Predictors of housing instability and stability among Housing First participants: A 24-month study
Bibliographic record
Abstract
This study examined the characteristics of people who experienced housing instability and stability after 24 months of being enrolled in Housing First (HF). A companion study addresses the same objective using qualitative methods. A sequential logistic regression was conducted to determine predictors of unstable and stable housing at 24 months of enrollment in HF in a randomized trial. We applied the Gelberg-Andersen. Behavioral Model for Vulnerable Populations to identify and group predictor variables. Thirty-one percent of the HF participants (N = 302/977) met the study criteria for housing instability (i.e. stably housed for less than 90% of last six months). Residence in Winnipeg, longer accumulated lifetime homelessness, and higher levels of substance use predicted unstable housing. Residence in Toronto and Montreal, older age, being in an ethno-racial minority group (other than Indigenous), higher income, higher perceived housing quality, and having a family physician predicted stable housing. The findings of the study have implications for strengthening HF supports to better address the needs of HF participants who may be at risk of housing instability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".