A cost-benefit/cost-effectiveness analysis of proposed supervised injection facilities in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: This paper will determine whether expanding Insite (North America's first and only supervised injection facility) to more locations in Canada such as Montreal, cost less than the health care consequences of not having such expanded programs for injection drug users. METHODS: By analyzing secondary data gathered in 2012, this paper relies on mathematical models to estimate the number of new HIV and Hepatitis C (HCV) infections prevented as a result of additional SIF locations in Montreal. RESULTS: With very conservative estimates, it is predicted that the addition of each supervised injection facility (up-to a maximum of three) in Montreal will on average prevent 11 cases of HIV and 65 cases of HCV each year. As a result, there is a net cost saving of CDN$0.686 million (HIV) and CDN$0.8 million (HCV) for each additional supervised injection site each year. This translates into a net average benefit-cost ratio of 1.21: 1 for both HIV and HCV. CONCLUSIONS: Funding supervised injection facilities in Montreal appears to be an efficient and effective use of financial resources in the public health domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".