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

Jennifer Chipps (), Amanda Cromhout and Umit Tokac
Additional contact information
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

IJERPH, 2025, vol. 22, issue 7, 1-10

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).

Keywords: nurses; resilience; mental health; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/22/7/996/pdf (application/pdf)
https://www.mdpi.com/1660-4601/22/7/996/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:22:y:2025:i:7:p:996-:d:1686563

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().