Hark Söylemez Nesrin
Department of Educational Sciences, Dicle University, Diyarbakir, Turkey.
BMC Med Educ. 2025 May 19;25(1):726. doi: 10.1186/s12909-025-07296-3.
Empathy is a key factor in the medical field as it strengthens doctor-patient relationships, enhances communication, and leads to improved patient outcomes. This study aims to investigate the empathy levels of medical students, providing insights into the factors that influence these levels and using advanced analytical methods for accurate predictions.
The study was conducted with 322 medical students from a public university in Turkey. A relational screening model was applied, using a "Personal Information Form" and an "Empathy Scale" to gather data. CHAID analysis was employed to identify the key predictors influencing empathy levels, whereas machine learning algorithms were utilized to classify and predict individuals' empathy levels.
The analysis revealed that 41.3% of students displayed high empathy, 44.7% moderate empathy, and 14.0% low empathy. Factors such as parental education, maternal occupation, and gender were significant in determining empathy levels, with gender being the most influential. The machine learning models achieved an 80.1% accuracy in predicting empathy levels.
The findings indicate that targeted educational and social interventions, especially those addressing gender differences, could improve empathy in medical students, potentially leading to better patient care.
Not applicable, as this study does not report results from a health care intervention involving human participants.
同理心是医学领域的一个关键因素，因为它能加强医患关系、改善沟通并带来更好的患者治疗效果。本研究旨在调查医学生的同理心水平，深入了解影响这些水平的因素，并使用先进的分析方法进行准确预测。
该研究对来自土耳其一所公立大学的322名医学生进行。采用关系筛选模型，使用“个人信息表”和“同理心量表”收集数据。采用CHAID分析来确定影响同理心水平的关键预测因素，而机器学习算法则用于对个体的同理心水平进行分类和预测。
分析显示，41.3%的学生表现出高同理心，44.7%表现出中等同理心，14.0%表现出低同理心。父母教育程度、母亲职业和性别等因素在确定同理心水平方面具有重要意义，其中性别影响最大。机器学习模型在预测同理心水平方面的准确率达到了80.1%。
研究结果表明，有针对性的教育和社会干预措施，特别是那些解决性别差异的措施，可以提高医学生的同理心，有可能带来更好的患者护理。
不适用，因为本研究未报告涉及人类参与者的医疗保健干预结果。
