Drug consumption rooms are effective to reduce at‐risk practices associated with HIV/HCV infections among people who inject drugs: Results from the COSINUS cohort study
Bibliographic record
Abstract
AIMS: The primary aim of this study was to evaluate the impact of drug consumption rooms (DCRs) in France on injection equipment-sharing, while the secondary aims focused upon their impact on access to hepatitis C virus (HCV) testing and opioid agonist treatment (OAT). DESIGN: The COhort to identify Structural and INdividual factors associated with drug USe (COSINUS cohort) was a 12-month longitudinal study of 665 people who inject drugs (PWID), conducted in Bordeaux, Marseille, Paris and Strasbourg. We used data from face-to-face interviews at enrolment and at 6-month and 12-month visits. SETTING AND PARTICIPANTS: The participants were recruited in harm reduction programmes in Bordeaux and Marseille and in DCRs in Strasbourg and Paris. Participants were aged more than 18 years, French-speaking and had injected substances the month before enrolment. MEASUREMENTS: We measured the impact of DCR exposure on injection equipment sharing, HCV testing and the use of medications for opioid use disorder, after adjustment for significant correlates. We used a two-step Heckman mixed-effects probit model, which allowed us to take into account the correlation of repeated measures and to control for potential bias due to non-randomization between the two groups (DCR-exposed versus DCR-unexposed participants). FINDINGS: The difference of declared injection equipment sharing between PWID exposed to DCRs versus non-exposed was 10% (1% for those exposed versus 11% for those non-exposed, marginal effect = -0.10; 95% confidence interval = -0.18, -0.03); there was no impact of DCRs on HCV testing and OAT. CONCLUSIONS: In the French context, drug consumption rooms appear to have a positive impact on at-risk practices for infectious diseases such as human immunodeficiency virus (HIV) and hepatitis C virus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".