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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p> Clinical trial number <p>Not applicable.</p>

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Use of machine learning to predict creativity among nurses: a multidisciplinary approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月15日

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  • Rola H. Mudallal,
  • Majd T. Mrayyan,
  • Mohammad Kharabsheh

摘要

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.