ArticleBMC nursing2024
Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis.
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 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence and risk factors of burnout symptoms among nurses during the COVID-19 pandemic: an updated systematic review and meta-analysis.Human resources for health · 2025Pooled it
- Occupational stress, burnout, and depression: an exploration from a network analysis perspective.Psychological medicine · 2026Article
- Digital literacy, psychological resilience, and work stress among nurses: the moderating role of ward environment.Frontiers in psychology · 2026Article
- Implementing and evaluating a comprehensive simulation-based program to enhance nursing students' coping with death, burnout, and communication skills.Frontiers in psychology · 2026Article
- Trajectories of symptoms in the acute phase of COVID-19 infection and their association with anxiety and depression 2 years after infection.BMC psychiatry · 2025Article
- Network analysis of burnout, depression, and anxiety with occupational and personal outcomes among clinical nurses in China.Scientific reports · 2025Article
- Emotional symptom networks in ICU nurses: a comparative network analysis of tertiary-A and tertiary-B hospitals in China.BMC nursing · 2025Article
- Article
- Burnout among South African nurses during the peak of COVID-19 pandemic: a holistic investigation.BMC nursing · 2025Article
- Stress, Anxiety and Depressive Symptoms, Burnout and Insomnia Among Greek Nurses One Year After the End of the Pandemic: A Moderated Chain Mediation Model.Journal of clinical medicine · 2025Article
- Enhancing depression risk assessment in critical care nurses: a call for quantitative modeling.Critical care (London, England) · 2025Article
- Network of job demands-resources and depressive symptoms in critical care nurses: a nationwide cross-sectional study.Critical care (London, England) · 2025Article
- Elucidating the relationship between burnout and sleep disturbances among firefighters: a network analysis.BMC public health · 2025Article
- Resilience and yoga training have additive benefits for mental health of intensive care unit nurses.The South African journal of psychiatry : SAJP : the journal of the Society of Psychiatrists of South Africa · 2025Article
- Factors of suicide-related behaviors based on stress-vulnerability model and prevention strategies among nurses: a scoping review.Frontiers in psychology · 2025Review
- Relationship between burnout and turnover intention among nurses: a network analysis.BMC nursing · 2024Article
- Relationships Between Burnout and Neuroticism Among Emergency Department Nurses: A Network Analysis.Nursing open · 2024Review
- Multilevel Psychometric Analysis of Clinician Burnout Using Bayesian G-Theory and IRT Fusion Models.Inquiry : a journal of medical care organization, provision and financingArticle
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9 authors.
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Abstract
backgroundMental 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.
methodA 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.
resultsThe 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.
conclusionThe 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.
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