Trajectories of Incarceration Over Six Years Among People with Mental Illness and Experiences of Homelessness: Predictive Factors Among Participants in a Randomized Trial of Housing First: Trajectoires d’incarcération sur six ans chez les personnes atteintes de maladie mentale et ayant connu l’itinérance–Facteurs prédictifs chez les participants à un essai à répartition aléatoire en marge du programme «Logement d’abord»
Bibliographic record
Abstract
Objective To identify long-term trajectories of incarceration, impact of Housing First intervention, and associated predictor factors among people with mental illness and experiences of homelessness who participated in a randomized trial of Housing First in Toronto, Canada. Methods Participants in the Toronto site of the At Home/Chez Soi study ( n = 559) were followed from 2009 to 2017. The primary outcome of interest was incarceration trajectories, analyzed using group-based trajectory modelling. Multinomial logistic regression was used to examine the association between Housing First intervention, baseline socio-demographic and health characteristics, and trajectories of incarceration. Results Three group-based incarceration trajectories were identified: Low (66.3%), decreasing (23.1%), and high (10.6%). Younger age, early onset of homelessness, longer duration of homelessness, male gender, drug and alcohol dependence or abuse disorders, and history of traumatic brain injury were significant predictors of high and decreasing incarceration trajectories compared to low trajectory. Receiving Housing First was not significantly associated with incarceration trajectory group. Conclusions A small subgroup of individuals with mental illness and experiences of homelessness demonstrated a persistently high and long-term incarceration trajectory. Multi-disciplinary collaborations with mental health, housing and the criminal justice systems are needed, especially for individuals at increased risk of future incarceration. The trial is registered in the ISRCTN registry (ISRCTN42520374).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".