Methods, Modeling, and Findings

This unit examines the research methods, modeling techniques, and empirical findings presented in the text, with emphasis on how large language models (LLMs) were applied and evaluated in health contexts. Students will move from conceptual understanding to hands-on replication of methods and critical interpretation of results, preparing them for application and ethical evaluation in the following unit.

Learning Objectives

• Analyze the role and structure of large language models as presented in the book by comparing architectures, training/data considerations, and known limitations.
• Apply described research methods to design and execute a simplified study workflow, including data collection, preprocessing, and selection of appropriate evaluation metrics.
• Demonstrate the construction, training, and validation of predictive models from unit materials by performing model selection, cross-validation, and performance measurement on provided datasets or simulations.
• Evaluate the study findings by interpreting results, identifying potential sources of bias or error, assessing statistical significance, and proposing evidence-based recommendations for health-related applications.