Neighborhood Cohesion and the Mental Health of Multimorbid Older Adults: CLSA Path Analysis Through Loneliness
Bibliographic record
Abstract
Abstract More older adults with multimorbidity are aging in place than ever before. Their mental health may be affected by housing and neighborhood factors. In this paper, we use structural equation modelling (SEM) to examine how the physical environment influences life satisfaction and depressive symptoms in two separate models. We included social environment (i.e., social support, social participation, walking) and loneliness as intermediate variables. Data were drawn from baseline and the first follow-up (after 3-4 years) of the Canadian Longitudinal Study on Aging (CLSA). Participants were N=14,301 adults aged □65 with □2 chronic illnesses. Good model fit were found after controlling for age, sex, education and baseline values (TFI=1.00; CFI=1.00; RMSEA<0.001; SRMR<0.001). The total effects of housing quality (Btotal=0.08,-0.07) and neighborhood cohesion (Btotal=0.03,-0.06) were weak but statistically significant in the expected direction. Together, the intermediate variables explained 21-31% of the total effects of housing quality and 67-100% of the total effects of neighborhood cohesion. Loneliness explains 27-29% of the total effects of physical environment on mental health, whereas walking explained a mere 0.4-0.9% of their total effects. Walking did not mediate between housing quality and mental health outcomes. Overall, the results support our path analysis framework: physical environment -> social environment -> loneliness -> mental health. Our model provided excellent explanations of the effects of neighborhood cohesion, especially on life satisfaction. If these associations reflect causal effects, community-based age-friendly interventions should focus on neighborhood cohesion and loneliness to promote the well-being of older adults who are aging in place with multimorbidity.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".