THE ROLE OF FRAILTY ON THE IMPACT OF SOCIAL RELATIONSHIPS ON HEALTH OUTCOMES: RESULT FROM THE FRÉLE LONGITUDINAL STUDY
Bibliographic record
Abstract
Abstract The link between social relationships and health is well-established as per Berkman and Krishna’s theory. However, the biological mechanisms by which social relationships impact health, such as frailty, remain unknown. This study aimed to examine whether the effects of changes in social relationships on changes in physical, mental, and cognitive health outcomes varied among frail older adults compared to robust peers. Data were from three waves of the FRéLE study among 1643 Canadian community-dwelling older adults aged 65 years and over. We performed latent growth curve models (LGMs) to test our objectives with the assumption of missing not at random. We measured social isolation through social participation, social networks, and support from different social ties (e.g., children, friends). We assessed frailty using the phenotype of frailty. Health outcomes include disability, chronic diseases, depression, and cognitive decline. The results revealed that increasing changes in social participation, social contact with friends, and social support from different social ties were associated with greater changes in cognitive and mental health, but not physical health, among frailer older adults compared to those who were more robust. This longitudinal study suggests that social support has a protective and compensatory role in enhancing mental health among frail older adults, but not among robust peers. Public health policies and interventions should focus on ameliorating social connectedness among physically frail older adults to enhance mental health outcomes. Future research studies could explore other risk factors that impact the relationships between social connectedness and health among older populations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".