Synthetic Content Validation of Pediatric Trust Instruments Using Persona-Driven Large Language Models: Development and Evaluation Study

Background

Large language models (LLMs) could assist in streamlining health care instrument validation. This is particularly relevant as traditional instrument development is time-intensive and resource-intensive. Currently, there is a significant gap in specifically developed and validated trust instruments for pediatric emergency and surgical contexts. Such tools are key because trust is foundational to the patient family-physician relationship and is associated with improved care-seeking and treatment adherence.