Relationship between burnout and turnover intention among nurses: a network analysis
Abstract Background Nurse burnout and turnover intention significantly impact global healthcare systems, especially intensified by the COVID-19 pandemic. This study employs network analysis to explore these phenomena, providing insights into the interdependencies and potential intervention points wi...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-024-02624-2 |
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| author | Jie Zheng Shengya Feng Yaping Feng Luoyan Wang Rong Gao Bowen Xue |
| author_facet | Jie Zheng Shengya Feng Yaping Feng Luoyan Wang Rong Gao Bowen Xue |
| author_sort | Jie Zheng |
| collection | DOAJ |
| description | Abstract Background Nurse burnout and turnover intention significantly impact global healthcare systems, especially intensified by the COVID-19 pandemic. This study employs network analysis to explore these phenomena, providing insights into the interdependencies and potential intervention points within the constructs of burnout and turnover intention among nurses. Methods A cross-sectional study was conducted with 560 nurses from three tertiary hospitals in Hangzhou, China. Data were collected via online questionnaires, including the Maslach Burnout Inventory-General Survey (MBI-GS) and the Turnover Intention Questionnaire (TIQ). Network analysis was performed using Gaussian graphical models to construct the network model and calculate related metrics. Results The network analysis revealed that items related to personal accomplishment and emotional exhaustion were central, indicating significant roles in influencing nurses’ turnover intentions. Specifically, perceived meaningful work and self-efficacy emerged as pivotal nodes, suggesting that enhancing these can mitigate turnover intentions. The network’s stability and accuracy were confirmed through bootstrapping methods, emphasizing the robustness of the findings. Conclusion The study underscores the importance of addressing nurse burnout by focusing on core elements like personal accomplishment and self-efficacy to reduce turnover intentions. These insights facilitate targeted interventions that could improve nurse retention and stability within healthcare systems. Future research should expand to multi-center studies to enhance the generalizability of these findings. |
| format | Article |
| id | doaj-art-d30be394d2c9462ebac13de8b4e9a0c3 |
| institution | OA Journals |
| issn | 1472-6955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-d30be394d2c9462ebac13de8b4e9a0c32025-08-20T01:59:40ZengBMCBMC Nursing1472-69552024-12-0123111010.1186/s12912-024-02624-2Relationship between burnout and turnover intention among nurses: a network analysisJie Zheng0Shengya Feng1Yaping Feng2Luoyan Wang3Rong Gao4Bowen Xue5School of Nursing, Shanxi Medical UniversitySchool of Nursing, Shanxi Medical UniversityAffiliated Hospital of Hangzhou Normal UniversityAffiliated Hospital of Hangzhou Normal UniversitySchool of Nursing, Shanxi Medical UniversityAffiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of MedicineAbstract Background Nurse burnout and turnover intention significantly impact global healthcare systems, especially intensified by the COVID-19 pandemic. This study employs network analysis to explore these phenomena, providing insights into the interdependencies and potential intervention points within the constructs of burnout and turnover intention among nurses. Methods A cross-sectional study was conducted with 560 nurses from three tertiary hospitals in Hangzhou, China. Data were collected via online questionnaires, including the Maslach Burnout Inventory-General Survey (MBI-GS) and the Turnover Intention Questionnaire (TIQ). Network analysis was performed using Gaussian graphical models to construct the network model and calculate related metrics. Results The network analysis revealed that items related to personal accomplishment and emotional exhaustion were central, indicating significant roles in influencing nurses’ turnover intentions. Specifically, perceived meaningful work and self-efficacy emerged as pivotal nodes, suggesting that enhancing these can mitigate turnover intentions. The network’s stability and accuracy were confirmed through bootstrapping methods, emphasizing the robustness of the findings. Conclusion The study underscores the importance of addressing nurse burnout by focusing on core elements like personal accomplishment and self-efficacy to reduce turnover intentions. These insights facilitate targeted interventions that could improve nurse retention and stability within healthcare systems. Future research should expand to multi-center studies to enhance the generalizability of these findings.https://doi.org/10.1186/s12912-024-02624-2BurnoutTurnover intentionsNursesNetwork analysis |
| spellingShingle | Jie Zheng Shengya Feng Yaping Feng Luoyan Wang Rong Gao Bowen Xue Relationship between burnout and turnover intention among nurses: a network analysis BMC Nursing Burnout Turnover intentions Nurses Network analysis |
| title | Relationship between burnout and turnover intention among nurses: a network analysis |
| title_full | Relationship between burnout and turnover intention among nurses: a network analysis |
| title_fullStr | Relationship between burnout and turnover intention among nurses: a network analysis |
| title_full_unstemmed | Relationship between burnout and turnover intention among nurses: a network analysis |
| title_short | Relationship between burnout and turnover intention among nurses: a network analysis |
| title_sort | relationship between burnout and turnover intention among nurses a network analysis |
| topic | Burnout Turnover intentions Nurses Network analysis |
| url | https://doi.org/10.1186/s12912-024-02624-2 |
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