Purpose: High-quality narrative feedback is essential for competency-based medical education, but manual evaluation of feedback is time-consuming and subjective. This research aims to develop and validate a machine learning (ML)-based model to automate the bulk evaluation of feedback quality from anesthesiology residency program instructors.
Methods: Using 990 narrative feedback entries from October 2023 to November 2025 at the First Affiliated Hospital of Ningbo University, we conducted training and validation. An additional 587 feedback records from Ningbo Li HuiLi Hospital were used as an external test set. Text processing employed the jieba Chinese word segmenter combined with an anesthesia-specific vocabulary database to extract TF-IDF and manual features. Data imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE). Logistic regression (LR), random forests (RF), and Gradient Boosting Machine (GBM) were used for training and validation. Model performance was measured using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, cross-validation accuracy, precision, recall, and F1 score.
Results: In internal training, LR performed optimally, demonstrating the best overall performance (F1 score: 0.941) and stability (cross-validation accuracy: 0.925 ± 0.026), along with the highest precision (0.906). In external testing, the LR model achieved an overall accuracy of 0.840 (95% CI: 0.808-0.867), with high recall (0.956) and moderate precision (0.636) for identifying high-quality feedback, yielding an F1 score of 0.764 and an AUC of 0.729.
Conclusion: This study successfully developed and externally validated a machine learning-based model for automated feedback quality assessment in Chinese anesthesiology residency training. With its high recall and stable internal performance, the model may serve as a screening tool to support competency-based medical education by enabling batch evaluation of narrative feedback.
Keywords: educational improvement; feedback quality; machine learning; medical education; natural language processing.
© 2026 Yao et al.