Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis
Abstract Background Oncology nurses face unique and intense demands due to the nature of their work, caring for patients with life-threatening illnesses. The emergence of professional burnout among these nurses is influenced by several factors, highlighting the importance of identifying protective a...
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| Format: | Article |
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BMC
2025-07-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-03277-5 |
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| author | Ana Rocha Cristina Costeira Raul Barbosa Florbela Gonçalves Miguel Castelo-Branco Joaquim Viana Margarida Gaudêncio Filipa Ventura |
| author_facet | Ana Rocha Cristina Costeira Raul Barbosa Florbela Gonçalves Miguel Castelo-Branco Joaquim Viana Margarida Gaudêncio Filipa Ventura |
| author_sort | Ana Rocha |
| collection | DOAJ |
| description | Abstract Background Oncology nurses face unique and intense demands due to the nature of their work, caring for patients with life-threatening illnesses. The emergence of professional burnout among these nurses is influenced by several factors, highlighting the importance of identifying protective and risk factors to mitigate its impact. This study aims to identify burnout profiles and protective socio-demographic and work-related patterns associated with reduced burnout among oncology nurses. Methods A cross-sectional study was conducted with 150 oncology nurses at a specialized hospital exclusively dedicated to adult oncology treatment in Portugal. Data collection included a self-administered questionnaire incorporating the validated Portuguese version of Maslach Burnout Inventory (MBI). Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. Results Six protective patterns against burnout were identified, characterized by conditions of permanent contracts, work-life balance, and supportive work environments. Moreover, factors such as holding management roles and being a parent of two or more children might even be protective in some circumstances, suggesting a nuanced relation between personal and professional factors. Machine learning analyses made apparent the unpredictability of burnout and highlighted the critical role of protective factors in mitigating its impact. Conclusions This study underscores the importance of resilience-building strategies and promoting protective factors, such as job stability, learned experience, and adequate rest, to reduce burnout risk among oncology nurses. Future research should validate these findings through hypothesis-driven analyses to inform targeted and context-specific burnout prevention programs. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-a9bc025936bd49098e7badca943a9519 |
| institution | Kabale University |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-a9bc025936bd49098e7badca943a95192025-08-20T04:01:26ZengBMCBMC Nursing1472-69552025-07-0124111310.1186/s12912-025-03277-5Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysisAna Rocha0Cristina Costeira1Raul Barbosa2Florbela Gonçalves3Miguel Castelo-Branco4Joaquim Viana5Margarida Gaudêncio6Filipa Ventura7Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC)Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC)Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of CoimbraFaculdade de Ciências da Saúde, Universidade da Beira InteriorFaculdade de Ciências da Saúde, Universidade da Beira InteriorULS Coimbra - Centro Hospitalar e Universitário de Coimbra EPEPalliative Care Unit, Portuguese Oncology Institute of CoimbraHealth Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC)Abstract Background Oncology nurses face unique and intense demands due to the nature of their work, caring for patients with life-threatening illnesses. The emergence of professional burnout among these nurses is influenced by several factors, highlighting the importance of identifying protective and risk factors to mitigate its impact. This study aims to identify burnout profiles and protective socio-demographic and work-related patterns associated with reduced burnout among oncology nurses. Methods A cross-sectional study was conducted with 150 oncology nurses at a specialized hospital exclusively dedicated to adult oncology treatment in Portugal. Data collection included a self-administered questionnaire incorporating the validated Portuguese version of Maslach Burnout Inventory (MBI). Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. Results Six protective patterns against burnout were identified, characterized by conditions of permanent contracts, work-life balance, and supportive work environments. Moreover, factors such as holding management roles and being a parent of two or more children might even be protective in some circumstances, suggesting a nuanced relation between personal and professional factors. Machine learning analyses made apparent the unpredictability of burnout and highlighted the critical role of protective factors in mitigating its impact. Conclusions This study underscores the importance of resilience-building strategies and promoting protective factors, such as job stability, learned experience, and adequate rest, to reduce burnout risk among oncology nurses. Future research should validate these findings through hypothesis-driven analyses to inform targeted and context-specific burnout prevention programs. Clinical trial number Not applicable.https://doi.org/10.1186/s12912-025-03277-5BurnoutOncology nursingProtective factorsWork environmentMachine learningOccupational health |
| spellingShingle | Ana Rocha Cristina Costeira Raul Barbosa Florbela Gonçalves Miguel Castelo-Branco Joaquim Viana Margarida Gaudêncio Filipa Ventura Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis BMC Nursing Burnout Oncology nursing Protective factors Work environment Machine learning Occupational health |
| title | Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis |
| title_full | Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis |
| title_fullStr | Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis |
| title_full_unstemmed | Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis |
| title_short | Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis |
| title_sort | burnout protective patterns among oncology nurses a cross sectional study using machine learning analysis |
| topic | Burnout Oncology nursing Protective factors Work environment Machine learning Occupational health |
| url | https://doi.org/10.1186/s12912-025-03277-5 |
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