Substance Use and Access to Health Care and Addiction Treatment among Homeless and Vulnerably Housed Persons in Three Canadian Cities
Bibliographic record
Abstract
INTRODUCTION: We examined the prevalence of substance use disorders among homeless and vulnerably housed persons in three Canadian cities and its association with unmet health care needs and access to addiction treatment using baseline data from the Health and Housing in Transition Study. METHODS: In 2009, 1191 homeless and vulnerably housed persons were recruited in Vancouver, Toronto, and Ottawa, Canada. Interviewer administered questionnaires collected data on socio-demographics, housing history, chronic health conditions, mental health diagnoses, problematic drug use (DAST-10≥6), problematic alcohol use (AUDIT≥20), unmet physical and mental health care needs, addiction treatment in the past 12 months. Three multiple logistic regression models were fit to examine the independent association of substance use with unmet physical health care need, unmet mental health care need, and addiction treatment. RESULTS: Substance use was highly prevalent, with over half (53%) screening positive for the DAST-10 and 38% screening positive for the AUDIT. Problematic drug use was 29%, problematic alcohol use was lower at 16% and 7% had both problematic drug and alcohol use. In multiple regression models for unmet need, we found that problematic drug use was independently associated with unmet physical (adjusted odds ratio [AOR] 1.95; 95% confidence interval [CI] 1.43-2.64) and unmet mental (AOR 3.06; 95% CI 2.17-4.30) health care needs. Problematic alcohol use was not associated with unmet health care needs. Among those with problematic substance use, problematic drug use was associated with a greater likelihood of accessing addiction treatment compared to those with problematic alcohol use alone (AOR 2.32; 95% CI 1.18-4.54). CONCLUSIONS: Problematic drug use among homeless and vulnerably housed individuals was associated with having unmet health care needs and accessing addiction treatment. Strategies to provide comprehensive health services including addiction treatment should be developed and integrated within community supported models of care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".