International journal of nursing practice · 2026
Measuring Ethical Perceptions of AI in Nursing: The Development and Validation of the AI-Ren Ethics in Healthcare (RATI) Scale.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zou Yuanyuan, Su Huimin, Long Quanyi, Xie Mei
Аннотация
AIM: To develop and validate a culturally sensitive instrument for assessing nurses' ethical perceptions of artificial intelligence in clinical practice.
METHODS: A two-phase scale development study was conducted among nurses in China. The first phase involved item generation through literature review and expert consultation, followed by item analysis, exploratory factor analysis and reliability testing. The second phase used confirmatory factor analysis and structural modelling to evaluate construct validity and examine relationships between ethical perceptions, technology acceptance and professional burnout.
RESULTS: The final scale consisted of 20 items across four dimensions: Ren-Based Ethical Awareness, AI Responsibility Judgement, Technological Humility and Collaboration and Integration of Cultural Values. The instrument demonstrated satisfactory reliability, construct validity, convergent validity and discriminant validity. Ethical perceptions of artificial intelligence were positively associated with technology acceptance and negatively associated with professional burnout, supporting the practical relevance of the scale in healthcare settings.
CONCLUSION: The AI-Ren Ethics in Healthcare Scale provides a reliable and culturally grounded instrument for assessing nurses' ethical perceptions of artificial intelligence in clinical practice. The scale may support ethical evaluation, education and culturally responsive implementation of artificial intelligence in healthcare environments.
METHODS: A two-phase scale development study was conducted among nurses in China. The first phase involved item generation through literature review and expert consultation, followed by item analysis, exploratory factor analysis and reliability testing. The second phase used confirmatory factor analysis and structural modelling to evaluate construct validity and examine relationships between ethical perceptions, technology acceptance and professional burnout.
RESULTS: The final scale consisted of 20 items across four dimensions: Ren-Based Ethical Awareness, AI Responsibility Judgement, Technological Humility and Collaboration and Integration of Cultural Values. The instrument demonstrated satisfactory reliability, construct validity, convergent validity and discriminant validity. Ethical perceptions of artificial intelligence were positively associated with technology acceptance and negatively associated with professional burnout, supporting the practical relevance of the scale in healthcare settings.
CONCLUSION: The AI-Ren Ethics in Healthcare Scale provides a reliable and culturally grounded instrument for assessing nurses' ethical perceptions of artificial intelligence in clinical practice. The scale may support ethical evaluation, education and culturally responsive implementation of artificial intelligence in healthcare environments.