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Arch Gerontol Geriatr. 2017 Jan - Feb;68:161-167. doi: 10.1016/j.archger.2016.10.011. Epub 2016 Oct 26.

The resilience status of empty-nest elderly in a community: A latent class analysis.

Author information

1
XiangYa Nursing School of Central South University (Nursing Psychological Research Center of XiangYa Nursing School), Henan Provincial People's Hospital, China. Electronic address: zzuzhangjie@163.com.
2
XiangYa Nursing School of Central South University (Nursing Psychological Research Center of XiangYa Nursing School), Henan Provincial People's Hospital, China. Electronic address: jpzhang1965@163.com.
3
Henan Provincial People's Hospital, China. Electronic address: sycqm2010@126.com.
4
XiangYa Nursing School of Central South University (Nursing Psychological Research Center of XiangYa Nursing School), China. Electronic address: pt860315@163.com.
5
XiangYa Nursing School of Central South University (Nursing Psychological Research Center of XiangYa Nursing School), China. Electronic address: lishuwen528@163.com.
6
XiangYa Nursing School of Central South University (Nursing Psychological Research Center of XiangYa Nursing School), China. Electronic address: 280249740@qq.com.
7
XiangYa Nursing School of Central South University (Nursing Psychological Research Center of XiangYa Nursing School), China. Electronic address: 137891027@qq.com.

Abstract

OBJECTIVE:

The aim of this study was to examine the status and characteristics of resilience among empty-nest elderly in a community in China using exploratory latent class analysis (LCA).

METHODS:

This study enrolled 250 empty-nest elderly as the study respondents. General information regarding the resilience of empty-nest elderly was investigated using the General Information Questionnaire and Connor-Davidson Resilience Scale, Chinese version, and we then used LCA and multivariate logistic regression to discuss the characteristics of resilience among empty-nest elderly individuals.

RESULTS:

Through the analysis, we found that the resilience of empty-nest elderly had obvious group characteristics and that statistical indicators can support the three categories of potential model. On the basis of the conditional probability on the various items of the questionnaire in each category, they were named "high resilience group," "low pressure resilience group," and "low resilience group," and the proportion was 26.6%, 40.4%, and 32.9%, respectively. Further study showed that age, marital status, education level, relationship with children, and physical exercise had a significant effect on the high resilience group compared to the low resilience group. Gender, education level, relationship with children, and physical exercise had a significant effect on the low pressure resilience group compared to the low resilience group.

CONCLUSIONS:

The resilience status of empty-nest elderly in communities can be divided into three categories. Each category had different characteristics of demographic information.

KEYWORDS:

Empty-nest elderly; Latent class analysis; Resilience

PMID:
27810664
DOI:
10.1016/j.archger.2016.10.011
[Indexed for MEDLINE]

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