A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa.

Journal: BMC health services research
PMID:

Abstract

BACKGROUND: The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nurses and informing policymakers. Currently, little is known about the unique factors associated with burnout and emotional exhaustion among nurses in South Africa. Furthermore, whether these factors can be predicted using demographic data alone is unclear. Machine learning has recently been proven to solve complex problems and accurately predict outcomes in medical settings. In this study, supervised machine learning models were developed to identify the factors that most strongly predict nurses reporting feelings of burnout and experiencing emotional exhaustion.

Authors

  • Maria Magdalena Van Zyl-Cillié
    Faculty of Engineering, North-West University, 11 Hoffman Street, Potchefstroom, South Africa. maria.vanzyl@nwu.ac.za.
  • Jacoba H Bührmann
    Faculty of Engineering, North-West University, 11 Hoffman Street, Potchefstroom, South Africa.
  • Alwiena J Blignaut
    NuMIQ Research Focus Area, School of Nursing Science, North-West University, 11 Hoffman Street, Potchefstroom, South Africa.
  • Derya Demirtas
    Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.
  • Siedine K Coetzee
    NuMIQ Research Focus Area, School of Nursing Science, North-West University, 11 Hoffman Street, Potchefstroom, South Africa.