Housing First Improves Residential Stability in Homeless Adults With Concurrent Substance Dependence and Mental Disorders
Bibliographic record
Abstract
OBJECTIVES: We examined the relationship between substance dependence and residential stability in homeless adults with current mental disorders 12 months after randomization to Housing First programs or treatment as usual (no housing or support through the study). METHODS: The Vancouver At Home study in Canada included 2 randomized controlled trials of Housing First interventions. Eligible participants met the criteria for homelessness or precarious housing, as well as a current mental disorder. Residential stability was defined as the number of days in stable residences 12 months after randomization. We used negative binomial regression modeling to examine the independent association between residential stability and substance dependence. RESULTS: We recruited 497 participants, and 58% (n = 288) met the criteria for substance dependence. We found no significant association between substance dependence and residential stability (adjusted incidence rate ratio = 0.97; 95% confidence interval = 0.69, 1.35) after adjusting for housing intervention, employment, sociodemographics, chronic health conditions, mental disorder severity, psychiatric symptoms, and lifetime duration of homelessness. CONCLUSIONS: People with mental disorders might achieve similar levels of housing stability from Housing First regardless of whether they experience concurrent substance dependence.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".