Development and psychometric evaluation of the artificial intelligence attitude scale for nurses

Abstract

BACKGROUND: 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. AIM: To develop and validate an Artificial Intelligence Attitude Scale specifically for Turkish nurses. METHOD: 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. RESULTS: 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.

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