Housing status and accidental substance-related acute toxicity deaths in Canada, 2016–2017
Bibliographic record
Abstract
INTRODUCTION: There is a complex relationship between housing status and substance use, where substance use reduces housing opportunities and being unhoused increases reasons to use substances, and the associated risks and stigma. METHODS: In this descriptive analysis of people without housing who died of accidental substance-related acute toxicity in Canada, we used death investigation data from a national chart review study of substance-related acute toxicity deaths in 2016 and 2017 to compare sociodemographic factors, health histories, circumstances of death and substances contributing to death of people who were unhoused and people not identified as unhoused, using Pearson chi-square test. The demographic distribution of people who died of acute toxicity was compared with the 2016 Nationally Coordinated Point-In-Time Count of Homelessness in Canadian Communities and the 2016 Census. RESULTS: People without housing were substantially overrepresented among those who died of acute toxicity in 2016 and 2017 (8.9% versus <1% of the overall population). The acute toxicity event leading to death of people without housing occurred more often in an outdoor setting (24%); an opioid and/or stimulant was identified as contributing to their death more frequently (68%-82%; both contributed in 59% of their deaths); and they were more frequently discharged from an institution in the month before their death (7%). CONCLUSION: We identified several potential opportunities to reduce acute toxicity deaths among people who are unhoused, including during contacts with health care and other institutions, through harm reduction supports for opioid and stimulant use, and by creating safer environments for people without housing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".