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

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model Yuxiang Chen, Anna Zhao, Haoran Yang, Tingting Chen, Xianqi Rao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3908847/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. anti-involution training Critical thinking Propensity indicators Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Critical thinking is a rational and reflective mode manifested when confronted with evidence, situations, criteria, and concepts (Sari et al., 2021 ). As healthcare professionals are intricately involved in the lives of individuals, there is often a need to make judicious, prompt, and rational judgments based on professional knowledge and the best available evidence, coupled with clinical realities and patient preferences. This necessitates the possession of providers to minimize errors and optimize clinical decision-making (Dissen, 2023 ). The phenomenon of involution, which has been highly scrutinized in recent years by scholars, is a complex, evolving, and multidimensional concept. It refers to the situation where individuals within a profession exert increasing efforts to compete for limited resources, decreasing the individual’s "benefit-to-effort ratio." This state is characterized by"internal consumption" or "irrational competition" (Dou et al., 2022 ; McCullough, 2019 ). The high level of involution in a societal structure generates social anxiety, negatively impacting the development of various professions (Chen et al., 2022 ; D. Yi et al., 2022 ). Therefore, it becomes necessary for all professions to strive towards anti-involution. In the healthcare industry, a phenomenon known as involution also exists among healthcare professionals, which hurts their professional growth and the delivery of medical services. This is particularly evident in medical research, where involution is pronounced. Although numerous research achievements are produced, they contribute little to advancing the research field, as scientific inquiry often remains confined to a simplistic level of self-depletion and repetition (Gan, 2023 ). Therefore, it is crucial to provide anti-involution training for healthcare professionals. Cultivating anti-involution and critical thinking is essential in developing healthcare professionals, and exploring the relationship between them is vital in their training. Research indicates that both anti-involution training and critical thinking are associated with innovative thinking (Handayani & Wulandari, 2021 ; Mu, 2021 ; Zeng, 2023 ). It can be shown that there is an indirect correlation between anti-involution training and critical thinking. Still, no direct correlation between the two has been reported in the literature.The effects of anti-involution training, young healthcare professionals in dental outpatient clinics, and enhancing critical thinking can be understood as components of the teaching process, learners, and learning outcomes. Existing studies have shown that factors influencing learner performance are multifaceted and can be classified into two categories of indicators (Al-Rahmi & Zeki, 2017 ; Mosimege & Winnaar, 2021 ; Uwineza et al., 2023 ). The first category refers to predispositional indicators, representing the attributes learners possess upon entering the learning environment (static indicators). The second category consists of behavioral performance indicators, which reflect learners’ performance during the learning process (dynamic indicators) (Efthimiopoulos & Mylonakou-Keke, 2012 ; Magsino, 2021 ; Müller & Seufert, 2018 ; Nickl et al., 2022 ). To date, no studies have reported on the factors influencing the effectiveness of anti-involution training in improving critical thinking. In this study, we aim to explore the correlation between anti-involution training and propensity indicators of young healthcare professionals in dental outpatient clinics in relation to the enhancement of critical thinking. Using machine learning algorithms, we will identify the key factors influencing the effectiveness of anti-involution training in improving critical thinking and construct a machine learning model to quantify the impact of these factors. This will enable educational professionals to intervene and guide learners at risk of poor learning performance in advance, thus maximizing the improvement of critical thinking among young healthcare professionals in dental outpatient clinics through anti-involution training. Additionally, we will utilize the SHAP method to enhance the Interpretability of the model, ensuring that the results are easily understood and applicable. Materials and Methods Participants This cross-sectional study was conducted in Kunming, China, from January to December 2020. The study participants comprised young healthcare professionals under 40 from five outpatient departments of the Affiliated Stomatological Hospital of Kunming Medical University. One hundred fourteen individuals participated in the anti-involution training before the study, while 91 individuals participated after the training. A total of 205 questionnaires were returned, and after excluding cases with missing essential information and obvious logical errors, 182 questionnaires were deemed suitable for analysis, resulting in an effective response rate of 88.78%. Informed consent was obtained from all study participants. Data collection The Chinese version of the Critical Thinking Disposition Inventory (CTDI-CV) was utilized as the survey tool in this study. The overall Cronbach’s α coefficient of the CTDI-CV was 0.90. The inventory consists of positive and negative items, rated on a 6-point Likert scale. The scoring for positive items ranges from 1 (strongly disagree) to 6 (strongly agree), while the scoring for negative items is the reverse (Zhang et al., 2017 ). The CTDI-CV encompasses seven traits comprising 70 items: truth-seeking, analyticity, open-mindedness, systematicity, inquisitiveness, self-confidence in critical thinking, and cognitive maturity. Each trait includes ten items, resulting in a total score range of 420 (Huang et al., 2021 ). Before completing the questionnaires, the research team provided detailed explanations to the participants regarding the purpose, significance, instructions, precautions, and deadline for questionnaire submission. Before the anti-involution training, baseline measurements were taken from young healthcare professionals under 40 in five outpatient departments of the Affiliated Stomatological Hospital of Kunming Medical University. The training, led by the department directors, lasted for one year and included a series of lectures on involution-related theories. Regular assessments on involution-related knowledge points and measurements of critical thinking disposition were conducted every three months. The data collected in this study encompassed two groups of factors: (1) participants’ propensity indicators, including initial knowledge and skills and inherent indicators. Initial knowledge and skills involved the pre-training total score of CTDI-CV, as well as the total scores of the seven traits of CTDI-CV before the training, including truth-seeking, analyticity, open-mindedness, systematicity, inquisitiveness, self-confidence in critical thinking, and cognitive maturity. Inherent indicators included gender and profession. (2) Conclusion: The effects of anti-involution training on enhancing critical thinking among young healthcare professionals in dental outpatient clinics. The difference between the two total scores of CTDI-CV was used to measure the effects of anti-involution training on enhancing critical thinking among young healthcare professionals in dental outpatient clinics. A difference ≥ 25 indicated a significant improvement, while a difference < 25 indicated no significant improvement. Based on this criterion, the participants were divided into the significantly improved and non-significantly improved groups. statistical analysis Analytical methods Descriptive analysis was conducted using SPSS (version 26.0), while other statistical analysis procedures were performed using R (version 3.6.1) and Python (version 3.4.3). Mann-Whitney U or Chi-square tests were used to analyze the demographic differences between the significantly improved and non-significantly improved groups, with continuous variables represented as median and interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical variables were presented as counts and percentages and compared using the Chi-square test. Paired samples t test was used to analyze the characteristics of the effects of anti-involution training on critical thinking among young healthcare professionals in dental outpatient clinics. Spearman correlation analysis and multivariate logistic regression were employed to evaluate the relationships between predispositional indicators and the effects of anti-involution training on critical thinking among young healthcare professionals in dental outpatient clinics, as well as the influence of the former on the latter. A two-sided p-value less than 0.05 was considered statistically significant. Data preprocessing In order to mitigate the influence of indicator dimensions and magnitudes on the study results and to facilitate comparability of data across each test index, the test indicators were subjected to normalization. The normalization process employed in this study was "MinMaxScaler," which linearly transformed the original data to ensure that the resulting values fell within the range of [0, 1]. Additionally, data balancing was performed using the “SMOTE” technique, which equalized the proportion of minority and majority classes to a ratio of 1:1 (Nguyen et al., 2023 ). Variable selection and prediction model establishment We employed Lasso regression for feature selection to select the most predictive and relevant features to improve model performance and reduce computational burden (Bainter et al., 2023 ). Lasso regression compresses the coefficients of variables in the regression model by generating a penalty function to prevent overfitting and address severe collinearity issues (Genç & Özkale, 2022 ; Guler & Guler, 2021 ). Lasso regression was performed with 10-fold cross-validation using the “glmnet 4.1.2” R package. The study participants were divided into training and validation sets at 7:3. On the training set, we fitted nine machine learning classifiers: logistic regression, XGBoost, LightGBM, random forest, AdaBoost, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), multilayer perceptron (MLP), and K-nearest neighbors (KNN). XGBoost was built using the “xgBoost 1.2.1” Python package, while LightGBM was built using the “lightgbm 3.2.1” Python package. The other machine-learning algorithms were built using the “sklearn 0.22.1” Python package. Model performance evaluation Model performance was evaluated based on its discriminative, calibration, clinical applicability, and generalizability. Resampling was employed to ensure consistency of training samples across multiple models, enabling comparison of the results. The best model selection was based on analyzing the importance of metrics across models on both training and testing sets. The area under the curve (AUC), which is commonly used to