The Potential Role for Supervised Injection Facilities in Canada’s Largest City, Toronto
Bibliographic record
Abstract
Supervised injection facilities (SIFs) or supervised consumption rooms are a component of harm reduction strategies that attempt to reduce drug overdoses and risky injection behaviors among injection drug users. The purpose of this study is to determine whether expanding SIFs into the City of Toronto, Ontario, would be a fiscally responsible decision. By analyzing secondary data gathered in 2013, this article relies on mathematical models to estimate the number of new HIV and hepatitis C virus infections prevented as a result of SIF locations in Toronto. After factoring in the costs associated with SIFs, the models produce cost–benefit and cost-effectiveness outputs. With very conservative estimates, it is predicted that establishing SIF locations in Toronto is cost effective with an average benefit–cost ratio of 1:1.2 for the first two facilities based on the sensitivity analysis at 30% sharing rate. Consequently, funding SIFs in Canada’s largest city appears to be an efficient and effective use of financial resources in the public health domain with cost savings in excess of CAN$728,620 per year for the first two facilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".