug use but no past-year use were considered to have prior illicit drug use. In this survey, illicit drug use included cannabis use. Findings: Overall, the prevalence of lifetime, past-year, and prior illicit drug use was 33.2% (9.8 million), 10.4% (3.1 million), and 22.7% (6.7 million), respectively. In models adjusting for sociodemographic covariates, prior illicit drug use was significantly associated with increased odds of past-year suicidal ideation (adjusted odds ratio [AOR] 1.21, 95% CI 1.04–1.40), and plans (1.48, 1.15–1.91), and past-year household food insecurity (1.27, 1.14–1.41), and the odds were much higher among prior injecting drug users than prior non-injecting drug users. No significant correlation was found between prior illicit drug use and past-year suicidal attempts, but there was a strong association between past-year illicit drug use and past-year suicidal attempts. Conclusions: Our findings suggest that even after people have stopped taking illicit drugs, prior illicit drug use, especially for prior injecting drug use, continues to be associated with increased risks of subsequent suicidal ideation, and plans, and household food insecurity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".