Analysis and Improvement Strategies of Clinical Fellowship Feedback Based on Text Mining

Corresponding author: JIANG JIAN, trywczy@163.com

DOI: 10.12201/bmr.202512.00064

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  Abstract: Purpose/Significance To mine textual evaluations from clinical fellows, assess the current status of training, and provide empirical evidence for targeted improvements in educational management Method/Process Textual evaluation data were collected from clinical fellows at a tertiary teaching hospital in Beijing from September 2023 to September 2025. BERTopic and a Chinese pre-trained language model were used to perform topic modeling and sentiment analysis. An improved Importance–Performance Analysis(IPA) approach was then applied to construct a “topic attention–sentiment satisfaction” matrix, and keyword co-occurrence analysis was conducted on negative texts within low-satisfaction topics. Result/Conclusion Six topics were identified and classified into four domains: strengths to be maintained, key areas for improvement, areas for moderate enhancement, and secondary optimization areas. Based on these findings, it is recommended that clinical fellowship management focus on consolidating faculty strengths, increasing opportunities for clinical and research practice, optimizing management processes and learning support environment.

  Key words: clinical fellowship; text mining; teaching management

  Submit time: 22 December 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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