The relationship between housing characteristics and subjective well-being among older Canadians: a focus on rural-urban residency and gender
Bibliographic record
Abstract
Objective. While most older Canadians prefer to “age in place” in their own homes, not all dwellings meet acceptable housing standards. As a social determinant of health and well-being, housing can affect healthy aging and shape life satisfaction. Evidence suggests that relationships between housing and well-being can differ across the rural-urban continuum and genders. This study examines rural-urban and gender variations in the associations between housing characteristics and subjective well-being, considering whether these are significant modifiers. Methods. I use cross-sectional, individual-level data from the Canadian Housing Survey (pooled 2018 and 2020 data) for respondents aged ≥ 65 years living across the ten provinces (weighted n=7,931,219). Associations between housing characteristics and life satisfaction are measured using weighted stratified multivariate logistic models while controlling for socioeconomic variables and health status. Housing conditions include tenure, dwelling type, repairs needed, overall dwelling satisfaction, thermal comfort, affordability, safety, length of residence, and sense of community belonging. Rural-urban residency is defined using the Index of Remoteness. Results. Results demonstrate that greater dwelling satisfaction, thermal comfort, and sense of belonging are significantly associated with higher life satisfaction across all subsamples. However, the association between some housing characteristics and life satisfaction vary across subgroups. Feeling safe in the dwelling is significantly associated with higher life satisfaction among urban residents, but not among rural dwellers. Living in a dwelling requiring major repairs is associated with lower life satisfaction among women, but not among men. Living in single/semi-detached or row houses is negatively associated with higher life satisfaction only among men; dwelling type is not significant among women. Conclusion. This project examines how conducive the current Canadian housing situation is to promoting well-being and healthy aging among older adults. In doing so, it informs whether the targeting of housing initiatives towards specific subgroups of older adults is needed to better support the ongoing growth of the diverse older adult population. My results suggest that good housing conditions are protective for all subpopulations without much variance in what good conditions are or the extent to which they are associated with well-being across subpopulations
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".