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Updated: Jun 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Bayesian data augmentation methods for the synthesis of qualitative and quantitative research findings
Jamie L Crandell1, Corrine I Voils, Yunkyung Chang
1Department of Biostatistics, University of North Carolina at Chapel Hill, #7460 Carrington Hall, Chapel Hill, NC 27599, USA; School of Nursing, University of North Carolina at Chapel Hill, #7460 Carrington Hall, Chapel Hill, NC 27599, USA.
Abstract:
The possible utility of Bayesian methods for the synthesis of qualitative and quantitative research has been repeatedly suggested but insufficiently investigated. In this project, we developed and used a Bayesian method for synthesis, with the goal of identifying factors that influence adherence to HIV medication regimens. We investigated the effect of 10 factors on adherence. Recognizing that not all factors were examined in all studies, we considered standard methods for dealing with missing data and chose a Bayesian data augmentation method. We were able to summarize, rank, and compare the effects of each of the 10 factors on medication adherence. This is a promising methodological development in the synthesis of qualitative and quantitative research.
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