An examination of social support influences on participation for older adults with chronic health conditions
Bibliographic record
Abstract
UNLABELLED: Social support can improve participation in everyday activities among older adults with chronic health conditions, but the specific types of support that are needed are unclear. PURPOSE: This study examined the types of social support that most strongly predict participation in everyday activities. METHOD: Two hundred and twenty-seven participants completed a self-administered cross-sectional survey. The sample included adults aged 60 years or more with arthritis, diabetes, chronic obstructive pulmonary disease and/or heart disease. Participation was defined as satisfaction with participation in 11 life areas. Social support was defined as availability of tangible, affectionate, emotional/informational and positive social interaction support. RESULTS: Multiple regression analyses showed that participants who perceived greater tangible support and positive social interaction support had higher satisfaction with participation than participants with lower levels of these types of support. CONCLUSIONS: Targeting and developing tangible and social interaction support may help to facilitate satisfaction with participation for older adults with chronic conditions. Creating networks for companionship appears equally as important as providing support for daily living needs. Implications for Rehabilitation Varying types of social support can improve participation in older adults with chronic health conditions. Tangible support and positive social interaction support are the strongest predictors of participation. Creating networks for companionship may be equally as important as providing support for daily living needs.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".