The Impact of Housing First on Criminal Convictions: Results of A Randomized Controlled Trail
Bibliographic record
Abstract
Background Homelessness and mental illness have a strong association with public disorder. Gaps in care for people experiencing homelessness and mental illness put them at risk as both victims and perpetrators of crime. Experimental evidence indicates that Housing First (HF) increases housing stability and perceived choice among those experiencing chronic homelessness and mental disorders. HF is also associated with lower residential costs than common alternative approaches. Few studies have examined the effect of HF on criminal behaviour and public or personal safety. Methods Individuals meeting criteria for homelessness and a current mental disorder were randomized to one of three conditions: treatment as usual (reference); scattered site HF; and congregate HF. Administrative data concerning justice system events were linked in order to study prior histories of offending and to test the relationship between housing status and offending following randomization for up to two years. Results Of the 297 study participants, 67% (N = 198) had recorded involvement with the justice system, with a mean of 8.07 convictions per person in the ten years prior to recruitment. The most common category of crime was “property offenses” (mean = 4.09). Following randomization, the scattered site HF condition was associated with significantly lower numbers of sentences than treatment as usual (Adjusted IRR = 0.29; p ≤ 0.05). Congregate HF was associated with a marginally significant reduction in sentences compared to treatment as usual (Adjusted IRR = 0.55; p = 0.108). Conclusions This study is the first randomized controlled trial to demonstrate benefits of HF in the domain of public safety and crime. The results also illustrate the interdependence between public health and public order. Our sample was frequently involved with the justice system, with great personal and societal costs. Further implementation of HF is strongly indicated in response to a critical gap in support for people who experience homelessness and mental illness. Trial registration: ISRCTN57595077 Key messages People who are homeless and mentally ill are at great risk for involvement in crime. Experimental evidence indicates that Housing First reduces crime.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".