Factors associated with housing stability and criminal convictions among people experiencing homelessness and serious mental illness: Results from a Housing First study
Bibliographic record
Abstract
Background: Housing First (HF) facilitates immediate access to independent housing with community-based supports for people experiencing homelessness and serious mental illness (PEHSMI).Despite positive outcomes associated with HF, studies have infrequently investigated factors that are associated with adverse outcomes once in HF.This thesis investigates factors which hinder housing stability following randomization to HF and factors associated with criminal convictions prior to and following randomization to HF. Methods: Three investigations were conducted using data from the Vancouver At Home study, which contains two randomized controlled trials each involving randomization to HF vs. treatment as usual (TAU) among PEHSMI.Using self-reported data, the first investigation examined the effect of experiencing homelessness in childhood or youth on housing stability (≥90% of days stably housed) after receiving HF (TAU excluded).The second investigation retrospectively examined factors associated with criminal convictions during the five-year period preceding baseline.The third investigation examined factors associated with criminal convictions after receiving HF (TAU excluded).Provincial administrative data were combined with self-reported baseline data for the second and third investigations.Results: 1) Among participants randomized to HF (n=297), those who had experienced homelessness in childhood or youth had significantly lower odds of housing stability.2) Prior to study baseline, seven variables were significantly associated with criminal convictions among participants (n=425), such as drug dependence, psychiatric hospitalization, and irregular frequency of social assistance payments (vs.regular).3) Following receipt of HF (n=255), five variables were significantly associated with criminal convictions, including daily drug use, daily alcohol use, and having received addictions counselling among others.Conclusions: Results underscore social marginalization as contributing to poorer housing stability in HF and criminal convictions while in HF and prior to enrollment among PEHSMI.Further supports are needed to facilitate improvements for a greater proportion of HF clients.HF providers may be able to identify clients with additional support needs related to housing stability and criminal convictions by asking about the factors found to be significant in analyses.v
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".