心理学
心理健康
验证性因素分析
比例(比率)
组内相关
应用心理学
临床心理学
结构效度
同时有效性
心理测量学
苦恼
探索性因素分析
可靠性(半导体)
人工智能
表面有效性
多学科方法
判别效度
测量不变性
公制(单位)
聊天机器人
发展心理学
有效性
社会心理学
认知
测试有效性
预测效度
内容有效性
收敛有效性
构造(python库)
探索性研究
联想(心理学)
考试(生物学)
作者
Aglaia Katsiroumpa,Olympia Konstantakopoulou,Ioannis Moisoglou,Parisis Gallos,Olga Galani,Paschalina Lialiou,Maria Tsiachri,Petros Galanis
标识
DOI:10.20944/preprints202511.1189.v1
摘要
Background/Objectives: Measuring attitudes toward the use of Artificial Intelligence (AI)-based chatbots for mental health support is highly important, especially as these technologies become more integrated into clinical and therapeutic settings. The aim of our study was to develop and validate a scale to measure attitudes toward the use of AI-based chatbots for mental health support, i.e., the Artificial Intelligence in Mental Health Scale (AIMHS). Methods: A multidisciplinary panel of experts assessed the content validity of items that were developed after an extensive literature review. To confirm face validity, we carried out cognitive interviews and calculated the item-level face validity index. Furthermore, we applied both exploratory and confirmatory factor analyses to verify the construct structure of the AIMHS. Moreover, we assessed measurement invariance across demographic subgroups, specifically examining differences by gender, age, and daily use of AI chatbots, social media platforms, and websites. Concurrent validity was evaluated using three instruments: the Artificial Intelligence Attitude Scale (AIAS-4), the Attitudes Towards Artificial Intelligence Scale (ATAI), and the Short Trust in Automation Scale (S-TIAS). Finally, reliability was tested through Cronbach’s alpha, Cohen’s kappa, and the intraclass correlation coefficient. Results: Exploratory and confirmatory factor analyses supported a two-factor model -technical personal advantages- that explained 81.28% of the variance. Moreover, the AIMHS demonstrated strong concurrent validity, evidenced by statistically significant correlations with AIAS-4, ATAI, and S-TIAS. Configural measurement invariance and metric invariance were supported by our findings. Cronbach’s alpha for the AIMHS was 0.798, and intraclass correlation coefficient was 0.938. Cohen’s kappa for the five items ranged from 0.760 to 0.848. Conclusions: The AIMHS is a psychometrically sound and user-friendly instrument for assessing attitudes toward the utilization of AI-based chatbots in mental health support.
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