Exploring the contextual risk factors and characteristics of individuals who died from the acute toxic effects of opioids and other illegal substances: listening to the coroner and medical examiner voice
Bibliographic record
Abstract
INTRODUCTION: Substance-related acute toxicity deaths continue to be a serious public health concern in Canada. This study explored coroner and medical examiner (C/ME)perspectives of contextual risk factors and characteristics associated with deaths from acute toxic effects of opioids and other illegal substances in Canada. METHODS: In-depth interviews were conducted with 36 C/MEs in eight provinces and territories between December 2017 and February 2018. Interview audio recordings were transcribed and coded for key themes using thematic analysis. RESULTS: Four themes described the perspectives of C/MEs: (1) Who is experiencing a substance-related acute toxicity death?; (2) Who is present at the time of death?; (3) Why are people experiencing an acute toxicity death?; (4) What are the social contextual factors contributing to deaths? Deaths crossed demographic and socioeconomic groups and included people who used substances on occasion, chronically, or for the first time. Using alone presents risk, while using in the presence of others can also contribute to risk if others are unable or unprepared to respond. People who died from a substance-related acute toxicity often had one or more contextual risk factors: contaminated substances, history of substance use, history of chronic pain and decreased tolerance. Social contextual factors contributing to deaths included diagnosed or undiagnosed mental illness, stigma, lack of support and lack of follow-up from health care. CONCLUSION: Findings revealed contextual factors and characteristics associated with substance-related acute toxicity deaths that contribute to a better understanding of the circumstances surrounding these deaths across Canada and that can inform targeted prevention and intervention efforts.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".