Tenants with additional needs: when housing first does not solve homelessness
Bibliographic record
Abstract
BACKGROUND: At Home/Chez-Soi was a 24 month randomized controlled trial of Housing First (HF) conducted in five Canadian cities. AIMS: This article attempts to identify the characteristics of participants who experienced housing instability one year after entering HF. METHODS: Those defined as experiencing housing instability were housed <50% of the last 9 months of the first year, excluding time in institutions, unless they were housed 100% of the past 3 months. RESULTS: Only 13.5% of HF participants (n = 157/1162) met criteria for housing instability. Several variables were significant predictors of instability in between-group comparisons and multiple regression analyses: residence in Winnipeg, cumulative lifetime homelessness, percent of previous 3 months spent in jail, and community psychological integration; while residence in Moncton and a diagnosis of PTSD or panic disorder predicted stability. The predictive models were weak, identifying correctly only 3.8% of individuals that failed to achieve housing stability. CONCLUSIONS: It is not possible to predict confidently at baseline who will experience early housing instability in HF. There are certain individual characteristics that might be considered risk factors. Providing HF to all individuals who qualify for a HF program remains the most valid way to administer admission to housing.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".