"Housing First" for Homeless Youth With Mental Illness
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: "Housing First" has been shown to improve housing stability in homeless individuals with mental illness, but had not been empirically tested in homeless youth. We aimed to evaluate the effect of "Housing First" on housing stability in homeless youth aged 18 to 24 years participating in At Home/Chez Soi, a 24-month randomized trial of "Housing First" in 5 Canadian cities. METHODS: Homeless individuals with mental illness were randomized to receive "Housing First" (combined with assertive community treatment or intensive case management depending on their level of need) or treatment as usual. We defined our primary outcome, housing stability, as the percent of days stably housed as a proportion of days for which residence data were available. RESULTS: Of 2148 participants who completed baseline interviews and were randomized, 7% (n = 156) were youth aged 18 to 24 years; 87 received "Housing First" and 69 received treatment as usual. In an adjusted analysis, youth in "Housing First" were stably housed a mean of 437 of 645 (65%) days for which data were available compared with youth in treatment as usual, who were stably housed a mean of 189 of 582 (31%) days for which data were available, resulting in an adjusted mean difference of 34% (95% confidence interval, 24%-45%; P < .001). CONCLUSIONS: "Housing First" was associated with improved housing stability in homeless youth with mental illness. Future research should explore whether adaptations of the model for youth yield additional improvements in housing stability and other outcomes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".