Bulletin No. 22: Supervised Injecting Facilities: What the literature tells us
Bibliographic record
Abstract
Key points Supervised Injecting Facilities (SIFs) are a well-known, and at times controversial public policy measure to reduce the harms associated with injecting drug use A substantial amount of literature has been published on SIFs We located 134 papers and reports that provided reviews, outcome studies, economic evaluations, policy analyses and descriptions of SIF from across the globe The annotated bibliography provides the details of these papers Overall, the research indicates some positive outcomes from SIFs in relation to: Reductions in overdose Less risky injecting practices Improved access to drug treatment, health and welfare services Improvements in public amenity Reductions in crime However, the majority of evidence comes largely from two sites (Sydney and Vancouver), and effectiveness research has been methodologically limited SIFs remain politically contentious, despite the evidence base
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".