Jul 2026· Frontiers in Public Health· Vol 14· 0 citations· 38 references
Medicine
TL;DR
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.
Abstract
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.
Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice and to explore the factors associated with profile membership with type affiliation.
Hospital-level resources, older age, and longer work experience were the factors most strongly associated with nurses’ AI training acceptance and AI tool usage, with work experience showing the strongest association with tool usage.
Linlin Guo, Qin Ding, M. Tang et al.· Frontiers in Public Health· 0 citations
Understanding nurses’ AI literacy and its relationship with thriving at work may help hospitals design more targeted support strategies to support nurses’ AI literacy and thriving at work in similar clinical contexts.
Rapid integration of artificial intelligence (AI) into maternal and child healthcare introduces unique psychological challenges for nursing staff. This study aimed to explore the network structure of AI anxiety among Chinese maternal and child health nurses and examine its associations with willingness to receive AI-related training, thereby generating preliminary implications for organizational support strategies.
A multicenter cross-sectional survey was conducted between May and August 2025, enrolling 848 nurses from 46 maternal and child healthcare institutions across 26 Chinese provinces. AI anxiety was assessed using the Artificial Intelligence Anxiety Scale (AIAS), and willingness to receive AI-related training was assessed using a single 10-point item. Network analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) and the Extended Bayesian Information Criterion (EBIC) was performed to estimate statistically central nodes, cross-dimensional bridge connectivity, and node predictability.
The highest strength centrality values were observed for a humanoid-AI-fear item and a learning-anxiety item. AI misuse concerns and job replacement fears showed the highest bridge-strength values, indicating relatively strong cross-dimensional connectivity among the anxiety dimensions. Notably, learning-anxiety items showed the strongest positive associations with willingness to receive AI-related training, with edge weights ranging from 0.22 to 0.24. The estimated strength and bridge-strength rankings showed acceptable stability, with correlation-stability coefficients of 0.594 and 0.750, respectively, while node predictability ranged from 69.3 to 92.1%.
AI anxiety among maternal and child health nurses showed a multifaceted network structure in which learning-related stress and fear of anthropomorphic AI were statistically prominent. These data generate preliminary hypotheses about organizational support but do not identify effective intervention targets. Because willingness to receive AI-related training was assessed using a single global item, the study does not identify nurses' multidimensional educational needs, preferred content or delivery modalities, competency priorities, or perceived barriers. Literature-informed options such as phased or simulation-based training should therefore undergo needs assessment and longitudinal or interventional evaluation before implementation.
Chengyu Huang, Qin Zeng, Jun Zhu et al.· Frontiers in Public Health· 0 citations
Background The rapid advancement of artificial intelligence is driving an unprecedented technological transformation in nursing. However, the successful integration of these technologies depends largely on the proficiency and perspectives of registered nurses. Consequently, there is an urgent need to examine the psychological and behavioral responses of this workforce. Methods A multi-center, cross-sectional survey was conducted from March to May 2026. a stratified convenience sampling method was employed to recruit 1,392 registered nurses from tertiary hospitals, secondary hospitals, and community health centers in Chongqing. Data collection instruments included a general demographic questionnaire, the Artificial Intelligence Literacy Scale, the Artificial Intelligence Anxiety Scale, and the General Attitudes Towards Artificial Intelligence Scale. Statistical analyses, including descriptive statistics, Spearman correlation, and multiple linear regression. Results A total of 1,392 registered nurses participated in the study, with a mean age of 34.69 ± 7.13 years. AI literacy scored 5.27 ± 0.90 (75.29% scoring rate). AI anxiety was moderate (51.29%), with the highest concerns appearing in socio-technical blindness (56.43%) and job replacement (55.14%). Overall, nurses maintained a positive attitude toward AI (74.00%). AI literacy was negatively correlated with anxiety (r = −0.338, p < 0.001) and significantly positively correlated with attitude (r = 0.551, p < 0.001), anxiety and attitude were negatively correlated (r = −0.541, p < 0.001). Regression analysis indicated that AI literacy was a significant associated factor for both anxiety levels (B = -0.453, p < 0.001) and attitudes (B = 0.377, p < 0.001), explaining 9.9 and 29.4% of the variance, respectively. Additionally, a lower frequency of electronic device use (p = 0.024) and lack of proficiency in device operation (p = 0.033) were associated with higher anxiety levels, whereas male nurses demonstrated more positive attitudes compared to female nurses (p = 0.011). Conclusion Nurses demonstrated high AI literacy and positive attitudes; however, anxiety remained prominent. Enhancing AI literacy may alleviate psychological anxiety, with device accessibility and usage patterns also playing critical roles. To facilitate the effective integration of artificial intelligence into clinical practice, administrators should strengthen institutional support mechanisms alongside providing facility resources and conventional education, thereby promoting the full utilization and translation of available resources.
Yi Dai, Xi-Li Zhao, Xiaochong Pan et al.· Frontiers in Public Health· 0 citations
Objective To identify latent profiles of moral sensitivity among operating room nurses and to examine factors influencing these profiles. Methods A cross-sectional study was conducted from May to July 2025. A total of 579 operating room nurses from 18 tertiary hospitals in Jiangsu Province, China, were recruited using convenience sampling. Data were collected using the Chinese version of the Moral Sensitivity Questionnaire-Revised Version (MSQ-R-CV), the Nurse Work Environment Perception Questionnaire, and the Role Clarity Scale. Latent profile analysis (LPA) was performed using Mplus 8.0. Binary logistic regression was conducted using SPSS 26.0 to identify independently associated factors of latent profile membership. Results Two distinct latent profiles were identified: a low moral sensitivity group (42.8%) and a high moral sensitivity group (57.2%). Binary logistic regression revealed that gender (male vs. female: OR = 44.27, 95% CI: 5.34–366.93, P < 0.001), age (≥45 years vs. < 25 years: OR = 9.33, 95% CI: 3.87–22.51, P < 0.001), years of experience (5–10 years vs. 1–5 years: OR = 22.64, 95% CI: 10.53–48.70, P < 0.001), and role clarity (OR = 1.09, 95% CI: 1.03–1.14, P < 0.001) were significant independently associated factors of high moral sensitivity. Conclusion The identification of a substantial low-sensitivity subgroup underscores the urgent need for targeted ethics training, particularly for younger and less experienced female nurses. Future targeted interventions focusing on role clarification, particularly for younger, less experienced, and female nurses, may effectively enhance moral sensitivity in this population.
Xiuyun Chen, Fang Yan, Yi Liu et al.· Frontiers in Medicine· 0 citations