characterize the diagnostic accuracy of a test or predictive model, was constructed using the “sklearn 0.22.1” Python package (Obuchowski & Bullen, 2018 ). Decision curve analysis (DCA) plots, representing a decision analysis technique with substantial advantages in assessing the clinical utility of models, were generated using the “rmda 1.6” R software (Vickers & Elkin, 2006 ). Calibration curves, which assess the predictive ability of models, were constructed using the “sklearn 0.22.1” Python package (Fenlon et al., 2018 ). Precision-recall (PR) curves, widely used in the field, were plotted using the “scikit 0.22.1” Python package to evaluate model performance (Li & Guo, 2021 ). Once the most helpful model was determined through comprehensive comparison, all eligible participants were reclassified into training, validation, and testing sets at 6:2:2. The best model underwent 5-fold cross-validation on the training set and was evaluated on the validation and testing sets. Learning curves were generated using the “scikit 0.22.1” Python package to assess the adaptability and stability of the model on the training and validation sets (Belkin et al., 2019 ). Interpretability of the model We utilized SHAP values to explain the optimal machine learning model. SHAP, derived from Shapley values in game theory, quantifies the contribution of each feature in the model to the final prediction. The SHAP summary plot combines feature importance and feature effects. Each point on the SHAP dependence plot represents a feature and its Shapley value for a specific instance. In SHAP explanations, feature attributes like Shapley values are visualized as "forces" that contribute to increasing or decreasing the prediction. Predictions are based on a baseline, and the baseline for Shapley values is computed as the average of all predictions. The "SHAP 0.39.0" Python package generated visual explanations of SHAP values for model importance and contributions (Futagami et al., 2021 ). Results Characteristics of the study participants The demographic characteristics of the participants are presented(Table 1) . In this study, we excluded a total of 23 participants with incomplete data, a total of 91 participants were enrolled in this study, with 62 participants who underwent anti-involution training showing no significant improvement in critical thinking skills and 29 participants showing significant improvement. Detailed data for 91 participants can be found in Supplementary Data S1. When comparing the non-significant improvement group to the significant improvement group, significant differences (p < 0.05) were observed in several critical thinking aspects, including CTDI-CV total score before training (p = 0.002), truth-seeking (p = 0.007), analyticity (p = 0.008), systematicity (p = 0.015), self-confidence in critical thinking (p = 0.047), inquisitiveness (p = 0.027), and cognitive maturity (p = 0.047). However, there were no significant differences (p > 0.05) in terms of profession (p = 0.378), gender (p = 0.953), and open-mindedness (p = 0.185). Table 1 Characteristics in non-significantly improved group and significantly improved group Variable non-significantly improved group significantly (n=62) significantly improved group(n=29) p profession,n(%) Stomatology 24(38.710%) 10(34.483%) 0.378 b Nursing 36(58.065%) 16(55.172%) Other 2(3.226%) 3(10.345%) gender Male 11(17.742%) 5(17.241%) 0.953 b female 51(82.258%) 24(82.759%) CTDI-CV total score before training, median [IQR] 263.00[249.00,279.00] 244.00[212.00,256.00] 0.002 a truth-seeking, median [IQR] 39.00[35.00,41.00] 33.00[30.00,40.00] 0.007 a analyticity, median [IQR] 35.00[32.00,39.00] 31.00[28.00,36.00] 0.008 a systematicity, median [IQR] 37.00[35.00,40.00] 33.00[30.00,37.00] 0.015 a self-confidence in critical thinking, median [IQR] 35.00[31.00,40.00] 30.00[25.00,37.00] 0.047 a inquisitiveness, median [IQR] 37.00[31.00,40.00] 33.00[27.00,38.00] 0.027 a cognitive maturity, median [IQR] 40.00[36.00,47.00] 37.00[33.00,42.00] 0.047 a p a values are calculated from the Mann-Whitney U test, and p b was obtained by x 2 -tests. p < 0.05 indicates statistical significance. Numbers in bold mean statistical significance. The effects of anti-involution training on the enhancement of critical thinking among young healthcare professionals in dental outpatient clinics The effects of anti-involution training on the enhancement of critical thinking among young healthcare professionals in dental outpatient clinics are presented(Table 2 and Fig. 1). The results reveal that, among the seven characteristics of critical thinking, there is a slight decrease in the average score of self-confidence in critical thinking. The average scores of the remaining six characteristics and the total score have all shown slight improvements. Notably, there is a statistically significant difference in the average score increase for cognitive maturity, which is 3.978±-0.014 (P=0.004). Additionally, the average score increase for the total score is 11.813±6.752 (P=0.001), signifying a statistically significant difference. Table 2 Scores of each critical thinking feature before and after anti-involution training（±s） Variable before anti-involution training (n=91) After anti-involution training(n=91) Increased scores P truth-seeking 36.989±6.918 38.132±6.843 1.143±-0.075 0.287 open-mindedness 37.308±6.458 38.341±6.469 1.033±0.011 0.343 analyticity 33.681±5.88 34.593±5.89 0.912±0.01 0.277 systematicity 35.198±7.14 36.121±7.082 0.923±-0.058 0.388 self-confidence in critical thinking 33.286±7.605 31.275±7.603 -2.011±-0.002 0.075 inquisitiveness 33.835±8.148 34.802±8.133 0.967±-0.015 0.427 cognitive maturity 39.637±8.846 43.615±8.833 3.978±-0.014 0.004 ** CTDI-CV total scores 249.934±39.513 261.747±46.266 11.813±6.752 0.001 *** * * * , * * , * represent 1% , 5% , 10% significance levels respectively. Numbers in bold mean statistical significance. Correlation analysis of the effects of anti-involution training on critical thinking and propensity indicators among young healthcare professionals in dental outpatient clinics As shown in Table 3, there is a negative correlation between truth-seeking, analyticity, systematicity, self-confidence in critical thinking, inquisitiveness, cognitive maturity, and CTDI-CV total scores with the effects of anti-involution training on the enhancement of critical thinking (truth-seeking: r = -0.284, p < 0.05; analyticity: r = -0.287, p < 0.05; systematicity: r = -0.258, p < 0.05; self-confidence in critical thinking: r = -0.210, p < 0.005; inquisitiveness: r = -0.234, p < 0.05; cognitive maturity: r = -0.210, p 0.05). However, there is no correlation between profession, gender, open-mindedness, and the effects of de-individuation training on the enhancement of critical thinking (profession: r = 0.078, p > 0.05; gender: r = -0.006, p > 0.05; open-mindedness: r =- 0.140, p > 0.05). Table 3 The Spearman's association analysis among Tendency indicators and the effects of anti-involution training on critical thinking Variable The effects of anti-involution training on the enhancement of critical thinking r p profession 0.078 0.461 gender -0.006 0.954 truth-seeking -0.284 0.006 ** open-mindedness -0.140 0.185 analyticity -0.278 0.008 ** systematicity -0.258 0.014 ** self-confidence in critical thinking -0.210 0.046 ** inquisitiveness -0.234 0.026 ** cognitive maturity -0.210 0.046 ** CTDI-CV total scores -0.327 0.002 ** The p-values were calculated using the Spearman correlation analysis.* * * , * * , * represent 1% , 5% , 10% significance levels respectively. Numbers in bold mean statistical significance. Identifying predictors LASSO regression analysis was conducted on the remaining independent variables with a tophus as the dependent variable (Fig. 2). LASSO can compress variable coefficients to prevent overfitting and solve severe collinearity problems (F. Yi et al., 2023). The results showed that (lambda with minimum mean square error = 0.06) 10 independent variables were reduced to 4, including truth-seeking, analyticity, and inquisitiveness(Fig. 2a,b) Comprehensive Analysis of Classified Multi-Mode Multiple ML models were utilized for data sample classification: XGBoost, logistic regression, LightGBM, Random Forest, AdaBoost, KNN, SVM, GNB, and MLP. A 5-fold cross-validation approach was performed to validate all models. The AUC value was employed to evaluate model predictions (Obuchowski & Bullen, 2018). Results indicated that Random Forest, XGBoost, and AdaBoost exhibited superior performance on the training set, whereas Random Forest, KNN, and LightGBM achieved the highest performance on the validation set (Fig. 3a,b);see more details in Supplemental Table S1and Table S2.The AUC value primarily assesses the predictive accuracy of the models without providing information on their clinical utility or preferences (Muschelli, 2020; Obuchowski & Bullen, 2018). Therefore, we analyzed the PR curve, calibration curve, and DCA. The DCA curve illustrated the favorable clinical applicability of the Random Forest model (Fig. 3c). The calibration curve suggested that the Random Forest and KNN models provided more accurate predictions (Fig. 3d). The Random Forest model displayed exceptional performance in the training and validation sets, generating the highest average precision (AP) value on the validation set (Fig. 3e,f). Considering all factors, the comprehensive analysis suggests that Random Forest can be considered the optimal model. The best model construction and evaluation The Random Forest model was trained using the training set with 5-fold cross-validation. The results revealed that the average AUC for the training set was 0.912 (0.843-0.980), while for the validation set, it was 0.889 (0.740-0.990). The AUC for the test set was 0.868 (0.731-1.000) (Fig. 4a-c);see more details in Supplemental Table S3.Considering that the performance of the validation set did not exceed or differ by less than 10% from the test set based on the AUC metric, it can be concluded that the model was successfully fitted. The calibration curve further demonstrated that the Random Forest model was accurate and predictive (Fig. 4d). These outcomes suggest that the Random Forest model can be utilized for classification modeling tasks on the dataset. The SHAP to Interpret ML model We employed SHAP to interpret the model to provide an intuitive explanation of the selected variables. The distribution of results for each participant is illustrated (Fig. 5a).In each feature importance line, different colored dots represent the attribution of results by all participants. Red dots represent high likelihood values, while blue dots represent low likelihood values. The variables inquisitiveness, truth-seeking, and analyticity ability of participants before anti-involution training are all negatively correlated with the results.Three risk factors were ranked by evaluating the average absolute SHAP values(Fig. 5b). The x-axis represents the importance of the predictive model measured by the SHAP values. The feature importance of the Random Forest model, from high to low, was observed as inquisitiveness, truth-seeking, and analyticity. Furthermore, we provide two typical examples to illustrate the Interpretability of the model. For one participant, there was no significant