Establishing a regional drug profile in Newfoundland and Labrador, Canada, using data from acute drug deaths
Bibliographic record
Abstract
INTRODUCTION: Drug deaths are rising in Canada and are driven by polydrug toxicity (that is toxicity resulting from multiple drugs belonging to different drug classes). Addressing polydrug toxicity requires the establishment of regional drug profiles so that appropriate harm reduction policies can be implemented. METHODS: Using toxicology data from individuals who died from acute drug toxicity, we established regional drug profiles for the Canadian province of Newfoundland and Labrador. Medical examiners determined which drugs contributed to each acute death. Counts were described to establish the most common drugs and classes. RESULTS: Between 2018 and 2023, 222 individuals died from unintentional acute drug toxicity, and a majority of deaths were from polydrug toxicity. Stimulants and opioids were the most frequent drug class combinations in the sample. Cocaine was the most frequent drug contributing to death and was involved in a majority of stimulant-related deaths. Opioid-related deaths involved many drugs, and deaths resulting from non-pharmaceutical opioids and opioid agonists used in opioid depen-dence treatment rose sharply in the later years of the study. DISCUSSION: Stimulants, especially cocaine, disproportionately contributed to stimulant-related deaths, reinforcing the need for stimulant-specific harm reduction measures in the region. Among opioids, the sharp rise in deaths from opioid agonists used in opioid depen-dence treatment requires policy attention, and the emergence of non-pharmaceutical opioids presents an opportunity to implement policies that have shown success in other regions. Policy impacts and suggestions are discussed, including the need for drug-checking services so that drug profiles can be established more quickly and reflect drug use that is not specific to toxicity. CONCLUSIONS: A total of 222 individuals died from unintentional acute drug toxicity in Newfoundland and Labrador between 2018 and 2023, with polydrug toxicity comprising a majority (55.4%) of these fatalities. Stimulants and opioids were the most prevalent drug classes, with cocaine implicated in most stimulant-related deaths and various types of opioids involved in opioid-related 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".