Akademik Veri Yönetim Sistemi
PeerJ Computer Science, cilt.12, 2026 (SCI-Expanded, Scopus)
Background: This study aims to create a reliable and valid scale to measure people's attitudes toward, perceptions of, and tendencies to use artificial intelligence-supported health counseling (AI-HC), as well as to look at its psychometric features. Methods: The research used a methodological design with two stages of data collection. The initial list of items had 55 questions, which was narrowed down to 32 based on expert feedback. In the first stage, exploratory factor analysis (EFA) was done with 592 participants, and in the second stage, confirmatory factor analysis (CFA) was carried out with 663 participants. Composite reliability (CR), average variance extracted (AVE), and the Fornell–Larcker criterion were calculated. Internal consistency was checked using Cronbach’s Alpha and McDonald’s Omega coefficients. Data analysis was done using SPSS 26.0 and AMOS 24.0. Results: In both the EFA and CFA datasets, most participants were female and aged between 18–24 years. Most had university degrees, and doctors were the most commonly preferred source of health information. Participants generally reported having low to moderate knowledge about AI technologies in healthcare. The socio-demographic makeup of both datasets was pretty similar. EFA revealed a four-factor structure with 24 items (Usage and Trust, Privacy Perception, Supportiveness Perception, and Medical Competence). The Kaiser–Meyer–Olkin (KMO) value was 0.955, and the total variance explained was 66.01%. In CFA, fit indices (χ2/df=2.957, GFI=0.918, AGFI=0.900, CFI=0.945, NFI=0.938, TLI=0.919, RMSEA=0.054, RMR=0.045) showed the model fit the data well. The four factors showed both convergent and discriminant validity, with acceptable CR and AVE values, and correlations between factors supported discriminant validity. Cronbach’s Alpha and McDonald’s Omega coefficients ranged from 0.79 to 0.94 across all subscales, showing high internal consistency. Based on the psychometric tests, the scale was accepted as a valid and reliable 24-item, four-factor tool. Conclusion: The AI-supported health counseling scale (AI-HCS) is a valid and reliable way to measure key factors influencing the adoption of AI in healthcare. This scale can help with developing AI strategies in health policies and designing patient-centered digital solutions.