Large language models for analyzing open text in global health surveys: why children are not accessing vaccine services in the Democratic Republic of the Congo.

ABSTRACT:

Background

This study evaluates the use of large language models (LLMs) to analyze free-text responses from large-scale global health surveys, using data from the Enquête de Couverture Vaccinale (ECV) household coverage surveys from 2020, 2021, 2022 and 2023 as a case study.

Methods

We tested several LLM approaches consisting of zero-shot and few-shot prompting, fine-tuning, and a natural language processing approach using semantic embeddings, to analyze responses on the reasons caregivers did not vaccinate their children.

Results

Performance ranged from 61.5% to 96% based on testing against a curated benchmarking dataset drawn from the ECV surveys, with accuracy improving when LLMs were fine-tuned or provided examples for few-shot learning. We show that even with as

SUBMITTER: Burstein R 

PROVIDER: S-EPMC12406778 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature