Self-Reported Health among Community-Dwelling Older Adults: A Multimethod Study to Understand the Complexity and Role of Adaptation to Health Adversity
Bibliographic record
Abstract
Self-reported health is typically captured as a response to the question, “In general, would you rate your health as excellent, very good, good, fair or poor?” Among community-dwelling older adults (≥65 years), self-reported health decreases as the number of chronic conditions increases. Despite this well-documented relationship, little is known about how other sociodemographic or health-related factors may shape this relationship, what may predict high self-reported health among this population, or how older adults perceive these factors as influencing their perceptions of health. Informed by the Lifecourse Model of Multimorbidity Resilience, the objective of this multimethod research study was to advance understanding of self-reported health among community-dwelling older adults. To this end, four research studies were completed: 1) scoping review of the factors associated with self-reported health, 2) cross-sectional analysis of baseline data from the Canadian Longitudinal Study on Aging to understand the relationship between multimorbidity and self-reported health and the factors that predict high self-reported health; 3) qualitative case study to explore the influence of individual, social, and environmental factors on self-reported health, including multimorbidity resilience, in community-dwelling older adults, and; 4) a multimethod study that brought together all findings in a matrix analysis. From this work, two meta-inferences were generated: 1) the factors that shape self-reported health are multidimensional and complex; and 2) adaptation to health adversity, resulting from experiences acquired over the lifecourse, shape how older adults perceive their health. Findings from this work advance three implications. First, there is a need to use and apply information gained by asking about self-reported health in clinical practice to inform care planning. Second, there is a need for whole person care to guide health and social care policy for older adults. Third, future health research must further explore longitudinal understanding of self-reported health as well as additional qualitative understanding of the differences of those older adults with the well-being paradox.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".