Longitudinal Associations between Perceived Quality of Living Spaces and Health-Related Quality of Life among Homeless and Vulnerably Housed Individuals Living in Three Canadian Cities
Bibliographic record
Abstract
The objective of this study was to examine longitudinal associations between perceived quality of living spaces and mental and physical health-related quality of life (HRQoL) among homeless and vulnerably housed individuals living in three Canadian cities. The Health and Housing in Transition (HHiT) study was a prospective cohort study conducted between 2009 and 2013 of N = 1190 individuals who were homeless and vulnerably housed at baseline. Perceived quality of living spaces (based on rated comfort, safety, spaciousness, privacy, friendliness and overall quality) and both mental and physical HRQoL were assessed at baseline and at four annual follow up points. Generalized estimating equation (GEE) analyses were used to examine associations between perceived quality of living spaces and both mental and physical HRQoL over the four-year study period, controlling for time-varying housing status, health and socio-demographic variables. The results showed that higher perceived quality of living spaces was positively associated with mental (b = 0.42; 95% CI 0.38—0.47) and physical (b = 0.11; 95% CI 0.07—0.15) HRQoL over the four-year study period. Findings indicate that policies aimed at increasing HRQoL in this population should prioritize improving their experienced quality of living spaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".