Public housing and healthcare use: Determining whether public housing functions as an intervention using linked population-based administrative data
Bibliographic record
Abstract
IntroductionPublic housing is a form of subsidized housing that is owned and/or managed by government. Previous research suggests that public housing has a positive impact on personal finances and education outcomes, but less is known about if/how it impacts health and healthcare use. Objectives and ApproachUsing linked administrative health and social data, we tested for changes in healthcare use among a cohort who moved into public housing in 2012 and 2013 in Manitoba, Canada, and compared utilization to a matched general population cohort who did not move into public housing. Generalized linear models with generalized estimating equations tested for differences in numbers of healthcare contacts in the years before and after the move-in date, adjusted for economic, residential mobility, and health characteristics. The data were modeled using a Poisson (rate ratio, RR), negative binomial (incident rate ratio, IRR), or a binomial (odds ratio, OR) distribution. ResultsThere were 2619 residents in the public housing cohort; 99.7% were matched to the general population. The cohort by time interaction was statistically significant for inpatient days (p Conclusion/ImplicationsPublic housing residents were more likely to use healthcare services than the matched population, but changes in use were similar in the two cohorts. There is little evidence that public housing impacts healthcare use, but it serves an important function of meeting basic needs for a vulnerable population group.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".