Social Determinants and Health Behaviours among Older Adults Experiencing Multimorbidity Using the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
This study examines associations between lifestyle behavioural factors and appraisals of "healthy aging" among older adults experiencing multimorbidity. A Social Determinants and Health Behaviour Model (SDHBM) is used to frame the analyses. Using baseline data from the Canadian Longitudinal Study on Aging (CLSA), we studied 12,272 Canadians 65 years of age or older who reported 2 or more of 27 chronic conditions. Additional analyses were conducted using three multimorbidity clusters: cardiovascular/metabolic, musculoskeletal, and mental health. Using hierarchical logistic regression, it was found that, for multmorbidity and the three illness clusters, healthy aging is consistently associated with not smoking (except for the mental health cluster), an absence of obesity (except for the cardiovascular and metabolic cluster), better sleep, and a better appetite. It is not associated with inactivity. Several socio-demographic, environmental, and illness covariates were also supported. The findings are examined using the SDHBM coupled with a resilience lens in order to elucidate how modifiable health behaviours can act as resources to mitigate multimorbidity adversities. This has implications for healthy aging for persons with multimorbidity, especially during the COVID-19 pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".