improvement observed in critical thinking, as indicated by a lower SHAP prediction score of 0.15 (Fig. 5c).In contrast, another participant exhibited significant improvement in critical thinking, as evidenced by a higher SHAP score of 0.95 (Fig. 5d). Discussion Critical thinking is both a skill and a mindset. It refers to an individual’s ability to analyze and solve problems in complex environments using existing knowledge and experience and the capacity to make decisions based on reflection, analysis, and reasoning (Ennis, 2018 ). Strong critical thinking skills are a fundamental ability and quality that healthcare professionals should possess (ŽivkoviĿ, 2016 ). The findings of this study indicate that the overall level of critical thinking abilities among young dental outpatient healthcare workers is moderate to low, falling short of the criterion for a positive critical thinking tendency (Cisneros, 2009 ; Gupta et al., 2012 ). Therefore, there is still ample room for improvement. Involvement is a state of internal consumption or irrational competition, and highly involved social structures hurt the development of various professions within society. The “internalization” of the medical industry has brought about negative consequences for the growth of healthcare professionals and the execution of medical activities, which may hinder improving critical thinking. Previous studies have shown that critical thinking and innovative thinking complement each other, and the key to anti-involution lies in enhancing innovative thinking (Arce-Saavedra & Blumen, 2022 ; Jing, 2021 ; Ramírez-Montoya et al., 2022 ; Sharma & Priyamvada, 2022 ). This study explores the effects of anti-involution training on enhancing critical thinking among young dental outpatient healthcare workers. The results indicate that, aside from a slight decrease in the average score for self-confidence in critical thinking, there were small but significant improvements in the average scores for the other six characteristics of critical thinking and the overall score. This suggests that anti-involution training has a specific positive effect on the critical thinking of healthcare professionals, providing additional evidence for the direct relationship between anti-involution training and critical thinking. However, this study also has some limitations. Firstly, there are various methods of anti-involution training. The single training method used in this study can only cover some aspects of anti-involution training. Secondly, there were fluctuations in the participants throughout the study period, resulting in the loss of 25 participants. To achieve a one-to-one match between the pre-and post-training samples, the data of the lost participants were excluded, which may lead to an insufficient sample size. Additionally, the study lasted one year, during which confounding factors may have been present, potentially introducing errors in the experimental results. As an instructional practice, improving the effectiveness of anti-involution training is a concern for scholars. The factors influencing learners’ performance are complex, and it is challenging to predict them based on a single factor. Previous research has shown a strong correlation between dispositional indicators and learners’ performance, particularly regarding initial knowledge, skills, and inherent indicators (Considine & Zappalà, 2002 ; Deakin Crick et al., 2015 ; Friedman & Mandel, 2011 ). This study explores the correlation between improving critical thinking among young dental outpatient healthcare workers through anti-involution training and their dispositional indicators. The correlation analysis results indicate a significant statistical significance between improving critical thinking and the pre-training total score, pre-training analytical ability, self-confidence, cognitive maturity, systematic thinking ability, and truth-seeking of critical thinking. These dispositional indicators are closely related to innovative thinking, thus further confirming the close relationship between innovative thinking and critical thinking, which aligns with the previous research conclusion of our research team (Syed Marzuki et al., 2020 ). However, apart from the factors in this study, dispositional indicators include other aspects such as family background, initial grades, expectations or satisfaction with education, and goal expectations (Considine & Zappalà, 2002 ; Deakin Crick et al., 2015 ; Friedman & Mandel, 2011 ). The relationship between these indicators and the improvement of critical thinking among young dental outpatient healthcare workers through anti-involution training requires further research for verification. Traditionally, education research has been relatively limited regarding research models, relying primarily on small-scale data and traditional statistical methods. Research data is often obtained through questionnaires and self-reports, with relatively limited sample sizes. Moreover, traditional statistical analysis methods have limitations in revealing complex relationships between variables. The widespread application of machine learning methods in various fields has gained attention and usage in social science research (Macalli et al., 2021 ). Machine learning has significant advantages in studying factors that influence learning performance. It excels in handling large datasets and extracting potential underlying connections that traditional methods may overlook. Recently, scholars have begun to use supervised machine learning methods such as Support Vector Machines (SVM), Random Forests, Deep Neural Networks (DNN), XGBoost, etc., to address classification and prediction problems in the field of educational research (Vergaray et al., 2022 ). In this study, the Random Forest algorithm was ultimately selected to construct a predictive model for the effect of anti-involution training on the improvement of critical thinking among young dental outpatient healthcare workers. The Random Forest model is a widely used machine learning model. An ensemble learning method combines multiple decision trees to make predictions. Each decision tree in the Random Forest is built independently using a random subset of the dataset and random features, which helps reduce overfitting and improve generalization (Rigatti, 2017 ). One of the key advantages of the Random Forest model is its ability to handle high-dimensional datasets and maintain good performance. It can handle large-scale datasets with numerous features without sacrificing accuracy (Malhotra & Karanicolas, 2020 ). The Random Forest model is known for its robustness and resistance to noise and outliers, making it suitable for various real-world applications. The Random Forest model can also estimate feature importance. By calculating the average decrease in impurity or information gain caused by each feature, it can identify the most influential features for prediction. This feature importance analysis can be valuable for feature selection and understanding the underlying relationships within the dataset. Furthermore, the Random Forest model can handle missing data and maintain accuracy even when a significant portion is missing. It can utilize the available information from other features to fill in missing values and make reliable predictions (Antoniadis et al., 2021 ). The Random Forest model is computationally efficient and can be parallelized for faster training and prediction. It can process large datasets efficiently using multi-core processors and distributed computing frameworks. In summary, the Random Forest model is a robust machine-learning algorithm that combines the predictions of multiple decision trees to provide accurate and robust results (Su et al., 2022 ). Its ability to handle high-dimensional datasets, estimate feature importance, handle missing data, and parallelize the computation makes it a popular choice for various applications in machine learning and data analysis (Gao et al., 2020 ). In this study, the Random Forest model exhibited favorable performance across various measures, particularly regarding AUC, accuracy, and sensitivity. However, the model showed lower specificity and cutoff threshold, which may be attributed to factors such as insufficient sample size, the presence of confounding factors that cannot be eliminated, significant collinearity among certain variables, and the exclusion of certain key variables. Future research can further improve these aspects, potentially enhancing the model's predictive capability. Conclusion In summary, the results of this study indicate a significant correlation between the truth-seeking, analytical, and inquisitiveness in the propensity indicators and the effectiveness of anti-involution training in enhancing critical thinking. Based on these findings, we have developed a simplified and replicable ML-based predictive model to make preliminary predictions on improving critical thinking. This model enables educators to make preliminary forecasts regarding the improvement of critical thinking, thereby facilitating timely intervention and guidance for learners who encounter challenges in enhancing their critical thinking abilities. Abbreviations ML Machine learning SMOTE Synthetic minority oversampling technique LASSO Least absolute shrinkage and selection operator ROC Receiver operating characteristic DCA Decision curve analysis SHAP Shapley Additive exPlanations AUC Area under the curve CTDI-CV The Chinese version of the Critical Thinking Disposition Inventory IQR Interquartile range GNB Gaussian Naive Bayes SVM Support Vector Machine MLP Multilayer perceptron KNN K-nearest neighbors PR Precision-recall DNN Deep Neural Networks Declarations Acknowledgments We thank all participants in this study. We also thank the Kunming Medical University Dental Hospital for its assistance in data collection. Author contributions LZ and CY conceived and designed this study. CY, ZA, LL and YH were responsible for data acquisition, analysis, and interpretation. CY, ZJ, LJ and CT participated in writing the manuscript. CY, LZ, and RX helped revise the manuscript. All the authors have read and approved the final manuscript. Funding This study was supported by The Kunming Medical University education reform Project [2021-JY-Y-036] and received the support of the National Natural Science Foundation of China (82360185). Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Conflict of interests The authors have no relevant competing interests to declare. Ethics approval and consent to participate The ethical approval for this study has been submitted to the Ethics Committee of Kunming Medical University. The committee reviewed this study and determined that ethical approval is not required as it complies with institutional guidelines and national laws and regulations. Thisstudy did not involve human clinical trials or animal experiments. Participation in the study is voluntary and verbal consent has been obtained from all participants. The confidentiality of participant responses has been ensured, and the data is only used for scientific purposes. To ensure complete anonymity, no data that may identify the interviewee was disclosed. In addition, this study was approved and supported by the Project Guidance Committee of Kunming Medical University. Consent for publication Not applicable. References Al-Rahmi, W. M. & Zeki, A. M. (2017). