Understanding the Link Between Housing and Drug Use: Findings from a Survey of People Who Use Drugs in a Mid-Size Canadian City
Bibliographic record
Abstract
BACKGROUND: This article investigates the potential links between housing instability and unregulated drug use at the intersection of the housing affordability crisis, drug poisoning emergency, and the COVID-19 pandemic. METHODS: = 406, April-September 2023). This study examines the associations between housing instability, the severity of individual drug use patterns, as measured through the Drug Use Disorders Identification Test (DUDIT) score, and the risk of accidental overdose using a series of linear and logistic regression models. RESULTS: Results indicate that precarious housing conditions were linked to a higher risk of unregulated drug use across respondents. Being unhoused or living in unstable housing was associated with higher DUDIT scores and an increased probability of accidental overdose. In addition, individuals who reported worsening housing situations during COVID-19 were more likely to report increased drug use. CONCLUSIONS: Contributing to the literature on social determinants of health, findings suggest that integrated housing and drug use interventions are vital for effective harm reduction and the promotion of public health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".