Evaluation of a Housing First programme for people from the public mental health sector with severe and persistent mental illnesses and precarious housing: Housing, health and service use outcomes
Bibliographic record
Abstract
AIMS AND CONTEXT: This paper reports the evaluation of the Doorway program (2015-18) in Melbourne, Australia. Doorway extends the original Housing First (HF) model in providing housing support to people with precarious housing at-risk of homelessness with Serious and Persistent Mental Illnesses (SPMIs) receiving care within Victoria's public mental health system. Doorway participants source and choose properties through the open rental market, and receive rental subsidies, assistance, advocacy and brokerage support through their Housing and Recovery Worker (HRW). The aim of this study is to estimate Doorway's impact on participants' housing, quality of life and mental health service use. METHODOLOGY: The study employed a a quasi-experimental study design with a comparison group, adjusted for ten potential confounders. The primary outcome measure was days of secure housing per participant. Secure housing status, health service usage and quality of life (HoNOS) data were extracted from participants' electronic hospital and Doorway records in deidentified, non-reidentifiable form. Analysis for continuous outcome variables was based on multivariate GLM modelling. RESULTS: Doorway housed 89 (57%) of 157 accepted participants. The 157 Doorway participants overall were also housed for significantly more days (119.4 extra days per participant) than control participants, albeit after some delay in locating and moving into housing (mean 14 weeks). There was a significant, positive Doorway effect on health outcomes (all and one dimension of the HoNOS). Doorway participants had significantly reduced length of stay during acute and community hospital admissions (7.4 fewer days per participant) compared with the control group. CONCLUSION: The Doorway model represents a new and substantial opportunity to house, enhance health outcomes and reduce mental health service use for people with SPMIs from the public mental health sector and at-risk of 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".