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University - Computer and Information Sciences , 29 (4). https://doi.org/10.1016/j.jksuci.2016.09.002 Antoniadis, A., Lambert-Lacroix, S. & Poggi, J. M. (2021). Random forests for global sensitivity analysis: A selective review. In Reliability Engineering and System Safety (Vol. 206). https://doi.org/10.1016/j.ress.2020.107312 Arce-Saavedra, B. J. & Blumen, S. (2022). Critical thinking, creativity, self-efficacy, and teaching practice in Peruvian teacher trainers. 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Z. & Mohammad Kamaruddin, L. (2020). Empirical Study on Motivation: Embedding design thinking mini project. Environment-Behaviour Proceedings Journal , 5 (15). https://doi.org/10.21834/ebpj.v5i15.2458 Uwineza, I., Uworwabayeho, A. & Yokoyama, K. (2023). Effects of Interactive Mathematics Software on Grade-5 Learners’ Performance. International Journal of Learning, Teaching and Educational Research , 22 (1). https://doi.org/10.26803/ijlter.22.1.10 Vergaray, A. D., Guerra, C., Cervera, N. & Burgos, E. (2022). Predicting Academic Performance using a Multiclassification Model: Case Study. International Journal of Advanced Computer Science and Applications , 13 (9). https://doi.org/10.14569/IJACSA.2022.01309102 Vickers, A. J. & Elkin, E. B. (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making , 26 (6). https://doi.org/10.1177/0272989X06295361 Yi, D., Wu, J., Zhang, M., Zeng, Q., Wang, J., Liang, J. & Cai, Y. (2022). Does Involution Cause Anxiety? An Empirical Study from Chinese Universities. International Journal of Environmental Research and Public Health , 19 (16). https://doi.org/10.3390/ijerph19169826 Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R. & Yu, H. (2023). XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease. BMC Medical Informatics and Decision Making , 23 (1). https://doi.org/10.1186/s12911-023-02238-9 Zeng, Q. (2023). Analysis of the Phenomenon of Chinese Educational Involution and Recommendations. BCP Business & Management , 41 . https://doi.org/10.54691/bcpbm.v41i.4419 Zhang, C., Fan, H., Xia, J., Guo, H., Jiang, X. & Yan, Y. (2017). The Effects of Reflective Training on the Disposition of Critical Thinking for Nursing Students in China: A Controlled Trial. Asian Nursing Research , 11 (3). https://doi.org/10.1016/j.anr.2017.07.002 ŽivkoviĿ, S. (2016). A Model of Critical Thinking as an Important Attribute for Success in the 21st Century. Procedia - Social and Behavioral Sciences , 232 . https://doi.org/10.1016/j.sbspro.2016.10.034 Additional Declarations No competing interests reported. Supplementary Files DataS1.xlsx TableS1.docx TableS2.docx TableS3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3908847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269907620,"identity":"9b8ff2b1-e888-4ff6-ab12-15ed5adb6a2f","order_by":0,"name":"Yuxiang Chen","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Chen","suffix":""},{"id":269907622,"identity":"e2ca967a-c685-4a4f-a2d1-b3f28fb424b8","order_by":1,"name":"Anna Zhao","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Zhao","suffix":""},{"id":269907623,"identity":"506fb5f5-49e6-4b19-91c1-2277fd0a3866","order_by":2,"name":"Haoran Yang","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Yang","suffix":""},{"id":269907624,"identity":"68b33472-a01b-46f7-9d89-e52ed9c73000","order_by":3,"name":"Tingting Chen","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Chen","suffix":""},{"id":269907625,"identity":"3151ed3a-d8ff-4fde-a513-a3bc4d5ea85d","order_by":4,"name":"Xianqi Rao","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianqi","middleName":"","lastName":"Rao","suffix":""},{"id":269907626,"identity":"256a8fcc-0e66-4ce9-baa8-88ba61ad6c4d","order_by":5,"name":"Jianzhong Zhou","email":"","orcid":"","institution":"Chuxiong Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jianzhong","middleName":"","lastName":"Zhou","suffix":""},{"id":269907627,"identity":"6a163114-255b-447b-92c1-84e9564776cc","order_by":6,"name":"Lin Li","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Li","suffix":""},{"id":269907628,"identity":"be8d2fc5-47a3-4139-8d54-5ee8b6fbaf6c","order_by":7,"name":"Jing Li","email":"","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":269907629,"identity":"5707fb3e-f1a4-40b8-818b-d79cd8711a8e","order_by":8,"name":"Ziliang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNvn3Dx8kGLDJsTEcPkCcFj6GHGaDBwV8xvyMxxKI0yLHkMMm+eCDXKJk8xkDIh3GcPaARIKBWYLBsTMfb7xhsJPTbSCkhbEvwSDBIC3P4MzZzZZzGJKNzQ4Q0sLMYJAAtKLY4MbZbdI8DAcStxHUwsZgcCDB4H/ihvtvnhGphYfHsAEYyIkzG86wEalFgi2ZAajFmJ/hmLHlHAMi/CI/g/n4zx9/wFH58MabCjs5glpQgAQPkVGDrIVUHaNgFIyCUTAiAACO3UMeNcdg6gAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Stomatology Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ziliang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-01-29 12:03:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3908847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3908847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50459367,"identity":"29b3e98a-8c90-4ed7-9e23-9d6e5595ddbe","added_by":"auto","created_at":"2024-01-31 20:25:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210888,"visible":true,"origin":"","legend":"\u003cp\u003eThe boxplot presents the distribution of scores for each critical thinking trait before and after anti-involution training. \u003cstrong\u003e(a)-(h)\u003c/strong\u003e corresponds to the scores distribution and the analysis of mean score differences for truth-seeking, open-mindedness, analyticity, systematicity, self-confidence in critical thinking, inquisitiveness, cognitive maturity, and CTDI-CV total scores before and after de-individuation training. The notation * * *, * *, and * indicate significance levels of 1%, 5%, and 10% respectively.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/889ad616b8dc4ef038c8a4d3.png"},{"id":50460253,"identity":"96b5529c-63c5-4ced-9b5f-660005aa12e2","added_by":"auto","created_at":"2024-01-31 20:33:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334617,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression analysis was used to select characteristic factors.\u003cstrong\u003e (a)\u003c/strong\u003e The use of 10-fold\u003c/p\u003e\n\u003cp\u003ecross-validation to draw vertical lines at selected values. \u003cstrong\u003e(b)\u003c/strong\u003e In the LASSO model, the coefficient profiles of 10 texture features were drawn from the log (λ) sequence. Vertical dotted lines are drawn at the minimum mean square error((λ = 0.06) and the standard error of the minimum distance (λ = 0.139).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/d1d998d3c9ae7f52674c725a.png"},{"id":50459376,"identity":"4710b825-865d-472c-af8d-c872ef3de3d5","added_by":"auto","created_at":"2024-01-31 20:25:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3549591,"visible":true,"origin":"","legend":"\u003cp\u003eML model comprehensive analysis. \u003cstrong\u003e(a) \u003c/strong\u003eROC and AUC values for the training set, \u003cstrong\u003e(b) \u003c/strong\u003eROC and AUC values for the validation set. All participants were sampled in a ratio of 8:2 for ten iterations. \u003cstrong\u003e(c) \u003c/strong\u003eCalibration curves were plotted for the validation set, using the average predicted probability on the x-axis, the actual probability of events on the y-axis, a dashed diagonal line as a reference, and additional solid lines representing different model fit. Approximate alignment between the fit and reference lines suggests higher model accuracy. \u003cstrong\u003e(d) \u003c/strong\u003eDCA for the validationset, where a black dashed line indicates a significant improvement in critical thinking for all participants, and red dashed and thin black lines indicate no significant improvement for any participant. The remaining solid lines represent different models. \u003cstrong\u003e(e) \u003c/strong\u003ePR curves for the training set. \u003cstrong\u003e(f)\u003c/strong\u003ePR curves for the validation set, with precision on the y-axis and recall on the x-axis. Suppose the PR curve of another model entirely covers the PR curve of one model. In that case, it can be concluded that the latter is superior to the former, with higher AP values indicating better model performance.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/a3932b810f240b2c3059a288.png"},{"id":50460256,"identity":"b67ab84e-c519-43f7-8888-0f4727326e0a","added_by":"auto","created_at":"2024-01-31 20:33:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1248448,"visible":true,"origin":"","legend":"\u003cp\u003eThe Random Forest model's training, validation, and test results. \u003cstrong\u003e(a) \u003c/strong\u003eROC and AUC values for the training set, \u003cstrong\u003e(b)\u003c/strong\u003e ROC and AUC values for the validation set. A 5-fold cross-validation approach was employed, training and cross-validating 20% of the participants. The solid lines in different colors represent the results from five iterations. \u003cstrong\u003e(c) \u003c/strong\u003eROC and AUC values for the test set. \u003cstrong\u003e(d)\u003c/strong\u003e Calibration curve for the test set.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/c5802a420bebd992331a87c1.png"},{"id":50459370,"identity":"ba47efd1-e757-453a-af60-569ed85b1474","added_by":"auto","created_at":"2024-01-31 20:25:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":246724,"visible":true,"origin":"","legend":"\u003cp\u003eSHAP interprets model \u003cstrong\u003e(a) \u003c/strong\u003eIn SHAP, the attributes of characteristics are depicted. Each feature is represented by a line, with the SHAP value displayed on the abscissa. Red dots denote the higher eigenvalues, while blue dots indicate the lower eigenvalues. \u003cstrong\u003e(b) \u003c/strong\u003eThe importance of each covariate in developing the final prediction model is described by the matrix diagram, which showcases the feature importance ranking as indicated by SHAP. \u003cstrong\u003e(c)\u003c/strong\u003e-\u003cstrong\u003e(d)\u003c/strong\u003e The individual contributions of Participants, both with and without significantly improved critical thinking, are assessed.