Multi-trajectory group profiles of well-being and associated predictors among adults experiencing homelessness and mental illness: findings from the At Home/Chez Soi study, Toronto site
Bibliographic record
Abstract
PURPOSE: To conduct a multi-dimensional and time-patterned analysis to identify distinct well-being trajectory profiles over a 6-year follow-up period among adults experiencing homelessness and mental illness. METHODS: Data from 543 participants of the At Home Chez Soi study's Toronto site were examined over a 6-year follow-up period, including measures of quality of life, community functioning, housing stability, and substance use. Well-being trajectories were identified using Group-Based Trajectory Modelling. Multinomial regression was used to identify predictor variables that were associated with each well-being trajectory profile. RESULTS: Four well-being profiles were identified: low well-being, moderate well-being, good well-being, and high well-being. Factors associated with a greater likelihood of following a better well-being profile included receiving Housing First, reporting female gender and non-white ethnicity, having post-secondary studies, and reporting a high resilience level. Concurrently, factors associated with a lower likelihood of better well-being profiles were having a history of chronic homelessness, experiences of discrimination in the healthcare setting, having comorbid mental disorders and a high level of symptom severity, and reporting a history of traumatic brain injury and childhood adversity. CONCLUSIONS: Individuals experiencing homelessness follow distinct well-being profiles associated with their socio-demographic characteristics, health status, trauma history, resilience capabilities, and access to housing and support services. This work can inform integrated housing and support services to enhance the well-being trajectories of individuals experiencing homelessness. TRIAL REGISTRATION: At Home/Chez Soi trial was registered with ISRCTN, ISRCTN42520374, http://www.isrctn.com/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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".