The Effect of Neighborhood Experiences on Positive Mental Health Among Community-Dwelling Older Adults
Bibliographic record
Abstract
Abstract Given reduced life spaces, the neighborhood often functions as a social venue for older adults. Yet how these everyday social spaces affect older adults’ psychosocial wellbeing remains largely unknown. Drawing on the GRP-CARE Survey data, this paper examined the relation between neighborhood experiences and positive mental health. Participants were 601 community-dwelling Singaporeans aged 50+ who lived in public housing neighborhoods. Neighborhood experiences were measured using the four-factorial, 16-item OpenX scale (Gan, Fung, Cho, 2019); positive mental health was measured using a six-factorial, 19-item scale (Vaingankar et al., 2011). Both scales have good psychometric properties and had been validated. Path analysis between relevant factors of both scales was conducted using Stata, within a theorized model of causation from neighborhood environment to social factors to psychosocial health. Age, education, ethnicity and sex were controlled for. Multiple linear regression analysis showed a strong, positive association between neighborhood experiences and mental health (p=0.000) even after controlling for personal traits (operationalized as depressive symptoms, GDS) in addition to sociodemographic variables. Path analysis showed that two distinct neighborhood health processes mediated this association. These were (1) the potential for a sense of community in the neighborhood improved emotional support, and (2) having better neighborly friendships improved interpersonal skills. These neighborhood health processes provide us with new lenses to understand older adults’ everyday experiences of their neighborhoods. Community-based interventions to improve older adults’ psychosocial wellbeing may be developed to facilitate these processes. Spatial and programmatic implications will be discussed in relation to age-friendly cities and communities (AFCC).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".