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/a2ad23fd923d6a51c3506ac2.png"},{"id":67585614,"identity":"50f430e9-86dc-4236-9760-fb1c7395b5b9","added_by":"auto","created_at":"2024-10-27 17:01:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5845021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/f1accb7a-3257-4ddb-a5ce-5f6a3c73c699.pdf"},{"id":50459374,"identity":"4eaf41b5-d2dd-4ef5-90d0-6690588e4fcf","added_by":"auto","created_at":"2024-01-31 20:25:08","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33197,"visible":true,"origin":"","legend":"","description":"","filename":"DataS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/894d1e82e8e1bd3ce543c503.xlsx"},{"id":50460619,"identity":"bb772da9-0d27-4611-a489-23e02fee989e","added_by":"auto","created_at":"2024-01-31 20:41:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13502,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/bd7e9c5cee78507c1d35f09c.docx"},{"id":50460254,"identity":"02133f54-d4c0-4a0a-945f-d7d5a0587b8e","added_by":"auto","created_at":"2024-01-31 20:33:08","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14339,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/fbe911f53b67fa9aeeb2b0cf.docx"},{"id":50459369,"identity":"ff1e52f4-e4b6-4f0b-b4f0-0868225499d7","added_by":"auto","created_at":"2024-01-31 20:25:08","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12298,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3908847/v1/933412f8772882cc94171d8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCritical thinking is a rational and reflective mode manifested when confronted with evidence, situations, criteria, and concepts (Sari et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As healthcare professionals are intricately involved in the lives of individuals, there is often a need to make judicious, prompt, and rational judgments based on professional knowledge and the best available evidence, coupled with clinical realities and patient preferences. This necessitates the possession of providers to minimize errors and optimize clinical decision-making (Dissen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe phenomenon of involution, which has been highly scrutinized in recent years by scholars, is a complex, evolving, and multidimensional concept. It refers to the situation where individuals within a profession exert increasing efforts to compete for limited resources, decreasing the individual\u0026rsquo;s \"benefit-to-effort ratio.\" This state is characterized by\"internal consumption\" or \"irrational competition\" (Dou et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McCullough, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The high level of involution in a societal structure generates social anxiety, negatively impacting the development of various professions (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; D. Yi et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, it becomes necessary for all professions to strive towards anti-involution. In the healthcare industry, a phenomenon known as involution also exists among healthcare professionals, which hurts their professional growth and the delivery of medical services. This is particularly evident in medical research, where involution is pronounced. Although numerous research achievements are produced, they contribute little to advancing the research field, as scientific inquiry often remains confined to a simplistic level of self-depletion and repetition (Gan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, it is crucial to provide anti-involution training for healthcare professionals.\u003c/p\u003e \u003cp\u003eCultivating anti-involution and critical thinking is essential in developing healthcare professionals, and exploring the relationship between them is vital in their training. Research indicates that both anti-involution training and critical thinking are associated with innovative thinking (Handayani \u0026amp; Wulandari, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zeng, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It can be shown that there is an indirect correlation between anti-involution training and critical thinking. Still, no direct correlation between the two has been reported in the literature.The effects of anti-involution training, young healthcare professionals in dental outpatient clinics, and enhancing critical thinking can be understood as components of the teaching process, learners, and learning outcomes. Existing studies have shown that factors influencing learner performance are multifaceted and can be classified into two categories of indicators (Al-Rahmi \u0026amp; Zeki, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mosimege \u0026amp; Winnaar, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uwineza et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The first category refers to predispositional indicators, representing the attributes learners possess upon entering the learning environment (static indicators). The second category consists of behavioral performance indicators, which reflect learners\u0026rsquo; performance during the learning process (dynamic indicators) (Efthimiopoulos \u0026amp; Mylonakou-Keke, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Magsino, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; M\u0026uuml;ller \u0026amp; Seufert, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nickl et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To date, no studies have reported on the factors influencing the effectiveness of anti-involution training in improving critical thinking.\u003c/p\u003e \u003cp\u003eIn this study, we aim to explore the correlation between anti-involution training and propensity indicators of young healthcare professionals in dental outpatient clinics in relation to the enhancement of critical thinking. Using machine learning algorithms, we will identify the key factors influencing the effectiveness of anti-involution training in improving critical thinking and construct a machine learning model to quantify the impact of these factors. This will enable educational professionals to intervene and guide learners at risk of poor learning performance in advance, thus maximizing the improvement of critical thinking among young healthcare professionals in dental outpatient clinics through anti-involution training. Additionally, we will utilize the SHAP method to enhance the Interpretability of the model, ensuring that the results are easily understood and applicable.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted in Kunming, China, from January to December 2020. The study participants comprised young healthcare professionals under 40 from five outpatient departments of the Affiliated Stomatological Hospital of Kunming Medical University. One hundred fourteen individuals participated in the anti-involution training before the study, while 91 individuals participated after the training. A total of 205 questionnaires were returned, and after excluding cases with missing essential information and obvious logical errors, 182 questionnaires were deemed suitable for analysis, resulting in an effective response rate of 88.78%. Informed consent was obtained from all study participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe Chinese version of the Critical Thinking Disposition Inventory (CTDI-CV) was utilized as the survey tool in this study. The overall Cronbach\u0026rsquo;s α coefficient of the CTDI-CV was 0.90. The inventory consists of positive and negative items, rated on a 6-point Likert scale. The scoring for positive items ranges from 1 (strongly disagree) to 6 (strongly agree), while the scoring for negative items is the reverse (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The CTDI-CV encompasses seven traits comprising 70 items: truth-seeking, analyticity, open-mindedness, systematicity, inquisitiveness, self-confidence in critical thinking, and cognitive maturity. Each trait includes ten items, resulting in a total score range of 420 (Huang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore completing the questionnaires, the research team provided detailed explanations to the participants regarding the purpose, significance, instructions, precautions, and deadline for questionnaire submission. Before the anti-involution training, baseline measurements were taken from young healthcare professionals under 40 in five outpatient departments of the Affiliated Stomatological Hospital of Kunming Medical University. The training, led by the department directors, lasted for one year and included a series of lectures on involution-related theories. Regular assessments on involution-related knowledge points and measurements of critical thinking disposition were conducted every three months.\u003c/p\u003e \u003cp\u003eThe data collected in this study encompassed two groups of factors: (1) participants\u0026rsquo; propensity indicators, including initial knowledge and skills and inherent indicators. Initial knowledge and skills involved the pre-training total score of CTDI-CV, as well as the total scores of the seven traits of CTDI-CV before the training, including truth-seeking, analyticity, open-mindedness, systematicity, inquisitiveness, self-confidence in critical thinking, and cognitive maturity. Inherent indicators included gender and profession. (2) Conclusion: The effects of anti-involution training on enhancing critical thinking among young healthcare professionals in dental outpatient clinics. The difference between the two total scores of CTDI-CV was used to measure the effects of anti-involution training on enhancing critical thinking among young healthcare professionals in dental outpatient clinics. A difference\u0026thinsp;\u0026ge;\u0026thinsp;25 indicated a significant improvement, while a difference\u0026thinsp;\u0026lt;\u0026thinsp;25 indicated no significant improvement. Based on this criterion, the participants were divided into the significantly improved and non-significantly improved groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003estatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eAnalytical methods\u003c/h2\u003e \u003cp\u003eDescriptive analysis was conducted using SPSS (version 26.0), while other statistical analysis procedures were performed using R (version 3.6.1) and Python (version 3.4.3). Mann-Whitney U or Chi-square tests were used to analyze the demographic differences between the significantly improved and non-significantly improved groups, with continuous variables represented as median and interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical variables were presented as counts and percentages and compared using the Chi-square test. Paired samples t test was used to analyze the characteristics of the effects of anti-involution training on critical thinking among young healthcare professionals in dental outpatient clinics. Spearman correlation analysis and multivariate logistic regression were employed to evaluate the relationships between predispositional indicators and the effects of anti-involution training on critical thinking among young healthcare professionals in dental outpatient clinics, as well as the influence of the former on the latter. A two-sided p-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData preprocessing\u003c/h2\u003e \u003cp\u003eIn order to mitigate the influence of indicator dimensions and magnitudes on the study results and to facilitate comparability of data across each test index, the test indicators were subjected to normalization. The normalization process employed in this study was \"MinMaxScaler,\" which linearly transformed the original data to ensure that the resulting values fell within the range of [0, 1]. Additionally, data balancing was performed using the \u0026ldquo;SMOTE\u0026rdquo; technique, which equalized the proportion of minority and majority classes to a ratio of 1:1 (Nguyen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariable selection and prediction model establishment\u003c/h2\u003e \u003cp\u003eWe employed Lasso regression for feature selection to select the most predictive and relevant features to improve model performance and reduce computational burden (Bainter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lasso regression compresses the coefficients of variables in the regression model by generating a penalty function to prevent overfitting and address severe collinearity issues (Gen\u0026ccedil; \u0026amp; \u0026Ouml;zkale, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guler \u0026amp; Guler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Lasso regression was performed with 10-fold cross-validation using the \u0026ldquo;glmnet 4.1.2\u0026rdquo; R package. The study participants were divided into training and validation sets at 7:3. On the training set, we fitted nine machine learning classifiers: logistic regression, XGBoost, LightGBM, random forest, AdaBoost, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), multilayer perceptron (MLP), and K-nearest neighbors (KNN). XGBoost was built using the \u0026ldquo;xgBoost 1.2.1\u0026rdquo; Python package, while LightGBM was built using the \u0026ldquo;lightgbm 3.2.1\u0026rdquo; Python package. The other machine-learning algorithms were built using the \u0026ldquo;sklearn 0.22.1\u0026rdquo; Python package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eModel performance evaluation\u003c/h2\u003e \u003cp\u003eModel performance was evaluated based on its discriminative, calibration, clinical applicability, and generalizability. Resampling was employed to ensure consistency of training samples across multiple models, enabling comparison of the results. The best model selection was based on analyzing the importance of metrics across models on both training and testing sets. The area under the curve (AUC), which is commonly used to characterize the diagnostic accuracy of a test or predictive model, was constructed using the \u0026ldquo;sklearn 0.22.1\u0026rdquo; Python package (Obuchowski \u0026amp; Bullen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Decision curve analysis (DCA) plots, representing a decision analysis technique with substantial advantages in assessing the clinical utility of models, were generated using the \u0026ldquo;rmda 1.6\u0026rdquo; R software (Vickers \u0026amp; Elkin, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Calibration curves, which assess the predictive ability of models, were constructed using the \u0026ldquo;sklearn 0.22.1\u0026rdquo; Python package (Fenlon et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Precision-recall (PR) curves, widely used in the field, were plotted using the \u0026ldquo;scikit 0.22.1\u0026rdquo; Python package to evaluate model performance (Li \u0026amp; Guo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Once the most helpful model was determined through comprehensive comparison, all eligible participants were reclassified into training, validation, and testing sets at 6:2:2. The best model underwent 5-fold cross-validation on the training set and was evaluated on the validation and testing sets. Learning curves were generated using the \u0026ldquo;scikit 0.22.1\u0026rdquo; Python package to assess the adaptability and stability of the model on the training and validation sets (Belkin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eInterpretability of the model\u003c/h2\u003e \u003cp\u003eWe utilized SHAP values to explain the optimal machine learning model. SHAP, derived from Shapley values in game theory, quantifies the contribution of each feature in the model to the final prediction. The SHAP summary plot combines feature importance and feature effects. Each point on the SHAP dependence plot represents a feature and its Shapley value for a specific instance. In SHAP explanations, feature attributes like Shapley values are visualized as \"forces\" that contribute to increasing or decreasing the prediction. Predictions are based on a baseline, and the baseline for Shapley values is computed as the average of all predictions. The \"SHAP 0.39.0\" Python package generated visual explanations of SHAP values for model importance and contributions (Futagami et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of the participants are presented(Table 1) . In this study, we excluded a total of 23 participants with incomplete data, a total of 91 participants were enrolled in this study, with 62 participants who underwent anti-involution training showing no significant improvement in critical thinking skills and 29 participants showing significant improvement. Detailed data for 91 participants can be found in Supplementary Data S1. When comparing the non-significant improvement group to the significant improvement group, significant differences (p \u0026lt; 0.05) were observed in several critical thinking aspects, including CTDI-CV total score before training (p = 0.002), truth-seeking (p = 0.007), analyticity (p = 0.008), systematicity (p = 0.015), self-confidence in critical thinking (p = 0.047), inquisitiveness (p = 0.027), and cognitive maturity (p = 0.047). However, there were no significant differences (p \u0026gt; 0.05) in terms of profession (p = 0.378), gender (p = 0.953), and open-mindedness (p = 0.185).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 \u0026nbsp;Characteristics in non-significantly improved group and significantly improved group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003enon-significantly improved group \u0026nbsp;significantly (n=62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003esignificantly improved group(n=29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eprofession,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eStomatology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e24(38.710%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e10(34.483%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e0.378\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eNursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e36(58.065%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e16(55.172%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e2(3.226%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e3(10.345%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e11(17.742%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e5(17.241%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e0.953\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e51(82.258%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e24(82.759%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eCTDI-CV total score before training, median [IQR]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e263.00[249.00,279.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e244.00[212.00,256.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1773049645390071%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003etruth-seeking, median [IQR]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e39.00[35.00,41.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e33.00[30.00,40.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eanalyticity, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e35.00[32.00,39.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e31.00[28.00,36.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003esystematicity, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e37.00[35.00,40.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e33.00[30.00,37.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003eself-confidence in critical thinking, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e35.00[31.00,40.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e30.00[25.00,37.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003einquisitiveness, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e37.00[31.00,40.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e33.00[27.00,38.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.25531914893617%\" valign=\"top\"\u003e\n \u003cp\u003ecognitive maturity, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e40.00[36.00,47.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.01418439716312%\" valign=\"top\"\u003e\n \u003cp\u003e37.00[33.00,42.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ep\u003csup\u003ea\u0026nbsp;\u003c/sup\u003evalues are calculated from the Mann-Whitney U test, and p\u003csup\u003eb\u003c/sup\u003e was obtained by x\u003csup\u003e2\u003c/sup\u003e-tests. p \u0026lt; 0.05 indicates statistical significance. Numbers in bold mean statistical significance.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe effects of anti-involution training on the enhancement of critical thinking among young healthcare professionals in dental outpatient clinics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effects of anti-involution training on the enhancement of critical thinking among young healthcare professionals in dental outpatient clinics are presented(Table 2 and Fig. 1). The results reveal that, among the seven characteristics of critical thinking, there is a slight decrease in the average score of self-confidence in critical thinking. The average scores of the remaining six characteristics and the total score have all shown slight improvements. Notably, there is a statistically significant difference in the average score increase for cognitive maturity, which is 3.978\u0026plusmn;-0.014 (P=0.004). Additionally, the average score increase for the total score is 11.813\u0026plusmn;6.752 (P=0.001), signifying a statistically significant difference.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Scores of each critical thinking feature before and after anti-involution training（\u0026plusmn;s）\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\" valign=\"top\"\u003e\n \u003cp\u003ebefore anti-involution training\u0026nbsp;(n=91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\" valign=\"top\"\u003e\n \u003cp\u003eAfter anti-involution training(n=91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\" valign=\"top\"\u003e\n \u003cp\u003eIncreased scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003etruth-seeking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e36.989\u0026plusmn;6.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e38.132\u0026plusmn;6.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e1.143\u0026plusmn;-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eopen-mindedness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e37.308\u0026plusmn;6.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e38.341\u0026plusmn;6.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e1.033\u0026plusmn;0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eanalyticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e33.681\u0026plusmn;5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e34.593\u0026plusmn;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e0.912\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003esystematicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e35.198\u0026plusmn;7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e36.121\u0026plusmn;7.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e0.923\u0026plusmn;-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eself-confidence in critical thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e33.286\u0026plusmn;7.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e31.275\u0026plusmn;7.