Housing First for People with Severe Mental Illness Who are Homeless: A Review of the Research and Findings from the at Home—Chez soi Demonstration Project
Bibliographic record
Abstract
OBJECTIVE: To provide a review of the extant research literature on Housing First (HF) for people with severe mental illness (SMI) who are homeless and to describe the findings of the recently completed At Home (AH)-Chez soi (CS) demonstration project. HF represents a paradigm shift in the delivery of community mental health services, whereby people with SMI who are homeless are supported through assertive community treatment or intensive case management to move into regular housing. METHOD: The AH-CS demonstration project entailed a randomized controlled trial conducted in 5 Canadian cities between 2009 and 2013. Mixed methods were used to examine the implementation of HF programs and participant outcomes, comparing 1158 people receiving HF to 990 people receiving standard care. RESULTS: Initial research conducted in the United States shows HF to be a promising approach, yielding superior outcomes in helping people to rapidly exit homelessness and establish stable housing. Findings from the AH-CS demonstration project reveal that HF can be successfully adapted to different contexts and for different populations without losing its fidelity. People receiving HF achieved superior housing outcomes and showed more rapid improvements in community functioning and quality of life than those receiving treatment as usual. CONCLUSIONS: Knowledge translation efforts have been undertaken to disseminate the positive findings and lessons learned from the AH-CS project and to scale up the HF approach across Canada.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".