Keywords
Occupational health
Psychometrics
Burnout, psychological
How to Cite
Abstract
Objective: To analyze the network structure corresponding to burnout symptoms in Brazilian nursing workers. Method: A cross-sectional study with non-probability sampling. The sample included 3,594 nurses, nursing technicians and nursing assistants and the data collection was performed remotely. The burnout symptoms were assessed with the Burnout Assessment Tool – General Version. The data were analyzed by means of the network analysis technique, using the Least Absolute Shrinkage and Selection Operator (LASSO) partial correlation and regression method. Centrality of the nodes was estimated through strength, closeness and betweenness indicators and network stability was assessed by means of the correlation stability coefficient. Results: The centrality strength analysis identified “I feel tense and stressed” and “I have trouble concentrating” as core symptoms. The betweenness and closeness indices evidenced “I feel anxious and/or suffer from panic attacks” and “I feel tense and stressed” as the most relevant symptoms. The stability coefficient confirmed the network structure. Conclusion: The network analysis allowed identifying the main burnout symptoms among Brazilian nursing workers and revealed strategic targets to devise mental health promotion actions for these professionals.
References
Borsboom D, Deserno MK, Rhemtulla M, Epskamp S, Fried EI, McNally RJ, et al. Network analysis of multivariate data in psychological science. Nat Rev Methods Primers. 2021;1(1):58. https://doi.org/10.1038/s43586-021-00055-w
Samra R, França AB, Lucassen MFG, Waterhouse P. A network approach to understanding distance learners’ experience of stress and mental distress whilst studying. Int J Educ Technol High Educ. 2023;20(1):27. https://doi.org/10.1186/s41239-023-00397-3. PMID: 37214594.
Gijzen MWM, Rasing SPA, Creemers DHM, Smit F, Engels RCME, De Beurs D. Suicide ideation as a symptom of adolescent depression. a network analysis. J Affect Disord. 2021;278:68–77. https://doi.org/10.1016/j.jad.2020.09.029. PMID: 32956963.
Machado WL, Cunha RD, Vissoci JRN. Análise de rede de variáveis psicológicas: estimação, acurácia, estabilidade e preditabilidade. In: Faiad C, Baptista MN, Primi R, organizadores. Tutoriais em análise de dados aplicados à psicometria. Petrópolis: Editora Vozes; 2021. p. 400–20.
Schaufeli WB, De Witte H, Desart S. Burnout Assessment Tool (BAT): test manual [Internet]. Leuven: KU Leuven; 2020 [citado 2025 Nov 27]. Disponível em: https://burnoutassessmenttool.be/wp-content/uploads/2020/08/Test-Manual-BAT-English-version-2.0-1.pdf
Schaufeli WB, Desart S, De Witte H. Burnout Assessment Tool (BAT): Development, Validity, and Reliability. Int J Environ Res Public Health. 2020;17(24):9495. https://doi.org/10.3390/ijerph17249495. PMID: 33352940.
Santin Júnior LJ, Martins BG, Campos JADB, Vazquez ACS, Marziale MHP, Mendes IAC, et al. Psychometric properties of the Burnout Assessment Tool - General version in nursing workers. Rev Lat Am Enfermagem. 2025;33:e4425. https://doi.org/10.1590/1518-8345.7367.4426. PMID: 39907387.
Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23(4):617–34. https://doi.org/10.1037/met0000167. PMID: 29595293.
Koller D, Friedman N. Probabilistic graphical models: principles and techniques. Cambridge: MIT Press; 2009.
Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9(3):432–41. https://doi.org/10.1093/biostatistics/kxm045. PMID: 18079126.
Foygel R, Drton M. Extended Bayesian Information Criteria for Gaussian Graphical Models. In: Lafferty J, Williams C, Shawe-Taylor J, Zemel R, Culotta A, editors. Proceedings of 24th Annual Conference on Neural Information Processing Systems 2010 [Internet]; 6-9 Dec 2010; Vancouver, Canada. San Diego (CA): NeurIPS; 2010 [citado 2025 Abr 20]. Disponível em: https://arxiv.org/abs/1011.6640v1
Hansen D, Shneiderman B, Smith MA. Analyzing Social Media Networks with NodeXL: Insights from a Connected World by Derek Hansen, Ben Shneiderman, and Marc A. Smith. Int J Hum Comput Interact. 2011;27(4):405–8. https://doi.org/10.1080/10447318.2011.544971
Organização Pan-Americana da Saúde. Enfermagem na Região das Américas [Internet]. Washington: OPAS; 2025 [citado 2025 Dez 15]. Disponível em: https://iris.paho.org/items/c0defc15-9cc4-4c0a-887a-a075a0e4de0d
Galanis P, Vraka I, Fragkou D, Bilali A, Kaitelidou D. Nurses’ burnout and associated risk factors during the COVID-19 pandemic: A systematic review and meta-analysis. J Adv Nurs. 2021;77(8):3286–302. https://doi.org/10.1111/jan.14839. PMID: 33764561.
Macedo MJ de A, Freitas CPP de, Bermudez MB, Souza Vazquez AC, Salum GA, Dreher CB. The shared and dissociable aspects of burnout, depression, anxiety, and irritability in health professionals during COVID-19 pandemic: A latent and network analysis. J Psychiatr Res. 2023;166:40–8. https://doi.org/10.1016/j.jpsychires.2023.09.005. PMID: 37738779.
Desart S, De Witte H. Burnout 2.0 - A New Look at the Conceptualization of Burnout. In: Taris T, Peeters M, De Witte H, organizers. The Fun and Frustration of Modern Working Life Contributions from an occupational health psychology perspective Festschrift for Prof dr Wilmar Schaufeli. Kalmthout: Pelckmans Pro; 2019. p. 140–52.
Demerouti E, Bakker AB, Nachreiner F, Schaufeli WB. The job demands-resources model of burnout. J Appl Psychol. 2001;86(3):499–512. https://doi.org/10.1037/0021-9010.86.3.499. PMID: 11419809.
Bakker AB, Demerouti E, Sanz-Vergel A. Job Demands–Resources Theory: Ten Years Later. Annu Rev Organ Psychol Organ Behav. 2023;10:25–53. https://doi.org/10.1146/annurev-orgpsych-120920-053933
Dall’Ora C, Ejebu OZ, Ball J, Griffiths P. Shift work characteristics and burnout among nurses: cross-sectional survey. Occup Med (Chic Ill). 2023;73(4):199–204. https://doi.org/10.1093/occmed/kqad046. PMID: 37130349.
Buckley L, McGillis Hall L, Price S, Visekruna S, McTavish C. Nurse retention in peri- and post-COVID-19 work environments: a scoping review of factors, strategies and interventions. BMJ Open. 2025;15(3):e096333. https://doi.org/10.1136/bmjopen-2024-096333. PMID: 40037671.
Getie A, Ayenew T, Amlak BT, Gedfew M, Edmealem A, Kebede WM. Global prevalence and contributing factors of nurse burnout: an umbrella review of systematic review and meta-analysis. BMC Nurs. 2025; 24(1):596. https://doi.org/10.1186/s12912-025-03266-8. PMID: 40420259.
Wang S, Luo G, Ding X, Ma X, Yang F, Zhang M, et al. Factors associated with burnout among frontline nurses in the post-COVID-19 epidemic era: a multicenter cross-sectional study. BMC Public Health. 2024;24(1):688. https://doi.org/10.1186/s12889-024-18223-4. PMID: 38438971.
Vargas-Benítez MÁ, Izquierdo-Espín FJ, Castro-Martínez N, Gómez-Urquiza JL, Albendín-García L, Velando-Soriano A, et al. Burnout syndrome and work engagement in nursing staff: a systematic review and meta-analysis. Front Med (Lausanne). 2023; 10:1125133. https://doi.org/10.3389/fmed.2023.1125133. PMID: 37529242.
Gavelin HM, Domellöf ME, Åström E, Nelson A, Launder NH, Neely AS, et al. Cognitive function in clinical burnout: A systematic review and meta-analysis. Work Stress. 2022;36(1):86–104. https://doi.org/10.1080/02678373.2021.2002972
Glise K, Wiegner L, Jonsdottir IH. Long-term follow-up of residual symptoms in patients treated for stress-related exhaustion. BMC Psychol. 2020;8(1):26. https://doi.org/10.1186/s40359-020-0395-8. PMID: 32188513.
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