NB housing study protocol: investigating the relationship between subsidized housing, mental health, physical health and healthcare use in New Brunswick, Canada
Bibliographic record
Abstract
BACKGROUND: Income and housing are pervasive social determinants of health. Subsidized housing is a prominent affordability mechanism in Canada; however, waitlists are lengthy. Subsidized rents should provide greater access to residual income, which may theoretically improve health outcomes. However, little is known about the health of tenants who wait for and receive subsidized housing. This is especially problematic for New Brunswick, a Canadian province with low population density, whose inhabitants experience income inequality, social exclusion, and challenges with healthcare access. METHODS: This study will use a longitudinal, prospective matched cohort design. All 4,750 households on New Brunswick's subsidized housing wait list will be approached to participate. The survey measures various demographic, social and health indicators at six-month intervals for up to 18 months as they wait for subsidized housing. Those who receive housing will join an intervention group and receive surveys for an additional 18 months post-move date. With consent, participants will have their data linked to a provincial administrative database of medical records. DISCUSSION: Knowledge of housing and health is sparse in Canada. This study will provide stakeholders with a wealth of health information on a population that is historically under-researched and underserved.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".