Impact of a Housing First intervention on homeless Veterans with mental illness: a Canadian multisite randomized controlled trial
Bibliographic record
Abstract
Introduction: A large proportion of homeless Veterans live with severe mental health problems. We examine the impact of a Housing First program that included recovery-oriented initiatives (assertive community treatment or intensive case management) among those homeless Veterans who participated in a multisite demonstration project on homelessness and mental health. Methods: The data come from a Canadian multisite randomized trial (ISRCTN42520374), At Home/Chez Soi, with a volunteer sample of 2,285 homeless or precariously housed individuals living with mental health problems. Of this sample, 98 individuals reported being Veterans, of whom 57 were randomized to the intervention group and 41 to the control group. The data come from self-reported measures administered at baseline and after 6, 12, 18, and 24 months from Fall 2009 to Spring 2013. Data were analyzed by fitting a mixed model for each outcome variable, and special attention was given to the event × treatment × Veteran status interaction term. Results: The Housing First approach was effective in improving housing stability, social functioning, and quality of life in homeless Veterans with mental health problems. These results are consistent with the intervention’s effectiveness with other homeless Canadians with mental health problems. Discussion: These results are consistent with those of previous US studies and suggest that a Housing First approach that includes recovery-oriented support would effectively contribute to reducing homelessness in the Canadian Veteran population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".