Predicting who applies to Public Housing using Linked Administrative Data
Bibliographic record
Abstract
ABSTRACT ObjectivePublic housing residents, who live in low income government rental housing, are often in poorer health than the rest of the population. However, few studies have been able to untangle the relationships between health and public housing residency, and to assess whether health contributes to the decision to apply. We used linked population-based administrative data from one Canadian province to compare the health and health service use of people who applied to public housing to that of people who did not apply. ApproachAdministrative data housed in the Manitoba Centre for Health Policy’s Population Health Research Data Repository were used to identify a cohort of individuals who applied to public housing in 2005 and 2006. They were matched one-to-one to a cohort from the general population using socio-demographic variables. A population registry provided demographic and geographic characteristics. Economic measures included receipt of income assistance and an area-level measure from the Statistics Canada Census. Measures of health and health service use were derived from hospital, physician, emergency department, and prescription drug databases. 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. ResultsThere were 10,324 individuals in each of the public housing applicant and matched cohorts; the majority were female (72.4%), young (62% less than 40 years), urban residents (61.2%), and received income assistance (52.8%). A higher percent of the public housing applicant cohort had physician-diagnosed physical and mental health conditions and used more health services compared to the matched cohort. Having a physician-diagnosed respiratory illness (odds ratio [OR] = 1.14, 95% confidence interval [CI] 1.05,1.25), diabetes (OR = 1.24, 95% CI 1.09,1.40), schizophrenia (OR = 1.58, 95% CI 1.30,1.92), affective disorders (OR = 1.37, 95% CI 1.27,1.48), and substance abuse disorders (OR = 1.46, 95% CI 1.25,1.71) were associated with an increased likelihood of applying for public housing, while being diagnosed with cancer (OR = 0.76, 95% CI 0.61,0.96) was associated with a decreased likelihood of applying, after controlling for income differences. High health service users were also more likely to apply for public housing, after controlling for income differences. ConclusionIndividuals who move into public housing are in poor health before they apply. Health and social service supports that are co-located with public housing facilities may help to ensure that residents have successful tenancies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".