Strategies for Addressing Canada’s Opioid Crisis: A Cross-National Comparative Analysis of Drug Policies
Bibliographic record
Abstract
In 2020, the opioid epidemic claimed the lives of at least 7,560 Canadians, making it the deadliest addiction crisis in Canadian history (1). Contributing to the worsening of the opioid crisis is the criminalization of drug use, which has stigmatized individuals who use drugs, exacerbated health harms, widened socioeconomic and racial disparities and created a toxic illegal drug market, now responsible for most overdose deaths (2). Reforming the criminal justice-led approach and decriminalizing small amounts of drugs for personal possession have been proposed as potential solutions to the opioid crisis, having been successfully implemented to combat drug-related harms in other countries. This study seeks to examine alternatives to the drug criminalization approach through employing a cross-national comparative analysis of drug policies in Canada, Portugal, West Virginia, and Switzerland and assess which policy framework has been successful at reducing drug-related harms. The investigation reveals that punitive drug policies exacerbate drug-related harms, including overdose deaths, drug-related incarceration, problematic drug use and HIV infections. Alternatively, the low rates of opioid overdose deaths and related harms in European countries, like Portugal and Switzerland, can be attributed to innovative harm reduction policies and programs that improve access to treatment facilities and safe drug supplies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".