HOUSING CHARACTERISTICS, NEIGHBORHOOD ENVIRONMENTS, AND SELF-RATED MENTAL HEALTH AMONG OLDER CANADIANS
Bibliographic record
Abstract
Abstract Previous studies have established a clear association between the surrounding environment and mental health. Whereas most literature has focused on neighborhood environment, very few studies have examined the role of housing characteristics in self-rated mental health (SRMH). Using data from the 2018 Canadian Housing Survey, this study investigated the relationships between housing characteristics, neighborhood environment, and SRMH among older Canadians and whether the relationships varied by education and gender. Using a sample of 21,725 Canadians, SRMH was measured by older adults’ self-evaluation of mental health on a 5-point scale. We categorized education into three groups: high school or less, some college, and university or beyond. Hierarchical linear regressions showed that men and women with high school education and women with some college educations were more likely to report worse SRMH when living in low-income housing. Reporting a home maintenance need was a unique risk factor of SRMH for men with a university education, whereas living in uninhabitable conditions uniquely predicted better SRMH for men with some college education. Regarding neighborhood environment, safer community was a protective factor of SRMH for women with university education only. Sense of belonging was positively associated with SRMH for all subgroups, except for men with a university education. Expressing a need for community service was significantly associated with lower SRMH for women who completed a high school education or some college. Findings of this study shed light on the diverse need for environmental improvement and maintenance programs to improve SRMH among older Canadians.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".