MULTIMORBIDITY RESILIENCE IN COMMUNITY-RESIDING OLDER ADULTS: MEASUREMENT AND HEALTH OUTCOMES
Bibliographic record
Abstract
Abstract Multimorbidity is widespread, costly, and associated with a range of deleterious outcomes; it affects an estimated 67-80% of older adults. This study tests the validity of a multimorbidity resilience index developed in a Canadian sample of older adults by Wister et al., (2018), with a U.S.-based sample, using National Social Life, Health, and Aging Project (NSHAP) data, and draws upon the index to investigate the effects of resilience on outcomes over time. We mapped Wister et al.’s (2018) index to NSHAP measures, and assessed cross-sectional associations with health outcomes, using logistic regression. To assess the effects of resilience on health outcomes over time, we estimated mixed models of the relationships between resilience on outcomes over a 5-year interval. Total resilience was consistently associated with improved outcomes, including pain level (OR=.51, CI .41-.64); reduced utilization (OR=.45, CI .33-.60); improved mental health (OR=9.13, CI 6.20-13.44); self-rated physical health (OR=6.97, CI 4.76 10.19); and sleep quality (OR=3.66, CI 2.76-4.86). Longitudinal model results indicate change in multimorbidity resilience and number of chronic diseases predict (α=.001) pain level and self-rated physical health. Effects were moderated by socio-demographic factors. Our findings validate Wister et al.’s (2018) resilience index in a U.S. sample, supporting the importance of this measure to capture core components of older adults’ capacity to sustain well-being in the context of living with multiple, chronic conditions. Results from the longitudinal models provide beginning insights into the effects of resilience on symptom experience and perceived health over time, highlighting potential levers for change.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".