Harm Reduction at its Best: A case for Promoting Safe Injection Facilities
Bibliographic record
Abstract
Injection Drug Users (IDU) represent less than 1% of Canada's the total population. Nevertheless, it is estimated that health and law enforcement costs for controlling the drug problem in Canada amount to $5 billion annually. The current strategies targeting IDU have limited efficacy with reducing emergency department visits, limiting HIV/Hep C virus transmission and providing accessible health care. This paper makes the case for safe injection facilities (SIF) as a means to improve IDU health outcomes, while reducing health care expenditures, decreasing public injecting and having no impact on crime rates. This topic is of particular concern now that the Conservative government is in the process of trying to pass bill C-2 to modify the Controlled Drugs and Substance Act making exemptions for SIF inaccessible. This is occurring while leading researchers in the field are applying for an exemption for such a facility in Ottawa.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.038 | 0.037 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".