开发并验证了评估用户对AI健康建议信任度的量表，支持临床与研究使用。
The Trust in AI-Generated Health Advice (TAIGHA) Scale and Short Version (TAIGHA-S): Development and Validation Study
- 基于认知与情感双维度设计信任与不信任量表，结合生成式AI与专家评审。
- 量表信效度优秀，全版28项经精简为10项，短版10项与全版相关性达0.96。
- 适合临床、研究中快速评估用户对AI健康建议的信任程度，尤其适用于时间紧张场景。
人工智能工具如大语言模型正被公众广泛用于获取健康信息与指导。在健康领域，采纳或拒绝AI建议可能带来直接临床影响。现有量表如自动化系统信任调查（Trust in Automated Systems Survey）仅评估通用技术可信度，缺乏针对AI健康建议信任的已验证测量工具。本研究开发并验证了基于理论的《对AI生成健康建议的信任度量表》（TAIGHA）及其4项简版（TAIGHA-S），涵盖认知与情感成分的信与不信维度。项目通过生成式AI生成初稿，经10位领域专家内容验证、30名普通参与者外观验证，及385名英国参与者在症状评估情景中的心理测量验证。经自动删减后保留28项，再依据专家评分精简至10项。TAIGHA显示极佳内容效度（S-CVI/Ave=0.99），结构方程模型确认两因子模型拟合良好（CFI=0.98，TLI=0.98，RMSEA=0.07，SRMR=0.03），内部一致性高（α=0.95）。收敛效度由与自动化系统信任量表的相关性（r=0.67/-0.66）及用户依赖程度（信任项：r=0.37）支持，区分效度由与阅读流畅性、心理负荷的低相关性（所有| r |<0.25）证实。TAIGHA-S与全版高度相关（r=0.96），信度良好（α=0.88）。TAIGHA与TAIGHA-S为评估用户对AI健康建议信任与不信任的可靠工具，分别报告信任与不信任可更全面评估干预效果，短版适用于时间受限情境。
原文摘要 · Abstract (English)
Artificial Intelligence tools such as large language models are increasingly used by the public to obtain health information and guidance. In health-related contexts, following or rejecting AI-generated advice can have direct clinical implications. Existing instruments like the Trust in Automated Systems Survey assess trustworthiness of generic technology, and no validated instrument measures users' trust in AI-generated health advice specifically. This study developed and validated the Trust in AI-Generated Health Advice (TAIGHA) scale and its four-item short form (TAIGHA-S) as theory-based instruments measuring trust and distrust, each with cognitive and affective components. The items were developed using a generative AI approach, followed by content validation with 10 domain experts, face validation with 30 lay participants, and psychometric validation with 385 UK participants who received AI-generated advice in a symptom-assessment scenario. After automated item reduction, 28 items were retained and reduced to 10 based on expert ratings. TAIGHA showed excellent content validity (S-CVI/Ave=0.99) and CFA confirmed a two-factor model with excellent fit (CFI=0.98, TLI=0.98, RMSEA=0.07, SRMR=0.03). Internal consistency was high (α=0.95). Convergent validity was supported by correlations with the Trust in Automated Systems Survey (r=0.67/-0.66) and users' reliance on the AI's advice (r=0.37 for trust), while divergent validity was supported by low correlations with reading flow and mental load (all |r|<0.25). TAIGHA-S correlated highly with the full scale (r=0.96) and showed good reliability (α=0.88). TAIGHA and TAIGHA-S are validated instruments for assessing user trust and distrust in AI-generated health advice. Reporting trust and distrust separately permits a more complete evaluation of AI interventions, and the short scale is well-suited for time-constrained settings.
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