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. 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. Methods: Two trust instruments were developed (patient family and physician versions). In phase 1, instruments underwent a 2-stage content validation process using parallel synthetic and human expert panels (5 patient family members, 3 physician synthetic experts, and 7 unique human experts). Synthetic panels consisted of 3 persona-prompted LLMs (Claude Sonnet 4, GPT-5, and Grok 4), with human panels serving as comparators. The scale-content validity index (S-CVI) threshold was set at ≥0.80, percent agreement on item relevancy was used to compare panels. In phase 2, LLM performance in quantitative research tasks was evaluated. The patient family instrument underwent Flesch-Kincaid assessment; both instruments underwent cosine similarity analyses using algorithmic and LLM-based methods. Results: In phase 1, percent agreement between panels for the patient family and physician instruments was 90.9% and 88.9% for dimensional validation and 90.9% and 100% for contextual validation. Fleiss κ for within-panel agreement ranged from poor to almost perfect; 0.20 (95% CI -0.23 to 0.63) to 1.00 (95% CI 1.00-1.00). All instrument subsections met or exceeded S-CVI thresholds across both rounds and panels. Phase 2 Flesch-Kincaid metrics converged across all 3 methods to target benchmark (mean Grade Level 8.1, SD 1.1; mean Reading Ease 60.1, SD 5.6), while reference readability was near target, showing convergence across implementations of the same formula. In contrast, cosine similarity analyses revealed significant LLM performance limitations; cosine similarity analyses were, therefore, abandoned in favor of the algorithmic method alone, which achieved a maximum cosine similarity of 0.83, indicating acceptable item distinctiveness overall. Conclusions: Persona-prompted LLMs effectively assisted preliminary content validation and may help reduce timelines from months to weeks, but showed limitations in quantitative computations. This suggests that, in this study, persona-prompted LLMs performed better in subjective rating tasks than in deterministic cosine similarity computations. These findings help illustrate task-dependent boundaries for LLM integration in psychometric research, necessitating selective human-LLM collaboration. While these instruments achieved preliminary content validity, they require full psychometric testing before they can be considered validated for clinical use. Synthetic Instrument Validation offers a feasible approach to preliminary health care instrument development while preserving key elements of content validation.