Bibliographic record
Abstract
The debate around supervised injection facilities (SIF) rages on more than a decade after the opening of Insite, Canada’s first supervised injection site in Vancouver. Recently, an article published in the journal Addiction reignited the discussion when it made a financial case for introducing facilities in Ottawa and Toronto. The model predicts that the introduction of two SIFs in Ottawa and three in Toronto would be a cost-savings measure to prevent the spread of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) among intravenous drug users (IVDUs). Over 600 total cases of HIV or HCV are projected to be averted in a 20-year period, saving over $40 million in healthcare costs in Toronto and over $30 million in Ottawa. Opponents deny the benefits of safe injection sites despite research conducted on Insite, which suggest that these facilities have tremendous utility and are economically viable. Insite targets and attracts high-risk IVDUs, fosters safer injection habits and prevents transmission of needle-sharing diseases. Insite’s facilities also offer complementary detoxification and rehabilitation services and encourage users to register for these programs. In contrast to arguments made by opponents, Insite has not been found to increase incidental overdoses, neighbourhood crime rates, or public disposal of needles. Given the outcomes of research conducted on Insite, the viability of similar facilities in Ontario should be further explored.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.113 | 0.026 |
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".