Evidence map›Paper›PMID 41688926›Full record

ArticleBMC geriatrics2026

What stole Chinese older adults' life satisfaction? Integrating medical statistics and machine learning with a life-course perspective using CHARLS data.

Jing Zhao, Yaya Wang, XiaoFei Du, ShaoPeng Wang, Jun Lin

Abstract read
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jing Zhao *Universiti Putra Malaysia, Serdang, Selangor, Malaysia. zhaojing09@126.com.
Yaya Wang *The Open University of China, Beijing, China.
XiaoFei DuTangshan Vocational and Technical College, Tangshan, Hebei Province, China.
ShaoPeng WangUniversiti Putra Malaysia, Serdang, Selangor, Malaysia.
Jun LinHaiyang Municipal of Statistical Bureau, Yantai, Shandong Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs China enters a rapidly aging era, understanding the determinants of life satisfaction among older adults has become a pressing research and policy concern. Life satisfaction in later life is shaped not only by current psychosocial and material conditions but also by accumulated experiences across the life course. Despite increasing attention to healthy aging, few studies have systematically examined how these factors jointly influence life satisfaction.

methodsUsing data from 4,627 individuals aged 60 and above drawn from the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS) merged with the 2014 Life History Survey, this study applied a stage-specific and integrated analytical framework. Generalized Ordered Logit Regression (GOLR) was combined with the XGBoost machine learning algorithm to capture both linear and nonlinear associations, as well as perform individual-level prediction of life satisfaction. This mixed-method approach enabled identification of key determinants across satisfaction levels while allowing for variable interactions and heterogeneity analysis.

resultsThe results show factors' heterogeneity across satisfaction levels. At lower levels, present-day psychosocial and material conditions including depressive symptoms, self-rated health, intergenerational relationships, and access to utilities play dominant roles. As satisfaction level increases, early-life factors become increasingly important. Two variables, satisfaction with children relationship and depressive symptoms, consistently influence life satisfaction across all stages. The transition to very satisfaction involves all five domains of predictors, while the highest level of satisfaction is explained by a smaller group of enduring psychological and intergenerational factors.

conclusionThis study provides an integrated and stage-specific understanding of life satisfaction among older Chinese adults. The findings deepen the theoretical understanding of life course influences, social support, and psychological resilience in later life. The study also suggests the need for tailored policy interventions that prioritize mental health care, social engagement, and basic living conditions at lower satisfaction levels, and incorporate early-life experiences and emotional connectedness for those with higher satisfaction.

Indexed as

AgingBoosting Machine Learning AlgorithmsPersonal SatisfactionAgedAged, 80 and overChinaEast Asian PeopleFemaleHumansLongitudinal StudiesMaleMiddle AgedDeterminantsGOLRLife course perspectiveLife satisfactionOlder adultsStage-specific analysisXGBoost algorithm

Identifiers

PMID41688926
PMCPMC13011550

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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.