运用机器学习预测护士的创造力：一种多学科方法。
Use of machine learning to predict creativity among nurses: a multidisciplinary approach.
作者信息
Mudallal Rola H, Mrayyan Majd T, Mohammad Kharabsheh
机构信息
Department of Community and Mental Health Nursing, Faculty of Nursing, The Hashemite University, Zarqa, Jordan.
School of Nursing, The Hashemite University, P.O. Box 150459, Zarqa, 13115, Jordan.
出版信息
BMC Nurs. 2025 May 15;24(1):539. doi: 10.1186/s12912-025-03151-4.
BACKGROUND
In this era of rapid development in science and technology, creativity has become an important requirement in nursing to satisfy the daily needs of their patients. However, nurses' creativity and related aspects are rarely studied in nursing research. This study was aimed to explore the factors influencing nurses' creativity and to develop a decision support system using machine learning to predict creativity levels among nurses.
METHODS
A multidisciplinary design comprising machine learning algorithms mixed with a descriptive, cross-sectional, correlational design was implemented to enhance data analysis and decision-making. A convenience sample of 191 registered nurses from eight hospitals- representing the broader nursing community in Jordan- was recruited to complete the online survey.
RESULTS
revealed that staff nurses reported a high level of creativity (M = 44.95). The machine learning model achieved good prediction performance with high precision. Specifically, Naïve Bayes achieved a recall of 99% for predicting psychological safety, around 98% for both gender and time commitment, 96% for years of experience, 92% for nurse age, and 82% for humble leadership. A decision support system was successfully developed based on these findings. Additionally, a multiple linear regression revealed five main predictors of nurses' creativity: humble leadership, psychological safety, experience, quality initiatives, and education level, together explaining about 30% of the variance in perceived creativity among staff nurses.
CONCLUSIONS
To augment nurses' creativity, managers are advised to adopt flexible leadership styles, create a safe work environment, and encourage staff development. The developed decision support system may be valuable for helping nurse managers evaluate creativity among nurses; this allows for more informed decisions about staff allocation, development, and resource optimization. Researchers are encouraged to use machine learning models because they achieve good prediction performance with high precision.
CLINICAL TRIAL NUMBER
Not applicable.
背景
在这个科技飞速发展的时代，创造力已成为护理工作满足患者日常需求的一项重要要求。然而，护士的创造力及相关方面在护理研究中很少被探讨。本研究旨在探究影响护士创造力的因素，并利用机器学习开发一个决策支持系统来预测护士的创造力水平。
方法
采用多学科设计，将机器学习算法与描述性、横断面、相关性设计相结合，以加强数据分析和决策制定。从约旦八家医院招募了191名注册护士作为便利样本，这些护士代表了更广泛的约旦护理群体，他们完成了在线调查。
结果
显示注册护士报告的创造力水平较高（M = 44.95）。机器学习模型实现了高精度的良好预测性能。具体而言，朴素贝叶斯在预测心理安全感方面的召回率达到99%，在预测性别和工作投入方面约为98%，在预测工作经验年限方面为96%，在预测护士年龄方面为92%，在预测谦逊领导力方面为82%。基于这些发现成功开发了一个决策支持系统。此外，多元线性回归揭示了护士创造力的五个主要预测因素：谦逊领导力、心理安全感、经验、质量改进举措和教育水平，它们共同解释了注册护士感知创造力差异的约30%。
结论
为提高护士的创造力，建议管理者采用灵活的领导风格，营造安全的工作环境，并鼓励员工发展。所开发的决策支持系统可能有助于护士管理者评估护士的创造力，从而在人员分配、发展和资源优化方面做出更明智的决策。鼓励研究人员使用机器学习模型，因为它们能实现高精度的良好预测性能。
临床试验编号
不适用。
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