Evidence map›Paper›PMID 41257685›Full record

ArticleBMC public health2025

Exploring the state and drivers of community integration for older adults in China: insights from a social ecosystem perspective.

Jia Wang, Wansha Zhou, Lei Xu, Xiaohui Xie, Yuanfang Wang, Xinglan Qin, Lisha Zheng, Lianhong Wang

Abstract read
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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jia WangNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Wansha ZhouNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Lei XuNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Xiaohui XieNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Yuanfang WangNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Xinglan QinNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Lisha ZhengNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China.
Lianhong WangNursing Department, Affiliated Hospital of Zunyi Medical University, ZunYi, Guizhou, China. 2726912415@qq.com.

Funding

Guizhou Provincial Science and Technology Projects Guizhou Composite Support [2023] General 263
6 · The paper itself

Abstract

backgroundCommunity integration is vital for healthy aging by fostering social participation. This study aims to investigate the current status of and influencing factors on community integration among older adults in China, to fill the research gap in this area, and to provide empirical evidence for the development of healthy aging policies in China and other countries facing the challenges of aging.

methodsA cross-sectional survey was conducted between May and October 2024 in Guizhou Province using a two-stage probability proportional to size sampling design. Data from 3,940 valid community-dwelling older adults aged ≥ 60 years were collected. Descriptive statistics, t-tests, and one-way ANOVA were used to examine differences in community integration scores, and hierarchical linear regression was applied to explore the effects of micro-, meso-, and macro-level factors within the social-ecosystem framework.

resultsThe average community integration score was 17.98 (SD = 6.17). Univariate analyses identified 19 variables significantly associated with community integration. Hierarchical regression showed that micro-level factors (e.g., age, gender, personal income, health status, ADL) explained the largest proportion of variance (18.2%), while adding meso-level factors (e.g., marital status, number of children, living alone, community exercise facilities) increased the explained variance to 20.6%. The inclusion of macro-level contextual variables (e.g., social insurance coverage rate, proportion of residents with higher education) raised it slightly to 21.1%, indicating that interpersonal and community resources exert stronger effects than broader structural conditions.

conclusionsCommunity integration among older adults in China remains low and is shaped by multilevel factors. Targeted interventions should integrate individual support, family involvement, and community resources to enhance participation and promote the development of age-friendly and health-promoting communities.

Indexed as

Community IntegrationAgedAged, 80 and overChinaCross-Sectional StudiesFemaleHumansIndependent LivingMaleMiddle AgedSocial ParticipationSurveys and QuestionnairesCommunity integrationCross-sectional surveyHealthy agingHierarchical linear regression analysisOlder adultsSocial ecosystem perspectiveSocial participation

Identifiers

PMID41257685
PMCPMC12629004

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LicenceCC BY-NC-ND
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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.