Evidence map›Paper›PMID 42421715›Full record

ArticleFrontiers in psychology2026

Beyond individual traits: differential associations of social support profiles with resilience in older learners.

Qian Gao, Hui Zhou, Hang Lu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Qian GaoSchool of Urban Management, Beijing Open University, Beijing, China.
Hui ZhouAcademic Affairs Office, Beijing Open University, Beijing, China.
Hang LuSchool of Education, Zhejiang Normal University, Jinhua, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lifelong learning is a vital pathway for fostering social integration and psychological well-being among older adults. This study examines the differential associations of social support and challenge patterns with resilience in older learners from a social-ecological perspective. Moving beyond the traditional focus on individual demographic traits, this study identifies multidimensional environmental factors-including family, school, and community support, as well as perceived learning barriers-to construct a Support-Challenge ecological analysis framework. Methods: K-means cluster analysis and robust Analysis of Variance (ANOVA) were employed to empirically analyze survey data collected from 1,783 older learners across 26 educational and residential learning sites in China. Results: Four distinct social support profiles among older learners were identified: Isolated-Comfortable, Balanced, Greenhouse-Empowered, and Supported-Challenged. Ecological configurations demonstrated significantly greater explanatory power for resilience than individual demographic characteristics. Notably, the Supported-Challenged group exhibited the highest levels of resilience, whereas the Isolated-Comfortable group demonstrated the lowest. Conclusion: These findings suggest that challenge and support synergistically influence the development of resilience in later life. Additionally, gender and education level serve as key factors predicting membership in different social support profiles. By integrating the dual dimensions of support and challenge, this study reveals the optimal ecological niche for resilience development, offering practical pathways for constructing empowering older adult learning ecosystems.

Indexed as

active agingK-means cluster analysisolder learnersresiliencesocial support systems

Identifiers

PMID42421715
PMCPMC13341973

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.