Turkish Adaptation and Psychometric Evaluation of the Trust in AI-Generated Health Advice (TAIGHA) Scale and Its Short Version (TAIGHA-S)

Purpose:This study aims to adapt the Trust in AI-Generated Health Advice (TAIGHA) Scale and its short version (TAIGHA-S) into Turkish and to evaluate their psychometric properties.Methods: This cross-sectional, methodological, and quantitative study included 958 participants. The TAIGHA and TAIGHA-S scales were developed by Kopka et al. (2026). The scales were adapted into Turkish using the back-and-forth translation method, the content validity index was calculated based on expert opinions, and a pilot study was conducted. Exploratory and confirmatory factor analyses were performed for validity analysis. Reliability of the scales was assessed using “Cα, AVE and CR values, item-total correlations, and test-retest reliability analyses.”Results: Exploratory factor analysis revealed that the TAIGHA and TAIGHA-S scales consist of dimensions of trust and distrust, consistent with the original structure. All items have high factor loadings (>0.80). Confirmatory factor analysis showed that the two-dimensional structure of the scales has good fit index values. Both scales have high levels of internal consistency, convergent validity, and test-retest reliability.Conclusion: The Turkish versions of the TAIGHA and TAIGHA-S scales are valid and reliable tools for assessing trust in health advice generated by artificial intelligence. The scales can be used in future research related to digital health and artificial intelligence.