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Using Machine Learning to Predict Resilience Among Nurses in a South African Setting

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  • Jennifer Chipps

    (School of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7441, South Africa)

  • Amanda Cromhout

    (School of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7441, South Africa)

  • Umit Tokac

    (College of Nursing, University of Missouri, St. Louis, MO 63121, USA)

Abstract

Nursing is a stressful profession. Stress can affect the mental health of nurses. A positive response to stress, resilience, is known to be a protective factor against mental health issues. This study aimed to use machine learning with secondary data from five survey studies, conducted between 2022 and 2023, to identify factors predicting high versus low levels of resilience in South African nursing samples from the Western Cape Province, South Africa. The sample included (1134 records (male = 250, 22.0%, female = 874, 77.1%, and other = 10 (0.9%) included all data on all categories of nursing staff (student nurses (567, 50%), professional registered nurses (315, 27.8%), and non-professional nurses (246, 21.7%) who completed a survey using a response to stress scale. We used random forest analysis, demographic variables, years of experience, and a brief 4-item screen of resilience to predict resilience. The model yielded limited added value from demographic groupings in this model, but the brief screening had an overall classification accuracy of 86.41% (95% CI: 0.810; 0.908).

Suggested Citation

  • Jennifer Chipps & Amanda Cromhout & Umit Tokac, 2025. "Using Machine Learning to Predict Resilience Among Nurses in a South African Setting," IJERPH, MDPI, vol. 22(7), pages 1-9, June.
    • Handle: RePEc:gam:jijerp:v:22:y:2025:i:7:p:996-:d:1686563
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