Licit and illicit substance use among people who inject drugs and the association with subsequent suicidal attempt
Bibliographic record
Abstract
AIM: To estimate associations between recent licit and illicit substance use and subsequent suicide attempt among people who inject drugs (PWID). DESIGN: Secondary analysis of longitudinal data from a prospective cohort study of PWID followed bi-annually between 2004 and 2011. SETTING: Montréal, Canada. PARTICIPANTS: Seven hundred and ninety-seven PWID who reported injection drug use in the previous 6 months, contributing to a total of 4460 study visits. The median number of visits per participant was five (interquartile range: 3-8). MEASUREMENTS: An interviewer-administered questionnaire eliciting information on socio-demographic factors, detailed information on substance use patterns and related behaviours, mental health markers and suicide attempt. The primary exposure variables examined were past-month use of alcohol [heavy (≥ 60 drinks); moderate (one to 59 drinks); none], sedative-hypnotics, cannabis, cocaine, amphetamine and opioids [regular (≥ 4 days); occasional (1-3 days); none]. The outcome was a binary measure of suicide attempt assessed in reference to the previous 6 months. FINDINGS: In multivariate analyses, a positive association was found among licit substances between heavy alcohol consumption [adjusted odds ratio (AOR) = 2.05; 95% confidence interval (CI) = 1.12-3.75], regular use of sedative-hypnotics (AOR = 1.89; 95% CI = 1.21-2.95) and subsequent attempted suicide. Among illicit substances, occasional use of cannabis (AOR = 1.84; 95% CI = 1.09-3.13) had a positive association with subsequent suicide attempt. No statistically significant association was found for the remaining substances. CONCLUSION: Among people who inject drugs, use of alcohol, sedative-hypnotics and cannabis, but not cocaine, amphetamine or opioids, appears to be associated with an increased likelihood of later attempted suicide.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".