Dynamics of Multimorbidity Resilience and Health Outcomes Over Time in Community-Residing Older Adults
Bibliographic record
Abstract
Abstract Multimorbidity resilience may mitigate the adverse effects of multiple chronic diseases on older adults’ health. Wister et al.’s (2018) multimorbidity resilience index was developed and tested in a cross-sectional sample of older adults in Canada. Building on these findings, we examined the reciprocal relationships of resilience on outcomes to test these potentially mitigating effects in a community-based, U.S. sample of older adults over time. The study sample includes 1,054 older adults from waves 2 and 3 of the National Social Life, Health, and Aging Project (NSHAP) study (Waite et al 2020). Wister et al.’s (2018) index was mapped to NSHAP measures, and reciprocal relationships of multimorbidity resilience and health outcomes over a 5-year period was tested using structural equation modeling (SEM). Results indicated significant effects of multimorbidity resilience on self-rated physical health and pain. Interestingly, a better functional resilience at baseline conferred better self-rated physical health at follow-up, while better psychological resilience predicted lower pain level. By contrast, the influence of health outcomes on any domain of multimorbidity resilience was not detectable at all, supporting the direction of these associations from resilience to outcomes. The study systematically investigated the dynamic hypotheses between multimorbidity resilience and health outcomes. That is, whether they are determinants or consequences, or both. Our findings suggest multimorbidity resilience predicts subsequent 5-year change in health outcomes, especially self-rated physical health and pain level, but not vice versa, strengthening the evidence of the importance of resilience in the health of older adults.
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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 | 0.007 |
| 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 | 0.000 | 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".