Life Course Risk and Protective Factors of Multimorbidity Resilience Among Older Adults in Rural China: A Longitudinal Study in Anhui Province Before and During COVID-19
Bibliographic record
Abstract
OBJECTIVES: Multimorbidity resilience reflects older adults' ability to cope with, adapt to, and rebound from its adverse effects through mobilizing resources. This study revised the multidomain Multimorbidity Resilience Index based on the Lifecourse Model of Multimorbidity Resilience referring to the life situations of older adults in rural China to measure the multimorbidity resilience from 2018 to 2021 and to explore factors influencing multimorbidity resilience from the perspective of Life Course theory. METHODS: This study used the seventh and eighth waves of longitudinal data (2018-2021) collected in Anhui, China. Older adults (945) with 2 or more chronic diseases were selected, and 1,201 (person-year) observations were collected and studied. A mixed linear model examined the effects of early- and later-factors on multimorbidity resilience. RESULTS: Multimorbidity resilience was negatively correlated with age and decreased faster with age after the outbreak of the coronavirus disease-2019 (COVID-19) pandemic. Married older adults have higher multimorbidity resilience. Exposure to hunger was associated with lower multimorbidity resilience when later factors were considered. Self-reported health before age 15, access to medical resources, and multimorbidity resilience were positively correlated. In addition, this study verified the relationship between multimorbidity resilience and the number of chronic diseases, exercise frequency, religious beliefs, self-reported health, and economic satisfaction, among other factors. DISCUSSION: The associations between life course factors and multimorbidity resilience emphasize the long-term impact of early-life experience and the adverse effects of increasing age, especially after the outbreak of the COVID-19 pandemic. The findings will drive policy development from a life course perspective encompassing prevention and follow-up treatment to promote active aging.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".