Trajectories of Homeless Shelter Utilization in the At Home/Chez Soi Trial of Housing First
Bibliographic record
Abstract
OBJECTIVE: Studies have shown that Housing First, a recovery-oriented housing intervention, is effective in reducing service utilization among homeless individuals with mental illness, but less is known about how Housing First affects patterns of service use over time and about characteristics associated with various utilization trajectories. This analysis aimed to explore latent class trajectories of shelter utilization in a randomized controlled trial of Housing First conducted across five Canadian cities. METHODS: Data from the At Home/Chez Soi trial were analyzed (N=2,058). Latent class growth analysis was performed using days of shelter utilization to identify trajectories over 24 months. Multinomial logistic regression was used to determine which baseline variables, including treatment group, could predict class membership. RESULTS: Four shelter use trajectories were identified: consistently low (N=1,631, 79%); mostly low (N=120, 6%); early temporary increase (N=179, 9%); and higher use, late temporary increase (N=128, 6%). Treatment group was a significant predictor of class membership. Those enrolled in Housing First had lower odds of experiencing higher shelter use trajectories (mostly low: odds ratio [OR]=0.50, 95% confidence interval [CI]=0.34-0.72; early temporary increase: OR=0.21, 95% CI=0.15-0.31; higher use, late temporary increase: OR=0.14, 95% CI=0.09-0.22). Other variables associated with trajectory classes included older age and longer time homeless, both of which were associated with higher shelter use. CONCLUSIONS: Several participant characteristics were associated with different shelter use patterns. Knowledge of variables associated with more favorable trajectories may help to inform service planning and contribute to modeling efforts for homelessness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".