Willingness to use a drug consumption room among people who use drugs in Lyon, France, a city with no open scene of drug use (the TRABOUL survey)
Bibliographic record
Abstract
BACKGROUND: Drug consumption rooms (DCRs) have been developed in cities with open drug scenes, with the aim to reduce drug-related harm. In Lyon, France's second-largest city, there is no distinct drug use area, which raised doubts regarding the need for a DCR. METHODS: We conducted a face-to-face survey of 264 people who use drugs (PWUDs), recruited in harm reduction or addiction treatment centers, in the streets or in squats. We assess their willingness to use a DCR, and we collected sociodemographic and medical features. Bivariable comparisons and analyses adjusted for sociodemographic parameters explored the association between willing to use a DCR and other variables, thus providing crude (ORs) and adjusted odds ratios (aORs) and 95% confidence intervals (95% CI). RESULTS: In total, 193 (73.1%) PWUDs accepted to participate (mean age 38.5 ± 9.3 years; 80.3% men). Among them, 64.2% declared willing to use a DCR. Being treatment-seeker (aOR 0.20, 95% CI [0.08-0.51]; p < 0.001) and not living alone (aOR 0.29; 95% CI [0.10-0.86], p = 0.025) were negatively associated with willing to use a DCR. By contrast, receiving precarity social insurance (aOR 4.12; 95% CI [1.86-9.14], p < 0.001), being seropositive for hepatitis C (aOR 3.60; 95% CI [1.20-10.84], p = 0.022), being cannabis user (aOR 2.45; 95% CI [1.01-5.99], p = 0.049), and reporting previous problems with residents (aOR 5.99; 95% CI [2.16-16.58], p < 0.001) or with the police (aOR = 4.85; 95% CI [1.43-16.39], p = 0.011) were positively associated. CONCLUSIONS: PWUDs, especially the most precarious ones, largely supported the opening of a DCR in Lyon, a city with no open drug scene.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".