How do individual, social, environmental, and resilience factors shape self-reported health among community-dwelling older adults: a qualitative case study
Bibliographic record
Abstract
BACKGROUND: While older adults are living longer, they often face health challenges, including living with multiple chronic conditions. How older adults respond and adapt to the challenges of multimorbidity to maintain health and wellness is of increasing research interest. Self-reported health, emerging as an important measure of health status, has broad clinical and research applications, and has been described as a predictor of future morbidity and mortality. However, there is limited understanding of how individual, social, and environmental factors, including those related to multimorbidity resilience, influence self-reported health among community-dwelling older adults (≥ 65 years). METHODS: Informed by the Lifecourse Model of Multimorbidity Resilience, this explanatory case study research explored older adults' perceptions of how these factors influence self-reported health. Data were generated through semi-structured telephone interviews with community-dwelling older adults. RESULTS: Fifteen older adults participated in this study. Four key themes, specific to how these older adults describe individual, social, environmental, and multimorbidity resilience factors as shaping their self-reported health, were identified: 1) health is a responsibility - "What I have to do"; 2) health is doing what you want to do despite health-related limitations - "I do what I want to do"; 3) the application and activation of personal strengths - "The way you think", and; 4) through comparison and learning from others - "Looking around at other people". These themes, while distinct, were found to be highly interconnected with recurring concepts such as independence, control, and psychological health and well-being, demonstrating the nuance and complexity of self-reported health. CONCLUSIONS: Findings from this study advance understanding of the factors that influence assessments of health among community-dwelling older adults. Self-reported health remains a highly predictive measure of future morbidity and mortality in this population, however, there is a need for future research to contribute additional understanding in order to shape policy and practice.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".