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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".