Examining the need profile of supportive housing applicants with and without current justice involvement: A cross-sectional study.
Bibliographic record
Abstract
OBJECTIVE: Permanent supportive housing has been identified as a possible intervention which could reduce the risk of criminal justice involvement (CJI) among people with serious mental illness who are experiencing residential instability and homelessness. However, the evidence evaluating the impact of supportive housing on CJI is mixed. Increasing understanding of the specific needs of people with justice involvement entering supportive housing may inform interventions which improve their criminal justice outcomes. Toward this end, this study compared the characteristics and need profiles of applicants to supportive housing with and without current CJI. METHODS: Secondary analyses were conducted of archival databases of 12,277 individuals applying for supportive housing in Toronto, Canada between 2009 and 2015. The characteristics and support needs of applicants with current CJI were compared to applicants with no current justice involvement. RESULTS: Of the applicants, 2,894 (23.6%) reported having justice involvement at the time of referral to supportive housing services. The strongest predictors of having justice involvement were a history of physical assaults, homelessness, problematic substance use, male gender, and younger age. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Criminological factors rather than functional or mental health needs were most predictive of having current justice involvement at referral. Development of justice-focused supportive housing models may be considered which supplement traditional supportive housing approaches with empirically based interventions targeting the criminogenic needs of people with mental illness and justice involvement. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".