A Longitudinal Study of Predictors of Housing Stability, Housing Quality, and Mental Health Functioning Among Single Homeless Individuals Staying in Emergency Shelters
Bibliographic record
Abstract
The current study examined risk and resilience factors at multiple levels that affect homeless individuals' ability to exit homelessness and achieve housing stability. It also examined the relationship between housing status, housing quality and mental health functioning. The methodology is a longitudinal study of single homeless individuals staying in emergency shelters in a medium-sized Canadian city who were followed for a 2 year period. Data were collected from participants at a baseline interview when they were homeless and at a 2-year follow-up. There were 329 participants interviewed at baseline and 197 (59.9%) participants interviewed at follow-up. Results from a structural equation modelling analysis found that having interpersonal and community resources were predictive of achieving housing stability. Specifically, having a larger social support network, access to subsidized housing, and greater income was related to achieving housing stability. On the other hand, having a substance use problem was a risk factor associated with a failure to achieving housing stability. Being female, feeling personally empowered, having housing that is perceived of being of higher quality were directly predictive of mental health functioning at follow-up. Findings are discussed in the context of previous research and their policy implications.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".