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Explainability of artificial neural network in predicting career fulfilment among medical doctors in developing nations: Applicability and implications

Author

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  • Thomas, Dara
  • Li, Ying
  • Ukwuoma, Chiagoziem C.
  • Dossa, Joel

Abstract

Career fulfilment among medical doctors is crucial for job satisfaction, retention, and healthcare quality, especially in developing nations with challenging healthcare systems. Traditional career guidance methods struggle to address the complexities of career fulfilment. While recent advancements in machine learning, particularly Artificial Neural Network (ANN) models, offer promising solutions for personalized career predictions, their applicability, interpretability, and impact remain challenging.

Suggested Citation

  • Thomas, Dara & Li, Ying & Ukwuoma, Chiagoziem C. & Dossa, Joel, 2024. "Explainability of artificial neural network in predicting career fulfilment among medical doctors in developing nations: Applicability and implications," Social Science & Medicine, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:socmed:v:360:y:2024:i:c:s0277953624007834
    DOI: 10.1016/j.socscimed.2024.117329
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    References listed on IDEAS

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    1. Rachelle Alpern & Maureen E Canavan & Jennifer T Thompson & Zahirah McNatt & Dawit Tatek & Tessa Lindfield & Elizabeth H Bradley, 2013. "Development of a Brief Instrument for Assessing Healthcare Employee Satisfaction in a Low-Income Setting," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-8, November.
    2. Donald Barron & Graham Ball & Matthew Robins & Caroline Sunderland, 2018. "Artificial neural networks and player recruitment in professional soccer," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-11, October.
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