Overdose Epidemic in Québec: Population-Level Approaches and Clinical Implications
Bibliographic record
Abstract
Canada’s national surveillance shows an 11% year-over-year decline in deaths from opioid and other unregulated drug poisonings, and a 10% drop in related hospitalisations in 2024. In stark contrast, Québec, home to more than nine million residents, and Montréal, the country’s second-largest city, experienced a continued rise in suspected drug-poisoning mortality through 2024, with fentanyl or analogues detected in almost two-thirds of opioid deaths. We conducted a narrative synthesis of provincial coroner and public-health surveillance tables, Health Canada dashboards, and the 2022–2025 Québec Strategy on Psychoactive-Substance Overdose Prevention. Results indicate a 40% increase in opioid-related mortality since 2018, a parallel uptick in stimulant toxicity, and a five-fold rise in overdose reversals at Montréal supervised-consumption services during the COVID-19 pandemic recovery. We aim to summarise the key problems underlying this epidemic and offer province-specific public-health strategies while also sending a call to action for first-line clinicians and psychiatrists to integrate overdose-risk screening, take-home naloxone, and stimulant-use-disorder treatments into routine care. We further urge Québec healthcare professionals to deepen their knowledge of provincial services such as supervised-injection sites and stay up to date with the rapidly evolving substance-use-prevention literature. Québec’s divergent trajectory underscores the need for region-tailored harm-reduction investments and stronger policy-to-clinic feedback loops to reduce preventable deaths.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".