arXiv (All)
open access
Evaluating Fine-Tuned and Base Language Models in Maternal and Vaccination Healthcare for African Settings
Background: Large language models (LLMs) can improve healthcare information delivery in low-resource settings but may produce inaccurate or culturally inappropriate advice. This study evaluated domain-specific fine-tuning for maternal health and vaccination in Nigeria. Objective: To compare HelpMum'
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.#143132
- Effect of Implementing Prevention Measures Through Personalized Information in Emergency Departments on Adherence to Prevention Measures#1139141
- Potential and limitations of a large language model in the statistical review of comparative categorical data: an exploratory study of structured prompt-guided approach.#427946
- Acceptability and Effectiveness of Text Message Reminders to Improve Patient Attendance During the Sociopolitical Crisis in Haiti: Telephone-Based Survey#15421
- Automatically Finding and Validating Unexpected Side-Effects of Interventions on Language Models#158552
Record · ID 1036794
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive