JMIR formative research
Synthetic Content Validation of Pediatric Trust Instruments Using Persona-Driven Large Language Models: Development and Evaluation Study
By Boone E, Loban K, Daskalo C and 5 others.
Abstract
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.
Objective
This study aimed (1) to develop new trust instruments and conduct their preliminary content validation confirming item relevance through a Synthetic Instrument Validation approach integrating human and LLM capabilities and (2) to evaluate appropriate use cases for LLMs in psychometric assessment.