Can housing safeguard the homeless? A longitudinal analysis of violent victimisation, housing, and mental health status of the homeless
Bibliographic record
Abstract
Utilising a routine activity and lifestyle theory framework, this study examines victimisation over time of a localised homeless population with mental health conditions that was part of a 24 month Housing First (HF) At Home-Chez Soi project in the Canadian Prairie City. Negative binomial regression equations to account for severe skews in the victimisation data were estimated at baseline, 12 months and 24 months. In most cases, net of demographic and risky lifestyle controls, at 12 and 24 months from baseline there was an inverse relationship between housing stability and victimisation, and as mental illness became more serious, so did victimisation rates. Serious mental illness was the most consistent and strongest predictor of victimisation, while stable housing effects were weak and sometimes nil. At baseline and 12 months, HF cases showed nil or opposite effects to the Treatment as Usual group, but produced lower victimisation rates at 24 months.
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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 | 0.001 | 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".