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社会系统计算科学（英文）
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2024年4期
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GPT Models Can Perform Thematic Analysis in Public Health Studies,Akin to Qualitative Researchers
GPT Models Can Perform Thematic Analysis in Public Health Studies,Akin to Qualitative Researchers
摘要
Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researchers in extracting themes from interview transcripts.Carefully engineered prompts were used to sequentially extract and synthesize transcripts into a concise set of study-level themes relevant to the study's goals.An evaluation using a 5-point Likert scale(0-4)assessed GPT-generated themes across 11 published studies based on four criteria:succinctness,alignment with researcher-identified themes,quality of explanations,and relevance of quotes.Across all four criteria,the scores averaged 3.05(95%Confidence Interval(CI):[2.93,3.16]).Our findings indicate that at least half of the GPT-generated themes align with those in published studies,exhibiting succinctness with minimal repetition,substantial depth of explanations,and relevant quotations.Despite these promising results,practices such as complementing outputs with field-specific knowledge are recommended.
Author：
Yuyi Yang
Charles Alba
Chenyu Wang
Xi Wang
Jami Anderson
Ruopeng An
作者单位：
Division of Computational and Data Science's,Washington University in St.Louis,St.Louis 63130,MO,USA
School of Medicine,Washington University in St.Louis,St.Louis 63130,MO,USA
Brown School,Washington University in St.Louis,St.Louis 63130,MO,USA
Implementation Science Center for Cancer Control,Washington University in St.Louis,St.Louis 63130,MO,USA
Constance and Martin Silver Center on Data Science and Social Equity,Silver School of Social Work,New York University,New York 10003,NY,USA
年，卷(期)：
2024, 5(4)
在线出版日期：
2025年6月13日
