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<p>The gig economy is transforming the labor landscape with flexible, short-term work opportunities that have become appealing across various healthcare sectors. This study explores the feasibility of integrating the gig economy framework (GEF) into Saudi Arabia’s nursing workforce. Using a neural network model, the study examines factors such as job flexibility, satisfaction, and workforce distribution within nursing, aiming to predict compatibility with the GEF. A structured questionnaire gathered data from nurses across Saudi Arabia, and an MLP neural network model analyzed these inputs, achieving a prediction accuracy of 73%. Results suggest that GEF could enhance workforce flexibility and address regional healthcare disparities, particularly in rural areas. However, issues like income instability, job security, and lack of benefits challenge its feasibility. Integrating GEF effectively would require policy adjustments to support nurses’ stability needs, like income protection and essential benefits. While GEF offers a promising solution to staffing shortages and aligns with Saudi Vision 2030’s healthcare goals, balancing flexibility with stability is critical for its long-term success in Saudi nursing. Enhanced AI modeling could further refine workforce compatibility predictions, enabling data-driven approaches to healthcare workforce planning.</p>

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Assessing the viability of the gig economy framework for the nursing workforce in Saudi Arabia: A neural network approach

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Salman Arafath Mohammed,
  • Abdulilah Mohammad Mayet,
  • Shamimul Qamar

摘要

The gig economy is transforming the labor landscape with flexible, short-term work opportunities that have become appealing across various healthcare sectors. This study explores the feasibility of integrating the gig economy framework (GEF) into Saudi Arabia’s nursing workforce. Using a neural network model, the study examines factors such as job flexibility, satisfaction, and workforce distribution within nursing, aiming to predict compatibility with the GEF. A structured questionnaire gathered data from nurses across Saudi Arabia, and an MLP neural network model analyzed these inputs, achieving a prediction accuracy of 73%. Results suggest that GEF could enhance workforce flexibility and address regional healthcare disparities, particularly in rural areas. However, issues like income instability, job security, and lack of benefits challenge its feasibility. Integrating GEF effectively would require policy adjustments to support nurses’ stability needs, like income protection and essential benefits. While GEF offers a promising solution to staffing shortages and aligns with Saudi Vision 2030’s healthcare goals, balancing flexibility with stability is critical for its long-term success in Saudi nursing. Enhanced AI modeling could further refine workforce compatibility predictions, enabling data-driven approaches to healthcare workforce planning.