Assessing Drug Consumption Rooms and Longer Term (5 Year) Impacts on Community and Clients
Bibliographic record
Abstract
Vincent Tran,1 Sharon E Reid,2,3 Amanda Roxburgh,1,4– 6 Carolyn A Day1,2 1Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia; 2Edith Collins Centre (Translational Research in Alcohol, Drugs and Toxicology), Sydney Local Health District, Sydney, New South Wales, Australia; 3Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia; 4Health Risks Program, Burnet Institute, Melbourne, Victoria, Australia; 5National Drug and Alcohol Research Centre, UNSW, Randwick, New South Wales, Australia; 6Monash Addiction Research Centre, Monash University, Clayton, Victoria, AustraliaCorrespondence: Carolyn A Day Tel +61 2 95191446Email carolyn.day@sydney.edu.auBackground: Drug consumption rooms (DCRs) and supervised injecting facilities (SIFs) provide a safe environment in which people who inject drugs (PWIDs) can inject under hygienic and supervised conditions. Numerous reviews have documented the benefits of these facilities; however, there is a lack of clarity surrounding their long-term effects.Purpose: To conduct, with a systematic approach, a literature review, of published peer-reviewed literature assessing the long-term impacts of DCRs/SIFs.Methods: A systematic search of the PubMed and Embase database was performed using the keywords: (“SUPERVISED” OR “SAFE*”) AND (“CONSUMPTION” OR “INJECT*” OR “SHOOTING”) AND (“FACILITY*” OR “ROOM*” OR “GALLERY*” OR “CENTRE*” OR “CENTER*” OR “SITE*”). Included studies were original articles reporting outcomes for five or more years and addressed at least one of the following client or community outcomes; (i) drug-related harms; (ii) access to substance use treatment and other health services; (iii) impact on local PWID population; (iv) impact on public drug use, drug-related crime and violence; and (v) local community attitudes to DCRs.Results: Four publications met our inclusion criteria, addressing four of the five outcomes. Long-term data suggested that while the health of PWID naturally declined over time, DCRs/SIFs helped reduce injecting-related harms. The studies showed that DCRs/SIFs facilitate drug treatment, access to health services and cessation of drug injecting. Local residents and business owners reported less public drug use and public syringe disposal following the opening of a DCR/SIF.Conclusion: Long-term evidence on DCRs/SIFs is consistent with established short-term research demonstrating the benefits of these facilities. A relative paucity of studies was identified, with most evidence originating from Sydney and Vancouver. The overall body of evidence would be improved by future studies following outcomes over longer periods and being undertaken in a variety of jurisdictions and models of DCRs/SIFs.Keywords: safe injecting facilities, intravenous, Injecting, harm reduction
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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 | 0.031 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".