Evidence map›Paper›PMID 41188972›Full record

ArticleBMC psychology2025

Depressive symptoms among migrant Chinese grandparents and intergenerational family conflict: a moderated mediation model.

Huili Su, Yujin Chao, Zhiqing Wang

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Article in BMC psychology, 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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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Huili SuSchool of Law and Politics, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
Yujin ChaoSchool of Media, Film and Journalism, Monash University, Caulfield, VIC, 3145, Australia.
Zhiqing WangSchool of Public Policy & Management, Tsinghua University, Haidian District, Beijing, 100084, China. wangzhiqing1993@mail.tsinghua.edu.cn.

Funding

General Project of Philosophy and Social Science Planning in Henan Province 2021BSH021Humanities and Social Sciences Research Project of Henan Provincial Department of Education 2022-ZZJH-090
6 · The paper itself

Abstract

backgroundAmid China's rapid urbanization and demographic aging, the population of migrant grandparents who relocate to cities to provide intergenerational care has been steadily expanding. This group is particularly vulnerable to depressive symptoms due to the accumulation of multiple stressors, and they face an elevated risk of intergenerational family conflict. This study aims to examine the relationship between depression among migrant grandparents and intergenerational conflict within families, with a particular focus on the mediating role of hostile attribution bias and the moderating role of collective family efficacy.

methodsUsing the Short Form of the Center for Epidemiological Studies Depression Scale (CES-D), the Collective Family Efficacy Questionnaire, the Hostile Attribution Bias Scale, and the Multidimensional Intergenerational Relationship Quality Scale, a questionnaire survey was conducted among 432 representative migrant grandparents in Zhengzhou, China, between March and May 2023. Data analysis included descriptive statistics, correlation analysis, and moderated mediation modeling.

results(1) Depressive symptoms were found to be positively and significantly associated with intergenerational family conflict (β = 0.336, 95%CI[0.253,0.418]). Hostile attribution bias demonstrated a significant mediating effect in the relationship between depressive symptoms and intergenerational family conflict (β = 0.175, 95%CI[0.097,0.257]). (2) Furthermore, collective family efficacy moderated the mediating role of hostile attribution bias. When collective family efficacy was at a low level, the mediating pathway of hostile attribution bias was significant (β = 0.486, 95%CI[0.374,0.597]); when collective family efficacy was at a high level, the mediating pathway was no longer significant (β = 0.058, 95%CI[-0.063,0.179]).

conclusionWithin the co-residential, multi-generational caregiving arrangements involving migrant grandparents in China, hostile attribution bias mediates the relationship between depressive symptoms and intergenerational family conflict, while collective family efficacy moderates the mediating pathway of hostile attribution bias on intergenerational family conflict. The findings provide a theoretical basis for designing targeted preventive interventions. Future interventions are recommended to focus on reducing hostile attribution bias and enhancing collective family efficacy to mitigate the negative relationship between depressive symptoms and intergenerational family conflict, thereby promoting the intergenerational well-being of migrant grandparents.

Indexed as

DepressionFamily ConflictGrandparentsIntergenerational RelationsTransients and MigrantsAdultAgedChinaEast Asian PeopleFemaleHostilityHumansMaleMediation AnalysisMiddle AgedCollective family efficacyDepressionHostile attribution biasIntergenerational family conflictMigrant grandparents

Identifiers

PMID41188972
PMCPMC12584487

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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.