Examining the potential role of a supervised injection facility in Saskatoon, Saskatchewan, to avert HIV among people who inject drugs
Bibliographic record
Abstract
BACKGROUND: Research predicting the public health and fiscal impact of Supervised Injection Facilities (SIFs), across different cities in Canada, has reported positive results on the reduction of HIV cases among People Who Inject Drugs (PWID). Most of the existing studies have focused on the outcomes of Insite, located in the Vancouver Downtown Eastside (DTES). Previous attention has not been afforded to other affected areas of Canada. The current study seeks to address this deficiency by assessing the cost-effectiveness of opening a SIF in Saskatoon, Saskatchewan. METHODS: We used two different mathematical models commonly used in the literature, including sensitivity analyses, to estimate the number of HIV infections averted due to the establishment of a SIF in the city of Saskatoon, Saskatchewan. RESULTS: Based on cumulative cost-effectiveness results, SIF establishment is cost-effective. The benefit to cost ratio was conservatively estimated to be 1.35 for the first two potential facilities. The study relied on 34% and 14% needle sharing rates for sensitivity analyses. The result for both sensitivity analyses and the base line estimates indicated positive prospects for the establishment of a SIF in Saskatoon. CONCLUSION: The opening of a SIF in Saskatoon, Saskatchewan is financially prudent in the reduction of tax payers' expenses and averting HIV infection rates among PWID.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".