Harm Reduction Policies and the Shaping of Canada’s Opioid Crisis
Bibliographic record
Abstract
The growing harms of the opioid crisis in Canada has encouraged proponents of harm reduction to implement safe injection sites. However, harm reduction policies continue to vary throughout the nation, resulting in restricted or no access to these facilities in several provinces and territories (Pelley, 2019). Safe injection sites have been recognized as a valuable harm reduction strategy that successfully reduces the harms associated with illicit drug use of both the individual drug user, as well as the local community (Kral & Davidson, 2017; Jackson, 2020; Mrazovac, et al., 2020; Lovisotto & Baker, 2021). These sites effectively reduce and prevent needle sharing, overdose-related deaths, street injection, disorderly conduct on the streets, hospital/emergency services, and encounters with law enforcement (Mrazovac, et al., 2020). While the existing literature widely argues for the increased implementation of these facilities, safe injection sites continue to be controversial out of concern that there will be an increased societal and economic cost (Kerr, et al., 2017; Serkissian, 2018; Giarratano, 2019). This research analyzed the data from Statistics Canada’s September 2021 report on “Opioid and Stimulant-Related Harms in Canada”, to assess if there was any correlation between provinces and territories that had restricted or no access to safe injection sites and higher rates of opioid related harms and deaths (Special Advisory Committee on the Epidemic of Opioid Overdoses, 2021). Contrary to what has been found in the existing literature, this decreased access resulted in lower rates of opioid related harms and deaths. However, this research was capable of demonstrating the structural problems that lead to substance use disorder and ultimately demonstrated that the opioid crisis is still a serious problem that every region in the nation is still facing (Belzak & Halverson, 2018; Hatt, 2022).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".