Evidence map›Paper›PMID 42363172›Full record

ArticleBMC nursing2026

Anxiety-depression symptom networks across emotion regulation profiles in Chinese nurses: a latent profile and network analysis.

Ping-Zhen Lin, Xu Wang, Yong-Sen Lin, Yan-Yan Lin, Lan-Lan Chen, Bi-Yu Wu

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Article in BMC nursing, 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ping-Zhen LinNursing Department, First Hospital of Quanzhou Affiliated to Fujian Medical University, No.1028 Anji Road, Chengdong Street, Fengze District, Quanzhou City, Fujian, 362000, P.R. China. lpz385675123@163.com.ORCID https://orcid.org/0009-0008-5497-6785
Xu WangFujian Key Laboratory of Oral Diseases & Fujian Provincial Engineering Research Center of Oral Biomaterial & Stomatological Key Lab of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian, P.R. China.
Yong-Sen LinDepartment of Neurology, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, P.R. China.
Yan-Yan LinDepartment of Infectious Disease, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, P.R. China.
Lan-Lan ChenNursing Department, First Hospital of Quanzhou Affiliated to Fujian Medical University, No.1028 Anji Road, Chengdong Street, Fengze District, Quanzhou City, Fujian, 362000, P.R. China.
Bi-Yu WuNursing Department, First Hospital of Quanzhou Affiliated to Fujian Medical University, No.1028 Anji Road, Chengdong Street, Fengze District, Quanzhou City, Fujian, 362000, P.R. China. wby202308@126.com.

Funding

Joint Funds for the Innovation of Science and Technology of Fujian Province 2025Y9495the Foundation for Cultivated Young Talents of Fujian Province, China 2023530820the Science and Technology Foundation of Quanzhou City 2023NS056
6 · The paper itself

Abstract

backgroundDepression and anxiety are highly prevalent among clinical nurses. This study employed a person-centered analytical approach to identify distinct emotion regulation (ER) profiles among Chinese clinical nurses and examined the structure of depressive and anxiety symptoms within each profile using latent profile analysis (LPA) complemented by network analysis (NA).

methodsA cross-sectional survey was conducted among clinical nurses in China. A total of 1,436 eligible nurses (mean age = 34.16, SD = 7.49; 93.5% female) participated. Participants completed the 10-item Emotion Regulation Questionnaire (ERQ), the 9-item Patient Health Questionnaire (PHQ-9), and the 7-item Generalized Anxiety Disorder scale (GAD-7). LPA was conducted using the two ERQ subscales, cognitive reappraisal and expressive suppression, to identify distinct ER profiles. NA was then applied to model the relationships among depressive and anxiety symptoms for the total sample and within each ER profile. Centrality (Expected Influence) and bridge centrality (Bridge Expected Influence) indices were calculated to identify core and bridging symptoms. Network accuracy and stability were assessed via bootstrap methods, and network structures were compared across profiles using the Network Comparison Test (NCT).

resultsLPA identified five distinct ER profiles: Low Regulation (7.2%), Below-Average Regulation (44.9%), Flexible Regulation (8.6%), Above-Average Regulation (32.0%), and High Regulation (7.2%). The Flexible Regulation group, characterized by high cognitive reappraisal and low expressive suppression, exhibited the lowest levels of depression and anxiety, whereas the Below-Average and Above-Average groups reported the highest symptom severity. NA revealed "Uncontrollable worry" (GAD6) and "Restlessness" (GAD5) as core and bridge symptoms across multiple profiles, while "Sad mood" (PHQ2), "Motor disturbance" (PHQ8), and "Suicidal ideation" (PHQ9) emerged as profile-specific central and bridge symptoms in the Low and Flexible Regulation groups. NCT indicated significant differences in global network structure across several profiles, yet global strength did not differ.

conclusionsThis study reveals heterogeneous ER patterns among clinical nurses and demonstrates that the Flexible Regulation group is the most adaptive configuration. Key central and bridge symptoms differ across profiles, offering empirically grounded targets for profile-informed interventions. Regular mental health screening with attention to profile-specific symptom patterns is recommended to support tailored prevention strategies and improve overall well-being among clinical nurses. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

AnxietyClinical nursesDepressionEmotion regulationLatent profile analysisNetwork analysis

Identifiers

PMID42363172
PMCPMC13576284

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