Outcome Trajectories among Homeless Individuals with Mental Disorders in a Multisite Randomised Controlled Trial of Housing First
Bibliographic record
Abstract
PURPOSE: Housing First (HF) has been shown to improve housing stability, on average, for formerly homeless adults with mental illness. However, little is known about patterns of change and characteristics that predict different outcome trajectories over time. This article reports on latent trajectories of housing stability among 2140 participants (84% followed 24 months) of a multisite randomised controlled trial of HF. METHODS: Data were analyzed using generalised growth mixture modeling for the total cohort. Predictor variables were chosen based on the original program logic model and detailed reviews of other qualitative and quantitative findings. Treatment group assignment and level of need at baseline were included in the model. RESULTS: In total, 73% of HF participants and 43% of treatment-as-usual (TAU) participants were in stable housing after 24 months of follow-up. Six trajectories of housing stability were identified for each of the HF and TAU groups. Variables that distinguished different trajectories included gender, age, prior month income, Aboriginal status, total time homeless, previous hospitalizations, overall health, psychiatric symptoms, and comorbidity, while others such as education, diagnosis, and substance use problems did not. CONCLUSION: While the observed patterns and their predictors are of interest for further research and general service planning, no set of variables is yet known that can accurately predict the likelihood of particular individuals benefiting from HF programs at the outset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".