ArticleBMC nursing2025
Use of machine learning to predict creativity among nurses: a multidisciplinary approach.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsA 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.
resultsrevealed 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.
conclusionsTo 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.
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