Health and social predictors of applications to public housing: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Residents of public housing are often in poor health. However, it is unclear whether poor health precedes residency in public housing. We compared the health of people who applied to public housing to people who did not apply and had similar socioeconomic characteristics. METHODS: Population-based administrative databases from Manitoba, Canada, containing health, housing and income assistance information were used to identify a cohort of individuals who applied to public housing and a matched cohort from the general population. Conditional logistic regression was used to test the association between a public housing application and health status and health service use, after controlling for income. RESULTS: There were 10 324 individuals in each of the public housing applicant and matched cohorts; the majority were women, young, urban residents, and received income assistance. A higher per cent of the public housing cohort had physician-diagnosed physical and mental health conditions compared to the matched cohort. Physical health, mental health and health service use were significantly associated with applying to public housing, after controlling for individual and area-level income. CONCLUSIONS: Applicants to public housing were in poorer health compared to people of the same income level who did not apply to public housing. These health issues may affect the long-term stability of their tenancy if appropriate services and supports are not provided. Additionally, preventing ill health, better management of mental health and additional supports may reduce the need for public housing, which, in turn, would alleviate the pressure on governments to provide this form of housing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".