Evidence map›Paper›PMID 38087241›Full record

ArticleBMC geriatrics2023

Potential factors associated with resilience among older adults in rural China: a multilevel analysis.

Yun Qiu, Zhen Cong, Xiaoxuan Wang, Shuzhuo Li

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Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yun QiuSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Zhen CongSchool of Public Health, The University of Alabama at Birmingham, Birmingham, AL, United States of America.
Xiaoxuan WangSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Shuzhuo LiCenter for Aging and Health Research, School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, Shaanxi, China. shzhli@xjtu.edu.cn.

Funding

National Natural Science Foundation of China 72074177
6 · The paper itself

Abstract

backgroundResilience is crucial for older adults who experience adversities, but research on the issue in rural China remains limited. This study aims to examine factors associated with resilience among older adults in rural China, as related to different types of resilience, and under different levels of adversity.

methodsData were taken from the eight-wave (2001-2021) Longitudinal Study of Older Adults in Anhui Province, China. We used data from the eighth wave (2021) for the outcome variables and lagged predictors (2018) to avoid reverse causal effects. The study sample included individuals 60 years and above, excluding new participants from 2021, those without any adverse events, and any respondents with incomplete analytic data. Resilience was operationalized as residuals of the regressions of life satisfaction (Life Satisfaction Scale) and depressive symptoms (CES-D) on adversity, referred to as Type-1 and Type-2 resilience respectively. These two types of resilience were then treated as the outcome variables in subsequent multilevel regressions, with the predictors focusing on individual, social, and environmental characteristics and resources. This study adheres to STROBE guidelines.

results43% of rural older adults exhibited both Type-1 and Type-2 resilience, whereas 18% exhibited only Type-1 resilience and 7% exhibited only Type-2 resilience. Common factors associated with both types of resilience included self-rated health, satisfaction with one's own financial situation, and the prestigiousness of social networks. Predictors for higher levels of Type-1 resilience included higher levels of financial and emotional support and more options for places of leisure. Predictors for higher levels of Type-2 resilience included greater access to medical care. The prestigiousness of social networks, higher levels of emotional support and instrumental support, access to medical care, and more options of places of leisure were positively associated with resilience in the low-adversity group (first tertile of adversity), only satisfaction with financial situation was positively correlated with the resilience of the middle-adversity group (second tertile), while better self-rated health, satisfaction with financial situation, and financial support yielded greater resilience in the high-adversity group (third tertile).

conclusionsWe examined two types of resilience among older adults in rural China, and found that they have shared and unique associated factors. In addition, the potential factors influencing resilience varied with the level of adversity.

Indexed as

Resilience, PsychologicalAgedChinaHumansLongitudinal StudiesMultilevel AnalysisRural PopulationSocial SupportResidual approachResilienceRural older adults

Identifiers

PMID38087241
PMCPMC10717782

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.