Community-Level Social Support to Mitigate the Impact of Combined Frailty and Multimorbidity on Psychological Distress Among Rural Chinese Older Adults During the COVID-19 Pandemic: Multilevel Modeling Study
Bibliographic record
Abstract
BACKGROUND: Accumulating research provides evidence that the psychological health of older people deteriorated from before to during the COVID-19 pandemic. Unlike robust individuals, coexisting frailty and multimorbidity expose older adults to more complicated and wide-ranging stressors. Community-level social support (CSS) is also an important impetus for age-friendly interventions, and it is 1 of the components of social capital that is seen as an ecological-level property. To date, we have not found research that examines whether CSS buffered the adverse impacts of combined frailty and multimorbidity on psychological distress in a rural setting during COVID-19 in China. OBJECTIVE: This study explores the combined effect of frailty and multimorbidity on psychological distress in rural Chinese older adults during the COVID-19 pandemic and examines whether CSS would buffer the aforementioned association. METHODS: Data used in this study were extracted from 2 waves of the Shandong Rural Elderly Health Cohort (SREHC), and the final analytic sample included 2785 respondents who participated in both baseline and follow-up surveys. Multilevel linear mixed effects models were used to quantify the strength of the longitudinal association between frailty and multimorbidity combinations and psychological distress using 2 waves of data for each participant, and then, cross-level interactions between CSS and combined frailty and multimorbidity were included to test whether CSS would buffer the adverse impact of coexisting frailty and multimorbidity on psychological distress. RESULTS: Frail older adults with multimorbidity reported the most psychological distress compared to individuals with only 1 or none of the conditions (β=.68, 95% CI 0.60-0.77, P<.001), and baseline coexisting frailty and multimorbidity predicted the most psychological distress during the COVID-19 pandemic (β=.32, 95% CI 0.22-0.43, P<.001). Further, CSS moderated the aforementioned association (β=-.16, 95% CI -0.23 to -0.09, P<.001), and increased CSS buffered the adverse effect of coexisting frailty and multimorbidity on psychological distress during the COVID-19 pandemic (β=-.11, 95% CI -0.22 to -0.01, P=.035). CONCLUSIONS: Our findings suggest that more public health and clinical attention should be paid to psychological distress among multimorbid older adults with frailty when facing public health emergencies. This research also suggests that community-level interventions prioritizing social support mechanisms, specifically improving the average levels of social support within communities, may be an effective approach to alleviate psychological distress for rural older adults who concurrently manifest frailty and multimorbidity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".