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Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis

2025-07-01Journal article Open access
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datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg17:Parcerias para a Implementação dos Objetivos
dc.contributor.authorRocha, Ana
dc.contributor.authorCosteira, Cristina
dc.contributor.authorBarbosa, Raul
dc.contributor.authorGonçalves, Florbela
dc.contributor.authorCastelo-Branco, Miguel
dc.contributor.authorViana, Joaquim
dc.contributor.authorGaudêncio, Margarida
dc.contributor.authorVentura, Filipa
dc.date.accessioned2025-09-02T15:21:14Z
dc.date.available2025-09-02T15:21:14Z
dc.date.issued2025-07-01
dc.descriptionAcknowledgements All nurses involved in the study.
dc.descriptionArticle number - 805
dc.description.abstractBackground 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.eng
dc.description.sponsorshipThis research received no external funding. The work of RB was funded by the FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R&D Unit - UIDB/00326/2020 or project code UIDP/00326/2020. The work of FV was also funded by FCT, CEECINST/00103/2018. The funders had no role in the research nor in the scientific report. The involvement of FV and RB in this work occurred in the context of the project Digital Person (COMPETE2030-FEDER-0092680, Project No. 14765), funded by FCT through the COMPETE 2030 Programme with support from FEDER.
dc.identifier.citationRocha, A., Costeira, C., Barbosa, R. et al. Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis. BMC Nurs 24, 805 (2025). https://doi.org/10.1186/s12912-025-03277-5
dc.identifier.doi10.1186/s12912-025-03277-5
dc.identifier.issn1472-6955
dc.identifier.urihttp://hdl.handle.net/10400.8/13959
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
dc.relation.hasversionhttps://bmcnurs.biomedcentral.com/articles/10.1186/s12912-025-03277-5
dc.relation.ispartofBMC Nursing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBurnout
dc.subjectOccupational health
dc.subjectOncology nursing
dc.subjectMachine learning
dc.subjectProtective factors
dc.subjectWork environment
dc.titleBurnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysiseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00326%2F2020/PT
oaire.citation.titleBMC Nursing
oaire.citation.volume24
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRocha
person.familyNameCosteira
person.givenNameAna
person.givenNameCristina Raquel Batista
person.identifier.ciencia-id5317-5714-6D56
person.identifier.orcid0000-0003-2165-5519
person.identifier.orcid0000-0002-4648-355X
person.identifier.scopus-author-id57200547379
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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relation.isAuthorOfPublication.latestForDiscovery4346170c-ce69-4d8a-b0b5-e583b666481c
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