Deductively coding psychosocial autopsy interview data using a few-shot learning large language model.

ABSTRACT:

Background

Psychosocial autopsy is a retrospective study of suicide, aimed to identify emerging themes and psychosocial risk factors. It typically relies heavily on qualitative data from interviews or medical documentation. However, qualitative research has often been scrutinized for being prone to bias and is notoriously time- and cost-intensive. Therefore, the current study aimed to investigate if a Large Language Model (LLM) can be feasibly integrated with qualitative research procedures, by evaluating the performance of the model in deductively coding and coherently summarizing interview data obtained in a psychosocial autopsy.

Methods

Data from 38 semi-structured interviews conducted with individuals bereaved by the suicide of a loved one was deductively coded by qualit

SUBMITTER: Balt E 

PROVIDER: S-EPMC11879832 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature