The continuous opioid death crisis in Canada: changing characteristics and implications for path options forward
Bibliographic record
Abstract
Since 2016, more than 30,000 people have died from opioid-related overdoses in Canada-more than from other, major accidental death causes combined. 1,2pioid deaths have markedly slowed trends in life expectancy and triggered (provincial but not federal) states of public health emergency.More recently, related media headlines and political proclamations ("We will not rest and do everything needed …") have largely disappeared.Yet those who read this muting as a sign of victory in the battle against the longstanding overdose death crisis are misguided.Per recent data, there were 3556 (rate: 19/100,000 population) opioid-related toxicity deaths in the first six months of 2022 -proportionally similar to the total number (7,993) and rate of opioidrelated deaths in 2021. 2 These indicators represent the highest annual tolls of opioid deaths since national Canadian statistics were established (2016); they reflect general patterns observed in the United States (US). 3 After raging and taking-many young-lives at growing levels for more than a decade, the opioid-death crisis is very much alive in Canada; worse yet, it has become widely accepted as a part of everyday reality and therefore embraced as a kind of 'new normal'.There are key evolutionary changes of the opioiddeath crisis that mirror those experienced in the US, which have been characterized as different 'waves' of the epidemic unfolding there. 3Concretely, what began in Canada in the early-2010s largely as spikes in overdose-deaths from potent prescription opioid drugs, by 2015/16 changed into an accelerating death epidemic from mostly illicit, toxic synthetic opioids (e.g., fentanyl/ analogues) distributed across North America. 1 Post-2020, >75%-while with stark regional differences -of opioid deaths in Canada have involved fentanyl. 2However, approximately half or more have also involved a psychostimulant (e.g., methamphetamine or cocaine) substance; moreover, increasing proportions have also
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".