Assessing Drug Consumption Rooms and Longer Term (5 Year) Impacts on Community and Clients
Bibliographic record
Abstract
BACKGROUND: 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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".