Computational ethnography and public health: Scaling and deepening lived experience research on social determinants of health with large language models

Comprehensive Summary

This study, presented by Francis McKay, evaluates the potential of “computational ethnography”, which uses computational tools like Large Language Models (LLMs) to bridge the tension in public health between ethnographic research based on lived experiences and insights generated from quantitative analyses. McKay conceptually explores the use of LLMs for two main purposes, which are LLM-based interviewing and augmented analysis of unstructured ethnographic data. As a result, the research concludes that LLMs can simultaneously scale and deepen ethnographic research, thus improving the identification of social determinants of health while preserving essential contextual understanding. However, there are inevitably significant ethical, epistemic, and technological challenges to overcome in order to mitigate considerations such as data privacy and manifested biases.

Outcomes and Implications

With the divide in public health in small-scale qualitative ethnographic research and quantitative analysis of large, structured datasets, this research offers a strategic method to translate individual narratives into actionable, population-level intervention strategies. By leveraging LLMs to scale-up ethnographic insights, these computational methods can better map out complex social determinants of health that are often missed by standard quantitative data. Therefore, computational ethnography has the clinical potential to generate contextualized insights for highly targeted preventative care. Since this research is conceptual and focuses on outlining the future challenges of computational ethnography, additional research is needed to advance towards future clinical implementation.