Exploratory Research on the Health and Social Outcomes of Public Housing
Bibliographic record
Abstract
BackgroundUnder the National Housing Strategy, the Canadian government will make historic investments in housing over the next decade. The Canadian Mortgage and Housing Corporation is leading a research strategy to evaluate the impact of these investments. As part of this initiative, the Manitoba Center for Health Policy is conducting a pilot study to determine whether administrative data can be used to assess impacts, specifically looking at health, education and involvement in the justice system. ApproachUsing administrative data we tested for changes in healthcare use and justice involvement in the two years before and after a cohort of individuals moved into public housing. Additionally, to determine if changes in the outcomes over time were unique to public housing, we included a matched comparison group of individuals who did not reside in public housing. GLM with generalized estimating equations tested for differences over time and between cohorts in the number of hospitalizations, inpatient days, emergency department visits, and contacts with the criminal justice system. The data were modeled using a Poisson distribution (rate ratio, RR). Results Compared to the matched cohort, individuals accepted into public housing showed a significant decline in number of hospitalizations (pre RR=1.58 (1.53, 1.63), post RR=1.23 (1.19, 1.27), days in hospital (pre RR=1.66 (1.64, 1.68), post RR=1.24 (1.23, 1.26) and visits to the emergency department (pre RR=1.57 (1.52, 1.62), post RR=1.42 (1.38, 1.47). A trend towards fewer involvements with the criminal justice system was also observed (pre RR=1.37 (1.32, 1.43), post RR=1.28 (1.22, 1.34). No significant differences were noted for total respiratory morbidity or high school grades. ConclusionAdministrative data show good potential to be used for the evaluation of public housing impacts on a wide range of health and social outcomes. Additional indicator comparisons will be reported at the conference.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".