Evidence map›Paper›PMID 42342997›Full record

ArticleScientific reports2026

Latent profiles of social connectedness and associated factors in maintenance hemodialysis patients.

Yun Zhang, Zhiyan Sun, Naiyue Ye, Xun Zhou, Menghan Zheng, Peili Xu

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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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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

6 authors.

Yun ZhangSchool of Nursing, Anhui University of Chinese Medicine, Hefei, Anhui Province, 230012, China.
Zhiyan SunSchool of Nursing, Anhui University of Chinese Medicine, Hefei, Anhui Province, 230012, China.
Naiyue YeHemodialysis Unit, The First People's Hospital of Hefei, Hefei, Anhui Province, 231220, China.
Xun ZhouSchool of Nursing, Wannan Medical University, Wuhu, 241002, China.
Menghan ZhengSchool of Nursing, Wannan Medical University, Wuhu, 241002, China.
Peili XuNursing Department, The First People's Hospital of Hefei, Hefei, Anhui Province, 231220, China. peiliAHUCM2022@126.com.

Funding

Anhui Provincial Association of Traditional Chinese Medicine Research Project 2024ZYYXH133
6 · The paper itself

Abstract

To date, no study has characterized the heterogeneity of social connectedness among patients undergoing maintenance hemodialysis (MHD). This study used latent profile analysis (LPA) to identify distinct latent profiles of social connectedness in this population and to explore the factors associated with different profiles. This cross-sectional study enrolled 365 patients undergoing MHD at a tertiary hospital in Anhui Province, China, from September 2025 to March 2026. Data were collected using a demographic questionnaire, the Social Connectedness Scale-Revised (SCS-R), the Social Support Rating Scale (SSRS), and the 10-item Connor-Davidson Resilience Scale (CD-RISC-10). Data analysis was performed using SPSS 26.0 and Mplus 8.3. A total of 345 valid questionnaires were collected. The response rate was 94.52% (345/365). We identified three potential profile categories: low social connectedness (n = 110, 31.9%), moderate social connectedness (n = 146, 42.3%), and high social connectedness (n = 89, 25.8%). Multivariate logistic regression analysis identified educational level, employment status, dialysis shift, types of chronic diseases, resilience, and social support as factors significantly associated with these three profiles (P < 0.05). The findings indicate that social connectedness among MHD patients exhibits significant heterogeneity across subgroups and is associated with a variety of factors. Healthcare professionals should identify patients' social connectedness profiles early and implement targeted interventions to improve their social connectedness.

Indexed as

Renal DialysisSocial SupportAdultAgedChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedResilience, PsychologicalSurveys and QuestionnairesLatent profile analysisMaintenance hemodialysisSocial connectednessSocial ecosystem theory

Identifiers

PMID42342997
PMCPMC13487171

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