Treffer: Features influencing surface acting of different clusters of nursing students in vocational college based on interpretable machine learning: A cross-sectional study.

To explore and explain the mechanisms that influence surface acting in nursing students with different characteristics. Nurses are now expected to deliver patient-centered care which necessitates the emotional labor. Surface acting, a form of emotional labor, can lead to negative outcomes. Given that nursing students are the backbone of the future nursing profession, there is an urgent need to investigate their surface acting tendencies and identify potential factors for early intervention. A cross-sectional study. This study was surveyed in a vocational college in Gansu, China. Participants completed the general information questionnaire, Bem Sex Role Inventory, Professional Identity Questionnaire of Nursing Students and Surface Acting Scale. K-means cluster analysis was performed, followed by random forest algorithm and SHapley Additive exPlanations based on Python program. A total of 1241 nursing students from vocational college were investigated and were clustered into 4 groups. The five dimensions of professional identity had higher feature importance in all four groups, with professional self-image having the highest feature importance in Cluster 3. Professional self-image and understanding retention benefits and turnover risks were negative predictors of surface acting in all four groups. Social comparison and self-reflection, independence of career choice and social modeling regarding nursing profession were positively correlated with surface acting in specific groups. In Cluster 1, there exists a positive correlation between professional self-image and the constructs of social comparison and self-reflection; as well as a negative correlation between maternal education and understanding of retention benefits and turnover risks. Professional identity significantly influences surface acting behaviors among nursing students, with professional self-image serving as a key negative predictor. Positive family conditions, access to educational resources, parental literacy, masculine or feminine gender roles and first-year nursing students, these traits have implications when dimensions of professional identity are used to predict surface acting behaviors. • Nursing students are the backbone of the future nursing profession. Understanding the mechanisms that influence surface acting of them will make contributions to their profession development. • Considering the shift from being variable-centered to being person-centered, we used the K-means algorithm, an unsupervised machine learning method to successfully cluster 4 groups with different characteristics. • The important features and interactions associated with different clusters identified by random forest model and SHapley Additive exPlanations by machine learning could help to understand underlying mechanisms of surface acting, identify risk nursing students and make interventions precisely. [ABSTRACT FROM AUTHOR]

Copyright of Nurse Education in Practice is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)