Harnessing AI Agents to Advance Research on Refugee Child Mental Health
This provides a scalable strategy to assist policymakers and practitioners in addressing refugee child mental health, though it is incremental as it applies existing AI methods to a new domain.
The research tackled the problem of processing unstructured refugee health data to improve child mental health insights by comparing two RAG pipelines, finding that DeepSeek R1-7B outperformed Zephyr-7B-beta with an answer relevance accuracy of 0.91.
The international refugee crisis deepens, exposing millions of dis placed children to extreme psychological trauma. This research suggests a com pact, AI-based framework for processing unstructured refugee health data and distilling knowledge on child mental health. We compare two Retrieval-Aug mented Generation (RAG) pipelines, Zephyr-7B-beta and DeepSeek R1-7B, to determine how well they process challenging humanitarian datasets while avoid ing hallucination hazards. By combining cutting-edge AI methods with migration research and child psychology, this study presents a scalable strategy to assist policymakers, mental health practitioners, and humanitarian agencies to better assist displaced children and recognize their mental wellbeing. In total, both the models worked properly but significantly Deepseek R1 is superior to Zephyr with an accuracy of answer relevance 0.91