Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.

Journal: BMC public health
PMID: 39192279

Abstract

BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers.

Authors

  • Gergely Feher
    Centre for Occupational Medicine, Medical School, University of Pécs, Pécs, Hungary.
  • Krisztian Kapus
    Centre for Occupational Medicine, Medical School, University of Pécs, Pécs, Hungary.
  • Antal Tibold
    Centre for Occupational Medicine, Medical School, University of Pécs, Pécs, Hungary.
  • Zoltan Banko
    Department of Labour Law and Social Security Law, Faculty of Law, University of Pécs, Pécs, Hungary.
  • Gyula Berke
    Department of Labour Law and Social Security Law, Faculty of Law, University of Pécs, Pécs, Hungary.
  • Boroka Gacs
    Department of Behavioural Sciences, Medical School, University of Pécs, Szigeti str. 12, Pécs, 7624, Hungary.
  • Imre Varadi
    Centre for Occupational Medicine, Medical School, University of Pécs, Pécs, Hungary.
  • Rita Nyulas
    Baranya County SZC Zipernowsky Károly Technical School, Pécs, Hungary.
  • András Matuz
    Department of Behavioural Sciences, Medical School, University of Pécs, Szigeti Str. 12, Pécs, 7624, Hungary. andras.matuz@aok.pte.hu.

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