Abstract summary 

Mental health problems are critical and common in medical staff working in Intensive Care Units (ICU) even at the late stage of COVID-19, particularly for nurses. There is little research to explore the inner relationships between common syndromes, such as depression and burnout. Network analysis (NA) was a novel approach to quantified the correlations between mental variables from the perspective of mathematics. This study was to investigate the interactions between burnout and depression symptoms through NA among ICU nurses.A cross-sectional study with a total of 616 Chinese nurses in ICU were carried out by convenience sampling from December 19, 2022 to January19, 2023 via online survey. Burnout symptoms were measured by Maslach Burnout Inventory-General Survey (MBI-GS) (Chinese version), and depressive symptoms were assessed by the 9-item Patient Health Questionnaire (PHQ-9). NA was applied to build interactions between burnout and depression symptoms. We identified central and bridge symptoms by R package qgraph in the network model. R package bootnet was used to examined the stability of network structure.The prevalence of burnout and depressive symptoms were 48.2% and 64.1%, respectively. Within depression-burnout network, PHQ4(Fatigue)-MBI2(Used up) and PHQ4(Fatigue)-MBI5(Breakdown) showed stronger associations. MBI2(Used up) had the strongest expected influence central symptoms, followed by MBI4(Stressed) and MBI7 (Less enthusiastic). For bridge symptoms. PHQ4(Fatigue), MBI5(Breakdown) and MBI2(Used up) weighed highest. Both correlation stability coefficients of central and bridge symptoms in the network structure were 0.68, showing a high excellent level of stability.The symptom of PHQ4(Fatigue) was the bridge to connect the emotion exhaustion and depression. Targeting this symptom will be effective to detect mental disorders and relieve mental syndromes of ICU nurses at the late stage of COVID-19 pandemic.

Authors & Co-authors:  Zhang Wu Ma Liu Shen Sun Ma Hu Lang

Study Outcome 

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