Evidence map›Paper›PMID 40841901›Full record

ArticleBMC nursing2025

Associations between perceived stress profiles, social connection and work engagement in clinical registered nurses: a mediation analysis and generalized additive models.

Yuan Liao, Miaochun Huang, Zhimin Gu, Chun Li, Yan Yu, Qimei Zhang, Xiangyu Lai, Jialin Liu, Kang He, Huiyun Chu and 5 more

Abstract read
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

15 authors.

Yuan LiaoAcademic Affairs Office, Guangzhou Universtiy of Chinese Medicine, Guangzhou, China.
Miaochun HuangThe Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
Zhimin GuSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Chun LiAcademic Affairs Office, Guangzhou Universtiy of Chinese Medicine, Guangzhou, China.
Yan YuSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Qimei ZhangThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Xiangyu LaiSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Jialin LiuSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Kang HeSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Huiyun ChuSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yao ZhaoSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Xinyu WuSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Lihua WuSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yu LiSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, China. hlly@gzucm.edu.cn.
Sujuan FangThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. 190657164@qq.com.

Funding

Guangdong Provincial Traditional Chinese Medicine Bureau Scientific Research Project 20252012Guangdong Undergraduate Colleges and Universities Teaching Quality and Teaching Reform Construction Project 2024-30-271
6 · The paper itself

Abstract

backgroundInvestigating the links between individual perceived stress, social connection, and work involvement plays a crucial role in enhancing the psychological health and overall well-being of clinical nurses, as well as elevating the quality of nursing care in clinical settings. This study incorporates the concepts of social connection and work engagement. By considering the potential heterogeneity of variables and applying a mediation model, we identified the influence pathways through which subgroups of perceived stress affect social connection and work engagement. This study offers a valuable reference for understanding the nurses' perceived stress profiles and improving their mental health.

methodThe research employed a cross-sectional study methodology. To select 600 clinical nurses from 3 hospitals in Guangzhou, a convenience sampling technique was implemented. Participants' general demographics, levels of work engagement, perceived stress, and social connections were gathered through questionnaires. Statistical analyses were performed utilizing latent profile examination, mediation analysis and generalized additive models.

results(1) The analysis revealed heterogeneity in stress levels among nurses, resulting in the identification of three distinct groups: low stress-high self-demand group (23.4%), high tension-low out-of-control group (57.5%), and high stress-low efficiency group (18.2%). (2) Clinical registered nurses that obtained support from their families were more inclined to be placed in the Low stress-high self-demand group. (3) Social connection significantly mediated the relationship between nurses' work engagement and perceived stress. (4) Work engagement demonstrated a non-linear relationship with both perceived stress and social connection.

conclusionThe social connections and work engagement of clinical nurses were notably affected by the stress on an individual. Nursing leaders should promptly identify the stress patterns of nurses, implement appropriate stress management strategies, enhance group cohesion through social networks, and promote the nurses' mental health and occupational well-being, which in turn can decrease nurse turnover rates and enhance the quality of clinical nursing care. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Clinical registered nursesGeneralized additive modelsLatent profile analysisPerceived stressSocial connectionWork engagement

Identifiers

PMID40841901
PMCPMC12372382

What OpenQuestion holds

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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.