Environmental Influences on Life Satisfaction and Depressive Symptoms Among Older Adults With Multimorbidity: Path Analysis Through Loneliness in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: More older adults with multimorbidity are aging in place than ever before. Knowing how the environment affects their mental well-being could enhance the efficacy of age-friendly interventions for multimorbidity resilience. With reference to the Transdisciplinary Neighborhood Health Framework, we construct and examine a priori models of environmental influences on life satisfaction and depressive symptoms. RESEARCH DESIGN AND METHODS: Baseline and follow-up data (after 3 years) were drawn from the Canadian Longitudinal Study on Aging to identify a subsample (n = 14,301) of participants aged at least 65 years with at least 2 chronic diseases. Path analysis examined sociobehavioral attributes (i.e., social support, social participation, walking) and loneliness as primary and secondary mediators, controlling for age, sex, education, and outcomes during baseline. RESULTS: Good model fit was found (TFI = 1.00; CFI = 1.00; RMSEA < 0.001; SRMR < 0.001). The total effects of housing quality (rtotal = 0.08, -0.07) and neighborhood cohesion (rtotal = 0.03, -0.06) were weak but statistically significant in the expected direction. The mediators explained 21%-31% of the total effects of housing quality and 67%-100% of the total effects of neighborhood cohesion. Loneliness mediated 27%-29% of these environmental influences on mental well-being, whereas walking mediated a mere 0.4%-0.9% of the total effects. Walking did not explain the relationship between housing quality and mental well-being. DISCUSSION AND IMPLICATIONS: Data supported a priori pathways from environment to mental well-being through sociobehavioral attributes and loneliness. If these pathways from neighborhood cohesion to life satisfaction reflect causal effects, community-based age-friendly interventions should focus on enhancing neighborhood cohesion to mitigate loneliness among multimorbid older adults for their mental well-being.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".