Drug consumption rooms (DCRs) as a setting to address hepatitis C – findings from an international online survey
Bibliographic record
Abstract
BACKGROUND: Prevalence of Hepatitis C Virus (HCV) among people who inject drugs (PWID) is high. Risky injecting behaviours have been found to decrease in drug consumption rooms (DCRs) and supervised injecting facilities (SIFs), yet HCV prevention and treatment in these settings have not been extensively explored. METHODS: To determine the range and scope of HCV prevention and treatment options in these services, we assessed DCR/SIF operational features, their clients' characteristics and the HCV-related services they provide. A comprehensive online survey was sent to the managers of the 91 DCRs/SIFs that were operating globally as of September 2016. A descriptive cross-country analysis of the main DCR/SIF characteristics was conducted and bivariate logistic models were used to assess factors associated with enhanced HCV service provision. RESULTS: Forty-nine valid responses were retrieved from DCRs/SIFs in all countries where they were established at the time of the survey (Australia, Canada, Denmark, France, Germany, Luxembourg, Netherlands, Norway, Spain and Switzerland). Internationally, the operational capacities of DCRs/SIFs varied in terms of funding, location, size and staffing, but their clients all shared common features of vulnerability and marginalisation. Estimated HCV prevalence rates were around 60%. Among a range of health and social services and referrals to other programs, most DCRs/SIFs provided HCV testing onsite (65%) and/or offered liver monitoring or disease management (54%). HCV treatment onsite was offered or was planned to be offered by 21% of DCRs/SIFs. HCV testing onsite was associated with provision of other services addressing blood-borne diseases and HCV treatment was linked to the provision of OST. HCV disease management was associated with employing a nurse at a DCR/SIF and HCV treatment was associated with employing a medical doctor. CONCLUSIONS: DCRs/SIFs offer easy-to-access HCV-related services for PWID. The availability of onsite medical professionals and provision of support and education to non-medical staff are key to enhanced provision of HCV-related services in DCRs/SIFs. Funding and support for HCV treatment at the community level, via low-threshold services such as DCRs/SIFs, are worthy of action.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".