How did a Housing First intervention improve health and social outcomes among homeless adults with mental illness in Toronto? Two-year outcomes from a randomised trial
Bibliographic record
Abstract
OBJECTIVES: We studied the impact of a Housing First (HF) intervention on housing, contact with the justice system, healthcare usage and health outcomes among At Home/Chez Soi randomised trial participants in Toronto, a city with an extensive service network for social and health services for individuals who are experiencing homelessness and mental illness. METHODS: Participants identified as high needs were randomised to receive either the intervention which provided them with housing and supports by an assertive community treatment team (HF+ACT) or treatment as usual (TAU). Participants (N=197) had in-person interviews every 3 months for 2 years. RESULTS: The HF+ACT group spent more time stably housed compared to the TAU group with the mean difference between the groups of 45.8% (95% CI 37.1% to 54.4%, p<0.0001). Accounting for baseline differences, HF+ACT group showed significant improvements over TAU group for community functioning, selected quality-of-life subscales and arrests at some time points during follow-up. No differences between HF+ACT and TAU groups over the follow-up were observed for health service usage, community integration and substance use. CONCLUSIONS: HF for individuals with high levels of need increased housing stability and selected health and justice outcomes over 2 years in a city with many social and health services. TRIAL REGISTRATION NUMBER: ISRCTN42520374.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".