本科护生人工智能焦虑的潜在剖面分析.
Objective:To analyze the latent profiles of artificial intelligence anxiety among undergraduate nursing students, and to explore the influencing factors of artificial intelligence anxiety among different profiles of nursing students. Methods: Using the convenience sampling method, from April to May...
| Publicado en: | Chinese Nursing Research Vol. 40; no. 16; pp. 2797 - 2804 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Chinese Nursing Research Editorial Office
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | Objective:To analyze the latent profiles of artificial intelligence anxiety among undergraduate nursing students, and to explore the influencing factors of artificial intelligence anxiety among different profiles of nursing students. Methods: Using the convenience sampling method, from April to May 2025, a total of 720 undergraduate nursing students from three medical colleges in Hubei province were selected as the research subjects. The general information questionnaire, Artificial Intelligence Anxiety Scale(AIAS) and Medical Artificial Intelligence Readiness Scale(MAIRS) were used for the investigation. Results:The score of artificial intelligence anxiety among undergraduate nursing students was 86.00±20.95. They could be divided into three latent profiles: low anxiety - rational alertness type (10.4%), medium anxiety-risk sensitivity type(65.3%), and high anxiety-generalized fear type(24.3%). Logistic regression analysis results showed that grade, the willingness to try using artificial intelligence tools in clinical practice, and medical artificial intelligence readiness were the influencing factors of artificial intelligence anxiety among undergraduate nursing students(all P<0.05). Conclusion: The artificial intelligence anxiety among undergraduate nursing students is at a moderately high level. There is heterogeneity. Nursing educators need to develop precise intervention plans for undergraduate nursing students based on the influencing factors, so as to help them better adapt to the nursing education and career development requirements in the era of intelligence. 目的:分析本科护生人工智能焦虑的潜在剖面, 并探讨不同剖面护生人工智能焦虑的影响因素。方法:采用便利抽样法, 于 2025 年4 月--5 月, 选取湖北省3 所医学院校的720 名本科护生作为研究对象, 应用一般资料调查表、人工智能焦虑量表、医学人工智 能准备度量表进行调查。结果:本科护生人工智能焦虑得分为（86. 00±20. 95）分, 可分为低焦虑-理性警觉型（10. 4%）、中焦虑-风险 敏感型（65. 3%）和高焦虑-泛化恐惧型（24. 3%）3 个潜在剖面。Logistic 回归分析结果显示, 年级、临床实践中尝试使用人工智能工 具的意愿、医学人工智能准备度是本科护生人工智能焦虑的影响因素（均P<0. 05）。结论:本科护生人工智能焦虑处于中等偏高水 平, 且存在群体异质性。护理教育者需根据影响因素为本科护生制定精准干预方案, 帮助其更好地适应智能时代的护理教育与职业 发展需求. |
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