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e-2.011\u0026plusmn;-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003einquisitiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e33.835\u0026plusmn;8.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e34.802\u0026plusmn;8.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e0.967\u0026plusmn;-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003ecognitive maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e39.637\u0026plusmn;8.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e43.615\u0026plusmn;8.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e3.978\u0026plusmn;-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eCTDI-CV total scores\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.955436720142604%\"\u003e\n \u003cp\u003e249.934\u0026plusmn;39.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.459893048128343%\"\u003e\n \u003cp\u003e261.747\u0026plusmn;46.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.547237076648841%\"\u003e\n \u003cp\u003e11.813\u0026plusmn;6.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* * * , * * , * represent 1% , 5% , 10% significance levels respectively. Numbers in bold mean statistical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of the effects of anti-involution training on critical thinking and propensity indicators among young healthcare professionals in dental outpatient clinics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3, there is a negative correlation between truth-seeking, analyticity, systematicity, self-confidence in critical thinking, inquisitiveness, cognitive maturity, and CTDI-CV total scores with the effects of anti-involution training on the enhancement of critical thinking (truth-seeking: r = -0.284, p \u0026lt; 0.05; analyticity: r = -0.287, p \u0026lt; 0.05; systematicity: r = -0.258, p \u0026lt; 0.05; self-confidence in critical thinking: r = -0.210, p \u0026lt; 0.005; inquisitiveness: r = -0.234, p \u0026lt; 0.05; cognitive maturity: r = -0.210, p \u0026lt; 0.05; CTDI-CV total scores: r = -0.327, p \u0026gt; 0.05). However, there is no correlation between profession, gender, open-mindedness, and the effects of de-individuation training on the enhancement of critical thinking (profession: r = 0.078, p \u0026gt; 0.05; gender: r = -0.006, p \u0026gt; 0.05; open-mindedness: r =- 0.140, p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 3 The Spearman\u0026apos;s association analysis among Tendency indicators and the effects of anti-involution training on critical thinking\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"69.35483870967742%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eThe effects of anti-involution training on the enhancement of critical thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.03875968992248%\" valign=\"top\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.96124031007752%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003eprofession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003etruth-seeking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003eopen-mindedness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003eanalyticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003esystematicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003eself-confidence in critical thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003einquisitiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003ecognitive maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.64516129032258%\" valign=\"top\"\u003e\n \u003cp\u003eCTDI-CV total scores\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.17204301075269%\"\u003e\n \u003cp\u003e-0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.182795698924732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe p-values were calculated using the Spearman correlation analysis.* * * , * * , * represent 1% , 5% , 10% significance levels respectively. Numbers in bold mean statistical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentifying predictors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLASSO regression analysis was conducted on the remaining independent variables with a tophus as the dependent variable (Fig. 2). LASSO can compress variable coefficients to prevent overfitting and solve severe collinearity problems (F. Yi et al., 2023). The results showed that (lambda with minimum mean square error =\u0026nbsp;0.06) 10 independent variables were reduced to 4, including truth-seeking, analyticity, and inquisitiveness(Fig. 2a,b)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive Analysis of Classified Multi-Mode\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple ML models were utilized for data sample classification: XGBoost, logistic regression, LightGBM, Random Forest, AdaBoost, KNN, SVM, GNB, and MLP. A 5-fold cross-validation approach was performed to validate all models. The AUC value was employed to evaluate model predictions (Obuchowski \u0026amp; Bullen, 2018). Results indicated that Random Forest, XGBoost, and AdaBoost exhibited superior performance on the training set, whereas Random Forest, KNN, and LightGBM achieved the highest performance on the validation set (Fig.\u0026nbsp;3a,b);see more details in Supplemental Table S1and Table S2.The AUC value primarily assesses the predictive accuracy of the models without providing information on their clinical utility or preferences (Muschelli, 2020; Obuchowski \u0026amp; Bullen, 2018). Therefore, we analyzed the PR curve, calibration curve, and DCA. The DCA curve illustrated the favorable clinical applicability of the Random Forest model (Fig.\u0026nbsp;3c). The calibration curve suggested that the Random Forest and KNN models provided more accurate predictions (Fig.\u0026nbsp;3d). The Random Forest model displayed exceptional performance in the training and validation sets, generating the highest average precision (AP) value on the validation set (Fig.\u0026nbsp;3e,f). Considering all factors, the comprehensive analysis suggests that Random Forest can be considered the optimal model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe best model construction and evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Random Forest model was trained using the training set with 5-fold cross-validation. The results revealed that the average AUC for the training set was 0.912 (0.843-0.980), while for the validation set, it was 0.889 (0.740-0.990). The AUC for the test set was 0.868 (0.731-1.000) (Fig. 4a-c);see more details in Supplemental Table S3.Considering that the performance of the validation set did not exceed or differ by less than 10% from the test set based on the AUC metric, it can be concluded that the model was successfully fitted. The calibration curve further demonstrated that the Random Forest model was accurate and predictive (Fig. 4d). These outcomes suggest that the Random Forest model can be utilized for classification modeling tasks on the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eThe SHAP to Interpret ML model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed SHAP to interpret the model to provide an intuitive explanation of the selected variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of results for each participant is illustrated (Fig. 5a).In each feature importance line, different colored dots represent the attribution of results by all participants. Red dots represent high likelihood values, while blue dots represent low likelihood values. The variables inquisitiveness, truth-seeking, and analyticity ability of participants before anti-involution training are all negatively correlated with the results.Three risk factors were ranked by evaluating the average absolute SHAP values(Fig. 5b). The x-axis represents the importance of the predictive model measured by the SHAP values. The feature importance of the Random Forest model, from high to low, was observed as inquisitiveness, truth-seeking, and analyticity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we provide two typical examples to illustrate the Interpretability of the model. For one participant, there was no significant improvement observed in critical thinking, as indicated by a lower SHAP prediction score of 0.15 (Fig. 5c).In contrast, another participant exhibited significant improvement in critical thinking, as evidenced by a higher SHAP score of 0.95 (Fig. 5d).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCritical thinking is both a skill and a mindset. It refers to an individual\u0026rsquo;s ability to analyze and solve problems in complex environments using existing knowledge and experience and the capacity to make decisions based on reflection, analysis, and reasoning (Ennis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Strong critical thinking skills are a fundamental ability and quality that healthcare professionals should possess (ŽivkoviĿ, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The findings of this study indicate that the overall level of critical thinking abilities among young dental outpatient healthcare workers is moderate to low, falling short of the criterion for a positive critical thinking tendency (Cisneros, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, there is still ample room for improvement.\u003c/p\u003e \u003cp\u003eInvolvement is a state of internal consumption or irrational competition, and highly involved social structures hurt the development of various professions within society. The \u0026ldquo;internalization\u0026rdquo; of the medical industry has brought about negative consequences for the growth of healthcare professionals and the execution of medical activities, which may hinder improving critical thinking. Previous studies have shown that critical thinking and innovative thinking complement each other, and the key to anti-involution lies in enhancing innovative thinking (Arce-Saavedra \u0026amp; Blumen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jing, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ram\u0026iacute;rez-Montoya et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sharma \u0026amp; Priyamvada, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study explores the effects of anti-involution training on enhancing critical thinking among young dental outpatient healthcare workers. The results indicate that, aside from a slight decrease in the average score for self-confidence in critical thinking, there were small but significant improvements in the average scores for the other six characteristics of critical thinking and the overall score. This suggests that anti-involution training has a specific positive effect on the critical thinking of healthcare professionals, providing additional evidence for the direct relationship between anti-involution training and critical thinking.\u003c/p\u003e \u003cp\u003eHowever, this study also has some limitations. Firstly, there are various methods of anti-involution training. The single training method used in this study can only cover some aspects of anti-involution training. Secondly, there were fluctuations in the participants throughout the study period, resulting in the loss of 25 participants. To achieve a one-to-one match between the pre-and post-training samples, the data of the lost participants were excluded, which may lead to an insufficient sample size. Additionally, the study lasted one year, during which confounding factors may have been present, potentially introducing errors in the experimental results.