浙江省高校学生AI诊断技术焦虑的影响因素与相关性研究

A Study on the Influencing Factors and Correlations of Anxiety about AI Diagnostic Technology among College Students in Zhejiang Province

摘要: 目的：调查高校学生人工智能(Artificial Intelligence)诊断技术焦虑现状及影响因素，为临床护理工作中开展前瞻性健康指导、改善患者就医体验提供参考。方法：采用便利抽样法，对浙江省1416名高校学生进行问卷调查，使用修订的AI诊断技术焦虑量表(含效能失调、风险感知、伦理隐私3维度，13条目，Cronbach’s α = 0.932)及一般资料问卷。采用多元线性回归分析影响因素。结果：学生AI诊断技术焦虑处于中等水平(总分3.44 ± 0.89)，伦理隐私维度得分最高。回归分析显示，主动使用意愿(β = −0.203)、政策了解程度(β = −0.170)、可靠性评价(β = −0.139)是保护因素；年龄(β = 0.168)、性别(β = 0.126)、对传统医疗信任(β = 0.110)、医-AI冲突决策偏好(β = 0.126)、过度依赖担忧(β = 0.121)、标注“辅助工具”态度(β = 0.125)是危险因素(均P < 0.05)，模型解释总变异48.2%。结论：高校学生AI诊断焦虑受认知、行为及专业背景多重影响。护理教育应重点强化数据安全与伦理素养，通过提升技术体验与政策认知，降低学生技术焦虑，提升其对智能技术的理性接纳度。

Abstract: Objective: To investigate the current situation and influencing factors of artificial intelligence diagnostic technology anxiety among university students, providing references for proactive health guidance and improving patient medical experiences in clinical nursing. Methods: Using convenience sampling, a questionnaire survey was conducted among 1416 university students in Zhejiang Province. The revised AI Diagnostic Technology Anxiety Scale (including three dimensions: efficacy dysfunction, risk perception, ethical privacy, 13 items, Cronbach’s α = 0.932) and a general information questionnaire were used. Multiple linear regression was employed to analyze influencing factors. Results: Students’ AI diagnostic technology anxiety was at a moderate level (total score 3.44 ± 0.89), with the ethical privacy dimension scoring the highest. Regression analysis showed that willingness to actively use AI (β = −0.203), level of policy understanding (β = −0.170), and reliability evaluation (β = −0.139) were protective factors; age (β = 0.168), gender (β = 0.126), trust in traditional medicine (β = 0.110), preference for physician-AI conflicting decisions (β = 0.126), worry about over-reliance (β = 0.121), and labeling AI as an “assistive tool” (β = 0.125) were risk factors (all P < 0.05). The model explained 48.2% of the total variance. Conclusion: University students’ AI diagnostic anxiety is influenced by multiple factors, including cognition, behavior, and professional background. Nursing education should focus on strengthening data security and ethical literacy, and by enhancing technical experience and policy awareness, reduce students’ technology anxiety and improve their rational acceptance of intelligent technology.