Evidence map›Paper›PMID 39609779›Full record

ArticleBMC nursing2024

Association between interpersonal sensitivity and loneliness in college nursing students based on a network approach.

Jiukai Zhao, Yibo Wu, Jie Yuan, Juanxia Miao, Xue Wang, Yu Yang, Shuang Zang

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
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4citing papers 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 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jiukai Zhao *Department of Community Nursing, School of Nursing, China Medical University, Shenyang, Liaoning Province, China.
Yibo Wu *School of Public Health, Peking University, Beijing, China.
Jie YuanJitang College of North China University of Science and Technology, Tangshan City, Hebei Province, China.
Juanxia MiaoDepartment of Community Nursing, School of Nursing, China Medical University, Shenyang, Liaoning Province, China.
Xue WangDepartment of Community Nursing, School of Nursing, China Medical University, Shenyang, Liaoning Province, China.
Yu YangDepartment of Vascular Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China. xgjzxyy@163.com.
Shuang ZangDepartment of Community Nursing, School of Nursing, China Medical University, Shenyang, Liaoning Province, China. zangshuang@cmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe symptoms of interpersonal sensitivity and loneliness are prevalent among college nursing students. This study aims to investigate the interactions at the symptom level and elucidate the characteristics of the interpersonal sensitivity and loneliness symptoms network among Chinese college nursing students.

methodA cohort of 864 college nursing students participated in the study. Interpersonal sensitivity was assessed using the Chinese Version of the Short Form of the Interpersonal Sensitivity Measure (IPSM-CS), while loneliness symptoms were evaluated using the three-item Loneliness Scale (T-ILS). Central symptoms and bridge symptoms were determined using centrality and bridge centrality indices, respectively. The stability of the network was evaluated through the case-dropping procedure.

resultsThe most robust direct relationship was observed between the interpersonal sensitivity symptoms 'Feel happy when someone compliments' (IPSM-CS9) and 'Make others happy' (IPSM-CS10). 'Feel happy when someone compliments' (IPSM-CS9) exhibited the highest node strength in the interpersonal sensitivity and loneliness network, with 'They would not like me' (IPSM-CS2) following closely behind. Among the loneliness symptoms, 'Relational loneliness' (T-ILS1) demonstrated the highest bridge strength, followed by 'Intimate loneliness' (T-ILS3) and 'Social loneliness' (T-ILS2). The entire network displayed robustness in both stability and accuracy assessments.

conclusionThis study emphasized the importance of central symptoms (e.g., 'Feel happy when someone compliments' and 'They would not like me') and bridge symptoms (e.g., 'Relational loneliness', 'Intimate loneliness', and 'Social loneliness'). Intervening in the central symptoms may effectively enhance the self-confidence of nursing students and foster harmonious relationships with others, thereby facilitating better adaptation to interpersonal relationships. Furthermore, by addressing bridge symptoms (e.g., meeting the need for approval and providing social support), nursing students can better adjust to their studies and practice with a more positive attitude during their college years.

Indexed as

A network approachInterpersonal sensitivityLonelinessNursing students

Identifiers

PMID39609779
PMCPMC11605870

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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.