\u003c/p\u003e \u003cp\u003eAs an instructional practice, improving the effectiveness of anti-involution training is a concern for scholars. The factors influencing learners\u0026rsquo; performance are complex, and it is challenging to predict them based on a single factor. Previous research has shown a strong correlation between dispositional indicators and learners\u0026rsquo; performance, particularly regarding initial knowledge, skills, and inherent indicators (Considine \u0026amp; Zappal\u0026agrave;, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Deakin Crick et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Friedman \u0026amp; Mandel, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This study explores the correlation between improving critical thinking among young dental outpatient healthcare workers through anti-involution training and their dispositional indicators. The correlation analysis results indicate a significant statistical significance between improving critical thinking and the pre-training total score, pre-training analytical ability, self-confidence, cognitive maturity, systematic thinking ability, and truth-seeking of critical thinking. These dispositional indicators are closely related to innovative thinking, thus further confirming the close relationship between innovative thinking and critical thinking, which aligns with the previous research conclusion of our research team (Syed Marzuki et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, apart from the factors in this study, dispositional indicators include other aspects such as family background, initial grades, expectations or satisfaction with education, and goal expectations (Considine \u0026amp; Zappal\u0026agrave;, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Deakin Crick et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Friedman \u0026amp; Mandel, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The relationship between these indicators and the improvement of critical thinking among young dental outpatient healthcare workers through anti-involution training requires further research for verification.\u003c/p\u003e \u003cp\u003eTraditionally, education research has been relatively limited regarding research models, relying primarily on small-scale data and traditional statistical methods. Research data is often obtained through questionnaires and self-reports, with relatively limited sample sizes. Moreover, traditional statistical analysis methods have limitations in revealing complex relationships between variables. The widespread application of machine learning methods in various fields has gained attention and usage in social science research (Macalli et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Machine learning has significant advantages in studying factors that influence learning performance. It excels in handling large datasets and extracting potential underlying connections that traditional methods may overlook. Recently, scholars have begun to use supervised machine learning methods such as Support Vector Machines (SVM), Random Forests, Deep Neural Networks (DNN), XGBoost, etc., to address classification and prediction problems in the field of educational research (Vergaray et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the Random Forest algorithm was ultimately selected to construct a predictive model for the effect of anti-involution training on the improvement of critical thinking among young dental outpatient healthcare workers. The Random Forest model is a widely used machine learning model. An ensemble learning method combines multiple decision trees to make predictions. Each decision tree in the Random Forest is built independently using a random subset of the dataset and random features, which helps reduce overfitting and improve generalization (Rigatti, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One of the key advantages of the Random Forest model is its ability to handle high-dimensional datasets and maintain good performance. It can handle large-scale datasets with numerous features without sacrificing accuracy (Malhotra \u0026amp; Karanicolas, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Random Forest model is known for its robustness and resistance to noise and outliers, making it suitable for various real-world applications. The Random Forest model can also estimate feature importance. By calculating the average decrease in impurity or information gain caused by each feature, it can identify the most influential features for prediction. This feature importance analysis can be valuable for feature selection and understanding the underlying relationships within the dataset.\u003c/p\u003e \u003cp\u003eFurthermore, the Random Forest model can handle missing data and maintain accuracy even when a significant portion is missing. It can utilize the available information from other features to fill in missing values and make reliable predictions (Antoniadis et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Random Forest model is computationally efficient and can be parallelized for faster training and prediction. It can process large datasets efficiently using multi-core processors and distributed computing frameworks. In summary, the Random Forest model is a robust machine-learning algorithm that combines the predictions of multiple decision trees to provide accurate and robust results (Su et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Its ability to handle high-dimensional datasets, estimate feature importance, handle missing data, and parallelize the computation makes it a popular choice for various applications in machine learning and data analysis (Gao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the Random Forest model exhibited favorable performance across various measures, particularly regarding AUC, accuracy, and sensitivity. However, the model showed lower specificity and cutoff threshold, which may be attributed to factors such as insufficient sample size, the presence of confounding factors that cannot be eliminated, significant collinearity among certain variables, and the exclusion of certain key variables. Future research can further improve these aspects, potentially enhancing the model's predictive capability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the results of this study indicate a significant correlation between the truth-seeking, analytical, and inquisitiveness in the propensity indicators and the effectiveness of anti-involution training in enhancing critical thinking. Based on these findings, we have developed a simplified and replicable ML-based predictive model to make preliminary predictions on improving critical thinking. This model enables educators to make preliminary forecasts regarding the improvement of critical thinking, thereby facilitating timely intervention and guidance for learners who encounter challenges in enhancing their critical thinking abilities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eML Machine learning\u003c/p\u003e\n\u003cp\u003eSMOTE Synthetic minority oversampling technique\u003c/p\u003e\n\u003cp\u003eLASSO Least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eDCA Decision curve analysis\u003c/p\u003e\n\u003cp\u003eSHAP Shapley Additive exPlanations\u003c/p\u003e\n\u003cp\u003eAUC Area under the curve\u003c/p\u003e\n\u003cp\u003eCTDI-CV The Chinese version of the Critical Thinking Disposition Inventory \u003c/p\u003e\n\u003cp\u003eIQR Interquartile range\u003c/p\u003e\n\u003cp\u003eGNB Gaussian Naive Bayes\u003c/p\u003e\n\u003cp\u003eSVM Support Vector Machine\u003c/p\u003e\n\u003cp\u003eMLP Multilayer perceptron \u003c/p\u003e\n\u003cp\u003eKNN K-nearest neighbors \u003c/p\u003e\n\u003cp\u003ePR Precision-recall\u003c/p\u003e\n\u003cp\u003eDNN Deep Neural Networks \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe thank all participants in this study. We also thank the Kunming Medical University Dental Hospital for its assistance in data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eLZ and CY conceived and designed this study. CY, ZA, LL and YH were responsible for data acquisition, analysis, and interpretation. CY, ZJ, LJ and CT participated in writing the manuscript. CY, LZ, and RX helped revise the manuscript. All the authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was supported by The Kunming Medical University education reform Project [2021-JY-Y-036] and received the support of the National Natural Science Foundation of China (82360185).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests \u0026nbsp;\u003c/strong\u003eThe authors have no relevant competing interests to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u003c/strong\u003eThe ethical approval for this study has been submitted to the Ethics Committee of Kunming Medical University. The committee reviewed this study and determined that ethical approval is not required as it complies with institutional guidelines and national laws and regulations. Thisstudy did not involve human clinical trials or animal experiments. Participation in the study is voluntary and verbal consent has been obtained from all participants. The confidentiality of participant responses has been ensured, and the data is only used for scientific purposes. To ensure complete anonymity, no data that may identify the interviewee was disclosed. In addition, this study was approved and supported by the Project Guidance Committee of Kunming Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Rahmi, W. M. \u0026amp; Zeki, A. M. (2017). 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A Model of Critical Thinking as an Important Attribute for Success in the 21st Century. \u003cem\u003eProcedia - Social and Behavioral Sciences\u003c/em\u003e, \u003cem\u003e232\u003c/em\u003e. https://doi.org/10.1016/j.sbspro.2016.10.034\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"anti-involution training, Critical thinking, Propensity indicators, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-3908847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3908847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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 \u0026ldquo;MinMaxScaler\u0026rdquo; 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTruth-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\u0026thinsp;=\u0026thinsp;0.889 (95% CI: 0.839\u0026ndash;0.937), accuracy\u0026thinsp;=\u0026thinsp;0.850, sensitivity\u0026thinsp;=\u0026thinsp;0.855, specificity\u0026thinsp;=\u0026thinsp;0.933.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 20:25:03","doi":"10.21203/rs.3.rs-3908847/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8802984a-321d-4d19-9b09-fe4665696ff9","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-27T16:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 20:25:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3908847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3908847","identity":"rs-3908847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}