Supervised injection facilities in Canada: past, present, and future
Bibliographic record
Abstract
Canada has long contended with harms arising from injection drug use. In response to epidemics of HIV infection and overdose in Vancouver in the mid-1990s, a range of actors advocated for the creation of supervised injection facilities (SIFs), and after several unsanctioned SIFs operated briefly and closed, Canada's first sanctioned SIF opened in 2003. However, while a large body of evidence highlights the successes of this SIF in reducing the health and social harms associated with injection drug use, extraordinary efforts were needed to preserve it, and continued activism by local people who inject drugs (PWID) and healthcare providers was needed to promote further innovation and address gaps in SIF service delivery. A growing acceptance of SIFs and increasing concern about overdose have since prompted a rapid escalation in efforts to establish SIFs in cities across Canada. While much progress has been made in that regard, there is a pressing need to create a more enabling environment for SIFs through amendment of federal legislation. Further innovation in SIF programming should also be encouraged through the creation of SIFs that accommodate assisted injecting, the inhalation of drugs. As well, peer-run, mobile, and hospital-based SIFs also constitute next steps needed to optimize the impact of this form of harm reduction intervention.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".