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

Jennifer Chipps1, Amanda Cromhout1, Umit Tokac2

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Summary
This summary is machine-generated.

This study identified factors predicting resilience in South African nurses using machine learning. A brief resilience screening tool accurately predicted high versus low resilience levels in nursing staff.

Keywords:
machine learningmental healthnursesrandom forestresilience

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Area of Science:

  • Nursing
  • Psychology
  • Data Science

Background:

  • Nursing is a high-stress profession, impacting mental health.
  • Resilience, a positive stress response, is crucial for mental well-being.
  • Understanding resilience factors is vital for supporting nurses' mental health.

Purpose of the Study:

  • To identify predictors of high versus low resilience in South African nurses.
  • To utilize machine learning for analyzing secondary survey data.
  • To assess the effectiveness of a brief resilience screening tool.

Main Methods:

  • Secondary data analysis from five surveys (2022-2023) of 1134 South African nurses.
  • Random forest analysis applied to demographic variables, years of experience, and a 4-item resilience screen.
  • Predictive modeling to identify factors associated with resilience levels.

Main Results:

  • The random forest model achieved an 86.41% classification accuracy for resilience levels.
  • Demographic variables provided limited additional predictive value.
  • A brief 4-item resilience screening demonstrated high predictive accuracy.

Conclusions:

  • A brief resilience screening tool is effective in identifying resilience levels in nurses.
  • Machine learning models can accurately predict resilience in nursing populations.
  • Further research can refine interventions to enhance nurse resilience.