Evidence map›Paper›PMID 40337728›Full record

ArticleFrontiers in public health2025

The hidden network: community sense, social desirability, and their protective influence on negative emotions in aging populations.

Ying Chu, Yanhong Liu, Xiaotong Qu, Xiaoxiang Wang

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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

4 authors.

Ying ChuDepartment of Marxism Sinicization, School of Marxism, Liaoning Normal University, Dalian, China.
Yanhong LiuDepartment of Nursing, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Xiaotong QuDepartment of Nursing, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Xiaoxiang WangDepartment of Marxism Sinicization, School of Marxism, Liaoning Normal University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With aging populations, understanding mechanisms linking social factors to emotional well-being in the older adult is critical. This study examines how social capital, social support, community sense, and social desirability interact to influence negative emotions. Methods: A cross-sectional survey of 904 older adult individuals in Liaoning, China, utilized standardized scales: GDS-10 (negative emotions), SSRS (social support), BSCS (community sense), and MCSDS (social desirability). Mediation and moderation analyses were conducted. Results: The results demonstrated significant negative correlations between social capital/social support and negative emotions. Social support was identified as a mediating factor linking social capital to reduced negative emotions. Additionally, community sense and social desirability moderated the relationship between social support and negative emotions, with stronger community belonging and higher social desirability amplifying the protective effect of social support. Conclusion: These findings underscore the importance of fostering social capital, strengthening social support networks, cultivating community integration, and addressing social desirability biases to alleviate negative emotions in the older adult. The study provides actionable insights for designing targeted mental health interventions aimed at improving emotional well-being in aging populations.

Indexed as

AgingEmotionsSocial CapitalSocial DesirabilitySocial SupportAgedAged, 80 and overChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnairescommunity sensenegative emotionsolder adultsocial capitalsocial desirabilitysocial support

Identifiers

PMID40337728
PMCPMC12055499

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.