Drug use, homelessness and health: responding to the opioid overdose crisis with housing and harm reduction services
Bibliographic record
Abstract
BACKGROUND: Canada is in the midst of an opioid overdose crisis and Alberta has one of the highest opioid use rates across the country. Populations made vulnerable through structural inequities who also use opioids, such as those who are unstably housed, are at an increased risk of experiencing harms associated with opioid use. The main purpose of this study was to explore if there was an association between unstable housing and hospital use for people who use opioids. METHODS: Analysis utilized self-reported data from the Alberta Health and Drug Use Survey which surveyed 813 Albertans in three cities. Hospital use was modeled using a logistic regression with our primary variable of interest being housing unstable status. Chi square tests were conducted between hospital use and variables associated with demographics, characteristics of drug use, health characteristics, and experiences of receiving services to establish model inclusion. RESULTS: Results revealed a significant association between housing instability and hospital use with unstably housed individuals twice as likely torequire hospital care. CONCLUSIONS: Results highlight the importance of concurrently addressing housing instability alongside the provision of harm reduction services such as safe supply and supervised consumption sites. These findings have significant implications for policy and policymakers during the opioid overdose epidemic, and provide a foundation for future areas of research.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".