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Interconnected Mental Health Symptoms: Network Analysis of Depression, Anxiety, Stress, and Burnout Among Psychiatric Nurses in the Context of the COVID-19 Pandemic

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Specialty Psychiatry
Date 2024 Nov 12
PMID 39529901
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Abstract

Background: Mental health symptoms such as anxiety, depression, stress, and burnout are common among healthcare workers. However, the interconnections among them remain under-explored. This study aimed to address the interrelationships among these symptoms in psychiatric nurses.

Methods: We conducted a nationwide survey in the early stage of the COVID-19 pandemic (January to March 2021) to investigate the interconnectedness of depression, anxiety, stress, and burnout among psychiatric nurses. Using network analysis, we identified central symptoms, important bridge symptoms, and the correlations among these central symptoms.

Results: Of the 9,224 psychiatric nurses (79.2% female) included in the statistical analyses, 27.6% reported clinically significant depression, 31.2% anxiety, 14.5% stress, and 23.8% burnout. Network analysis revealed that stress had the highest expected influence (EI) value (0.920) and the highest strength among all nodes. The node for depression scored the highest in both closeness and betweenness. Emotional exhaustion (EE) had the highest bridge expected influence (BEI) of 0.340, with the strongest intergroup association between EE and depression. No significant differences were found in gender or frontline work experience (all p > 0.05).

Conclusions: Burnout, depression, anxiety, and stress are relatively common among psychiatric nurses in the context of the COVID-19 pandemic. While anxiety was the most prevalent, stress emerged as the core symptom, and depression as an important bridging node. Interventions targeting the core symptoms and bridging nodes may improve the mental health of psychiatric nurses.

Citing Articles

Stress, Anxiety and Depressive Symptoms, Burnout and Insomnia Among Greek Nurses One Year After the End of the Pandemic: A Moderated Chain Mediation Model.

Pachi A, Sikaras C, Melas D, Alikanioti S, Soultanis N, Ivanidou M J Clin Med. 2025; 14(4).

PMID: 40004676 PMC: 11856992. DOI: 10.3390/jcm14041145.

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