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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".