Does receipt of social housing impact mental health? Results of a quasi-experimental study in the Greater Toronto Area
Bibliographic record
Abstract
Affordable housing is commonly described as an important determinant of health, but there are relatively few intervention studies of the effects of housing on health. In this paper, we describe the results of a quasi-experimental, longitudinal study investigating the impacts of receiving social housing among a cohort of 502 people on waitlists for social housing in the Greater Toronto Area, Canada. Specifically, we sought to determine if adults who received housing were more likely than a control group to show improvements in depression, psychological distress, and self-rated mental health 6, 12 and 18 months after moving to housing. Amongst the participants, 137 received social housing and completed at least one follow-up interview; 304 participants did not receive housing and completed at least one follow-up interview and were treated as a control group (47 people provided data to both groups). The difference-in-differences technique was used to estimate the effect of receiving housing by comparing changes in the outcomes over time in the housed (intervention) group and the group that remained on the waitlist for social housing (control group). Adjusted mixed effects linear models showed that receiving housing resulted in significant decreases in psychological distress and self-rated mental health between the groups. Improvements in self-rated mental health between the groups were observed 6, 12 and 18 months after receiving housing (6 months, +2.9, p < 0.05; 12 months, +2.6, p < 0.05; 18 months, +3.0, p < 0.05). Reductions in psychological distress (-1.4, p < 0.05) were observed 12 months after receiving housing. Overall findings suggest that receiving subsidized housing improves mental health over a 6-to-18-month time horizon. This has policy and funding implications suggesting a need to reduce wait times and expand access to subsidized 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| 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".