What the study found
The study found that the Trust in AI-Generated Health Advice scale (TAIGHA) and its four-item short form (TAIGHA-S) are validated questionnaires for measuring users' state trust and distrust in AI-generated health advice. The full scale and short form showed strong psychometric properties.
Why the authors say this matters
The authors say this matters because people increasingly use AI tools, including large language models, to get health information and support health-related decisions. The study suggests that measuring trust in AI-generated health advice is important because advice-taking can have clinical, safety, and healthcare-system consequences.
What the researchers tested
The researchers developed theory-based questionnaire items using a generative AI approach and then validated them in several steps. These included automated validation, content validation with 10 domain experts, face validation with 30 lay participants, and psychometric validation with 385 UK participants who received AI-generated health advice for symptom assessment.
What worked and what didn't
After automated item reduction, 28 items were retained and then reduced to 10 based on expert ratings. The final TAIGHA scale showed excellent content validity, face validity, and model fit, and it had high internal consistency for both trust and distrust; the short form correlated highly with the full scale and also showed high reliability. The abstract does not describe any major elements that did not work, beyond the item-reduction process that narrowed the scale.
What to keep in mind
The psychometric validation was conducted with 385 participants in the U.K. receiving AI-generated health advice for symptom assessment, so the reported validation is specific to that sample and context. The abstract does not describe other limitations.
- TAIGHA and TAIGHA-S were developed to measure trust and distrust in AI-generated health advice.
- The study used automated validation plus expert, lay, and participant testing.
- The final TAIGHA scale showed excellent content validity, face validity, and model fit.
- Both trust and distrust subscales showed high internal consistency.
- The short form correlated strongly with the full scale and was also reliable.

