Business School, Sichuan University, Sichuan, China; Global Organization of African Academic Doctors (OAAD), P.O. Box 14833-00100, Langata, Nairobi, Kenya.
Business School, Sichuan University, Sichuan, China.
Soc Sci Med. 2024 Nov;360:117329. doi: 10.1016/j.socscimed.2024.117329. Epub 2024 Sep 11.
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.
This study explores the applicability, explainability, and implications of ANN models in predicting career fulfillment among medical doctors in developing nations, considering socio-economic, psychological, and professional factors. Box plots visualized data distribution, while Heatmaps assessed data intensity and relationships. Matthew's correlation coefficient and Taylor's chart were used to evaluate model performance. Input feature contributions to ANN predictions were analyzed using permutation importance, SHAP, LIME, and Williams plots. The model was tested on a dataset tailored to medical professionals in Nigeria and China, with evaluation metrics including Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R Score.
The ANN model demonstrates strong predictive accuracy, capturing relationships between input factors and outcomes. For Chinese doctors, it achieved an MSE of 0.0004 and R of 0.9994, while for Nigerian doctors, it recorded an MSE of 0.0003 and R of 0.9998. Key factors for Chinese doctors' satisfaction were IF1 and IF2, while EF1 and EF3 were crucial in preventing dissatisfaction. For Nigerian doctors, IF2 and IF3 drove satisfaction, while EF1 and EF4 were significant in avoiding dissatisfaction.
The results highlights the ANN model's effectiveness in predicting career fulfillment among medical doctors in developing nations, offering a valuable tool for career guidance, policymaking, and improving job satisfaction, retention, and healthcare quality.
医生的职业满意度对工作满意度、保留率和医疗质量至关重要，尤其是在医疗体系具有挑战性的发展中国家。传统的职业指导方法难以应对职业满意度的复杂性。虽然机器学习，特别是人工神经网络 (ANN) 模型的最新进展为个性化职业预测提供了有希望的解决方案，但它们的适用性、可解释性和影响仍然具有挑战性。
本研究探讨了 ANN 模型在预测发展中国家医生职业满意度中的适用性、可解释性和影响，考虑了社会经济、心理和专业因素。箱线图可视化了数据分布，而热图评估了数据强度和关系。马修相关系数和泰勒图用于评估模型性能。使用排列重要性、SHAP、LIME 和威廉姆斯图分析 ANN 预测的输入特征贡献。该模型在针对尼日利亚和中国医疗专业人员量身定制的数据集上进行了测试，评估指标包括平均绝对误差、均方误差、均方根误差和 R 分数。
ANN 模型表现出很强的预测准确性，捕捉了输入因素与结果之间的关系。对于中国医生，它的均方误差为 0.0004，R 为 0.9994，而对于尼日利亚医生，它的均方误差为 0.0003，R 为 0.9998。中国医生满意度的关键因素是 IF1 和 IF2，而 EF1 和 EF3 对于防止不满至关重要。对于尼日利亚医生，IF2 和 IF3 驱动满意度，而 EF1 和 EF4 对于避免不满至关重要。
研究结果强调了 ANN 模型在预测发展中国家医生职业满意度方面的有效性，为职业指导、政策制定以及提高工作满意度、保留率和医疗质量提供了有价值的工具。
