Yıldırım Tuğba Öztürk, Karaman Mesut
Nursing Department, Doğuş University, Istanbul, Türkiye.
Department of Business Administration, Institute of Social Sciences, Sivas Cumhuriyet University, Sivas, Türkiye.
BMC Nurs. 2025 Apr 22;24(1):441. doi: 10.1186/s12912-025-03098-6.
Since artificial intelligence is transforming healthcare, targeted interventions aimed at optimizing its integration and use in clinical settings requires the assessment of nurses' attitudes towards AI.
To develop and validate an Artificial Intelligence Attitude Scale specifically for Turkish nurses.
This methodological study was conducted between October 2024 and December 2024, and its sample consisted of 678 nurses working in Turkey. The item pool was developed through a comprehensive literature review. Data analysis included descriptive statistics, item analysis, and exploratory and confirmatory factor analyses, as well as assessments of convergent and divergent validity, correlation analysis, internal consistency reliability, and test-retest reliability.
The content validity index for the items ranged from 0.85 to 1.00. Exploratory factor analysis revealed that the eigenvalues for four factors were greater than one, and these four factors accounted for 77.28% of the total variance. The scale demonstrated an acceptable model fit, with a goodness of fit index of 0.921 and a root mean square error of approximation (RMSEA) of 0.064. Cronbach's alpha coefficients ranged from 0.93 to 0.95 across the subscales, indicating high internal consistency, with the scale showing convergent and divergent validity. In addition, the Artificial Intelligence Attitude Scale for Nurses was found to have high test-retest reliability. This study may offer valuable insights into nurses' attitudes toward digital technologies, thereby informing the trajectory of digital transformation in healthcare services.
由于人工智能正在改变医疗保健领域，旨在优化其在临床环境中的整合与应用的针对性干预措施需要评估护士对人工智能的态度。
开发并验证专门针对土耳其护士的人工智能态度量表。
这项方法学研究于2024年10月至12月进行，样本包括在土耳其工作的678名护士。通过全面的文献综述建立项目池。数据分析包括描述性统计、项目分析、探索性和验证性因素分析，以及收敛效度和区分效度评估、相关分析、内部一致性信度和重测信度评估。
各项目的内容效度指数在0.85至1.00之间。探索性因素分析表明，四个因素的特征值大于1，这四个因素占总方差的77.28%。该量表显示出可接受的模型拟合度，拟合优度指数为0.921，近似均方根误差（RMSEA）为0.064。各分量表的Cronbach's alpha系数在0.93至0.95之间，表明内部一致性较高，该量表具有收敛效度和区分效度。此外，护士人工智能态度量表具有较高的重测信度。本研究可能为护士对数字技术的态度提供有价值的见解，从而为医疗服务数字化转型的轨迹提供信息。
