Suicide attempts among people who use drugs: a comparative gender-based analysis using the ANRS-Coquelicot survey in France
Bibliographic record
Abstract
Objective There exists no national French study on suicide attempts among people who use drugs (PWUD). Our objectives are to analyze lifetime suicide attempts in that population and to compare associated risks based on gender.Method The ANRS-Coquelicot study (2011–2013) conducted 1718 interviews with people over 18 who have injected or snorted a drug or medication (whether or not it was prescribed) at least once in their lifetime, who speak French or Russian, and who attend harm reduction facilities and drug treatment centers in France. Through stratified multivariate analyses based on gender using a Poisson regression, we determined risk factors.Results Among PWUD, 39.9% had attempted suicide (n = 655), with a distribution of 58.8% (n = 188) for women and 35.0% (n = 467) for men. Experiences of overdose, depression, receiving psychiatric care, setting during adolescence, daily alcohol use, and number of substances consumed during the last month were risk factors for men. Among women, experiences of overdose, self-reported HIV positive, and daily use of benzodiazepines and cocaine were risk factors.Conclusion we should develop psychiatric care into health services catering specifically to PWUD. As risk factors differed based on gender, we should strive to create distinct preventive health measures adapted to different populations. Keywords: people who use drugs; lifetime suicide attempts; gender; mental health, quantitative survey
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".