The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

Abstract

Abstract Background The relationship between the impact of anti-involution training on critical thinking and its propensity indicators among young healthcare professionals in dental outpatient clinics remains to be determined. Therefore, this study aimed to investigate these associations and develop an interpretable machine learning (ML) model to assess their predictive value in enhancing critical thinking through anti-involution training. Methods A cross-sectional survey encompassing 114 participants was conducted. Spearman correlation analysis was utilized to evaluate the association between propensity indicators and the enhancement of critical thinking through anti-involution training. Subsequently, the data underwent normalization utilizing the “MinMaxScaler” technique, while balancing was achieved by applying the synthetic minority oversampling technique (SMOTE). Following this, predictors were identified using the most minor absolute shrinkage and selection operator (LASSO) regression. Next, diverse machine learning algorithms constructed an individual prediction model to enhance critical thinking through anti-involution training. The prediction model's performance was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The Shapley additive interpretation (SHAP) method was utilized to interpret the ML model. Results Truth-seeking, analytical thinking, and inquisitiveness were identified as predictive factors for enhancing critical thinking. A Random Forest algorithm-based model incorporating these variables yielded favorable results: AUC = 0.889 (95% CI: 0.839–0.937), accuracy = 0.850, sensitivity = 0.855, specificity = 0.933. Conclusion The inclinations toward truth-seeking, analytical thinking, and inquisitiveness significantly correlate with the effectiveness of anti-involution training in enhancing critical thinking. Our simplified ML-based predictive model allows for preliminary forecasting, enabling early intervention and guidance for learners facing difficulties in improving critical thinking.