Impact of a medically supervised safer injection facility on community drug use patterns: a before and after study
Bibliographic record
Abstract
PROBLEM: Illicit use of injected drugs is linked with high rates of HIV infection and fatal overdose, as well as community concerns about public drug use. Supervised injecting facilities have been proposed as a potential solution, but fears have been raised that they might encourage drug use. DESIGN: A before and after study. Participants and setting 871 injecting drug users recruited from the community in Vancouver, Canada. KEY MEASURES FOR IMPROVEMENT: Rates of relapse into injected drug use among former users and of stopping drug use among current users. STRATEGIES FOR CHANGE: Local health authorities established the Vancouver supervised injecting facility to provide injecting drug users with sterile injecting equipment, intervention in the event of overdose, primary health care, and referral to external health and social services. EFFECTS OF CHANGE: Analysis of periods before and after the facility's opening showed no substantial increase in the rate of relapse into injected drug use (17% v 20%) and no substantial decrease in the rate of stopping injected drug use (17% v 15%). LESSONS LEARNT: Recently reported benefits of supervised injecting facilities on drug users' high risk behaviours and on public order do not seem to have been offset by negative community impacts.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".