AI诊断情境下技术焦虑量表的修订及信效度检验

Revision and Reliability and Validity Testing of the Technology Anxiety Scale in the Context of AI Diagnosis

摘要: 目的：基于压力认知评价理论(Cognitive Appraisal Theory)，修订适用于医疗人工智能(Artificial Intelligence)诊断场景的技术焦虑量表，并检验其信效度，为护理人员评估青年群体AI诊断焦虑水平提供可靠工具。方法：收集有效样本1416份，随机分半进行探索性与验证性因子分析；3周后，选取其中247名进行重测。采用技术焦虑量表、一般自我效能感量表(GSES)作为效标工具。结果：量表最终包含效能失调、风险感知、伦理隐私3个因子，共13个条目，累计解释方差77.20%。验证性因子分析拟合良好(χ²/df = 2.66, RMSEA = 0.08, CFI = 0.96, TLI = 0.95)。量表及维度得分与技术焦虑量表、GSES均显著相关(|r| = 0.33~0.63)。总量表Cronbach’s α系数为0.93，重测信度ICC为0.85。结论：修订后的量表信效度良好，可作为评估青年群体AI诊断焦虑的有效工具。

Abstract: Objective: Based on the Cognitive Appraisal Theory of stress, we revised a technology anxiety scale tailored for medical AI diagnostic scenarios and tested its reliability and validity, providing a trustworthy tool for healthcare workers to assess AI diagnosis anxiety among young people. Method: We collected 1,416 valid samples, randomly split them in half for exploratory and confirmatory factor analysis; three weeks later, 247 participants were retested. The technology anxiety scale and the General Self-Efficacy Scale (GSES) were used as criterion measures. Results: The final scale included three factors—efficacy dysfunction, risk perception, and ethical privacy—covering 13 items and explaining 77.20% of the total variance. Confirmatory factor analysis showed a good fit (χ²/df = 2.66, RMSEA = 0.08, CFI = 0.96, TLI = 0.95). The total scale and dimension scores were significantly correlated with both the technology anxiety scale and GSES (|r| = 0.33~0.63). The total scale Cronbach’s α was 0.93, and retest reliability ICC was 0.85. Conclusion: The revised scale has good reliability and validity and can serve as an effective tool for assessing AI diagnostic anxiety in young people.