| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2024-11-27 |
| タイトル |
|
|
タイトル |
A mixed-methods study comparing human-led and ChatGPT-driven qualitative analysis in medical education research |
|
言語 |
en |
| 著者 |
Kondo, Takeshi
Miyachi, Junichiro
Jönsson, Anders
Nishigori, Hiroshi
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
|
|
権利情報Resource |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International |
|
言語 |
en |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
qualitative study |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
medical education |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
ChatGPT |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
artificial intelligence |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
large language models |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Qualitative research, used to analyse non-numerical data including interview texts, is crucial in understanding medical education processes. However, it is often complex and time-consuming, leading to an interest in technology for streamlining the analysis. This study investigated the applicability of ChatGPT, a large language model, in thematic analysis for medical qualitative research. Previous research has used ChatGPT to explore the deductive process as a qualitative study. This study evaluated thematic analysis including the inductive process by ChatGPT with reference to human qualitative analysis. A convergent design mixed-methods study was used. Using a thematic analysis approach, ChatGPT (model: GPT-4) analysed some interview data from a previously published medical research article. The assessors evaluated the qualitative analysis of ChatGPT using human qualitative analysis as a benchmark. Three assessors compared the human-conducted and ChatGPT-driven qualitative analyses. ChatGPT scored higher in most aspects but showed variable transferability and mixed depth scores. In the integrated analysis including qualitative data, six themes were identified: superficial similarity of results with human analysis, good first impression, explicit association with data and process, contamination by directions in prompts, deficiency of thick descriptions based on context and research questions, and lack of theoretical derivation. ChatGPT excels at extracting key data points and summarising information; however, it is prone to prompt contamination, which necessitates careful scrutiny. To achieve deeper analysis, it is essential to supplement the research context with human input and explore the theoretical framework. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Nagoya University Graduate School of Medicine, School of Medicine |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
departmental bulletin paper |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| ID登録 |
|
|
ID登録 |
10.18999/nagjms.86.4.620 |
|
ID登録タイプ |
JaLC |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
URI |
|
|
関連識別子 |
https://www.med.nagoya-u.ac.jp/medlib/nagoya_j_med_sci/864.html |
| 助成情報 |
|
|
|
識別子タイプ |
Crossref Funder |
|
|
助成機関識別子 |
https://doi.org/10.13039/501100001691 |
|
|
助成機関名 |
日本学術振興会 |
|
|
言語 |
ja |
|
|
助成機関名 |
Japan Society for the Promotion of Science |
|
|
言語 |
en |
|
|
研究課題番号 |
21K10372 |
|
|
研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K10372/ |
|
|
研究課題名 |
デジタルテクノロジーについていけない教職員の苦悩 |
|
|
言語 |
ja |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0027-7622 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2186-3326 |
| 書誌情報 |
en : Nagoya Journal of Medical Science
巻 86,
号 4,
p. 620-644,
発行日 2024-11
|
08_Kondo.pdf (761.6 KB)
.png)
