Frontiers in public health · 2026
Analysis of latent profiles and influencing factors of artificial intelligence anxiety among newly recruited nurses: a cross-sectional study.
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Ma Xinru, Wang Huan
Аннотация
OBJECTIVE: To investigate the current status of artificial intelligence (AI) anxiety among newly recruited nurses, explore its latent categories and characteristics, and analyze related influencing factors, thereby providing a scientific basis for promoting the acceptance of AI technology among newly recruited nurses and enhancing the nursing workforce's adaptability to AI applications.
METHODS: Convenience sampling was used to select 715 newly recruited nurses from four Grade A tertiary hospitals in Liaoning, Shandong, and Jilin, China. Data were collected using a demographic questionnaire, the Artificial Intelligence Anxiety Scale, the Attitude Scale toward the Use of Artificial Intelligence Technology in Nursing, and the General Self-Efficacy Scale. Latent profile analysis was conducted using Mplus 8.3 software to explore the categories and characteristics of AI anxiety among newly recruited nurses, and univariate analysis and multivariate logistic regression analysis were performed using SPSS 26.0 software to investigate the influencing factors of different categories.
RESULTS: AI anxiety among newly recruited nurses was classified into three categories: low anxiety-technology acceptance (47.6%), moderate anxiety-ambivalent watch and wait (37.5%), and high anxiety-technology rejection (15.0%). Educational level, income level, experience with AI training, proficiency in AI technology, attitude toward AI, and self-efficacy were identified as significant predictors of the latent dimensions of AI anxiety among newly recruited nurses.
CONCLUSION: Heterogeneity exists in AI anxiety among newly recruited nurses, suggesting that nursing managers should focus on the "high anxiety-technology rejection" group by providing targeted AI training and psychological support to enhance these nurses' acceptance and application of AI technology.
METHODS: Convenience sampling was used to select 715 newly recruited nurses from four Grade A tertiary hospitals in Liaoning, Shandong, and Jilin, China. Data were collected using a demographic questionnaire, the Artificial Intelligence Anxiety Scale, the Attitude Scale toward the Use of Artificial Intelligence Technology in Nursing, and the General Self-Efficacy Scale. Latent profile analysis was conducted using Mplus 8.3 software to explore the categories and characteristics of AI anxiety among newly recruited nurses, and univariate analysis and multivariate logistic regression analysis were performed using SPSS 26.0 software to investigate the influencing factors of different categories.
RESULTS: AI anxiety among newly recruited nurses was classified into three categories: low anxiety-technology acceptance (47.6%), moderate anxiety-ambivalent watch and wait (37.5%), and high anxiety-technology rejection (15.0%). Educational level, income level, experience with AI training, proficiency in AI technology, attitude toward AI, and self-efficacy were identified as significant predictors of the latent dimensions of AI anxiety among newly recruited nurses.
CONCLUSION: Heterogeneity exists in AI anxiety among newly recruited nurses, suggesting that nursing managers should focus on the "high anxiety-technology rejection" group by providing targeted AI training and psychological support to enhance these nurses' acceptance and application of AI technology.