Enhancing Analytical Thinking in Early-Career Physicians: Evaluating the EBM-CBL-PBL Integrated Teaching Model

Enhancing Analytical Thinking in Early-Career Physicians: Evaluating the EBM-CBL-PBL Integrated Teaching 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 Enhancing Analytical Thinking in Early-Career Physicians: Evaluating the EBM-CBL-PBL Integrated Teaching Model Xianzhi Chen, Manman Xu, Deshun Liu, Fang Yang, Guobao Xu, Huaichen Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6439748/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 Clinical reasoning is a critical skill in medical education. Dual-process theory highlights the interaction between intuitive and analytical thinking, where the former may lead to diagnostic errors. This study employed the cognitive reflection test (CRT) to assess cognitive reflection patterns across different experience levels and genders, while evaluating the impact of an integrated EBM-CBL-PBL teaching model on the development of analytical thinking in medical trainees. Methods A cross-sectional study was conducted among trainees, Resident Physicians, Attending Physicians and Consultant-level Physicians registered at the First Affiliated Hospital of Anhui University of Science and Technology. A generalized linear mixed model (GLMM) was used to analyse the relationships among sex, clinical experience, and analytical thinking. Participants comprising clinical medicine trainees were randomly allocated to either the traditional pedagogy group (n = 18) or the EBM-CBL-PBL intervention group (n = 18), with comparisons made on the basis of CRT responses (intuitive vs. reflective) and teaching satisfaction. Results Analytical thinking (CRT–Reflective) increased with increasing clinical experience (trainees: 41.18%, residents: 51.85%, attending physicians: 57.14%). Senior female physicians presented the strongest analytical tendency (OR = 5.919, p = 0.005). The EBM-CBL-PBL approach enhanced reflective cognition (61.11% vs. 44.44%, p < 0.05) and satisfaction (p < 0.001), but no significant gender interaction was observed (P = 0.396). Conclusion Clinical experience contributes to the development of analytical thinking, particularly among female physicians. The EBM-CBL-PBL teaching model improves analytical understanding in early-career physicians but does not eliminate gender differences. Sustained effects require long-term practice. Dual-process theory CRT EBM-CBL-PBL integrated teaching model GLMM Reflective response Background The clinical clerkship phase represents a critical learning period in medical education, particularly for developing diagnostic clinical reasoning. Accurate diagnosis not only facilitates efficient implementation of examinations and treatments but also enables precise disease management. This process relies on "clinical reasoning", which is the cognitive ability medical students require when evaluating and managing patient cases[ 1 ]. Diagnostic decision-making is a complex process involving two primary modes of thinking according to Epstein and Hammond's dual-process theory: intuitive and analytical [ 2 , 3 ]. Intuitive thinking, which is based on rapid, unconscious pattern recognition, is suitable for routine cases or time-sensitive situations but remains vulnerable to emotional fluctuations that may compromise decision accuracy. Analytical thinking, which is more deliberate and logic-driven, yields superior judgment in complex or uncertain scenarios. These complementary cognitive modes are flexibly employed on the basis of case complexity, experience level, and clinician confidence. However, studies reveal that clinical novices disproportionately rely on intuitive thinking [ 4 ], with approximately 75% of diagnostic errors linked to clinical reasoning deficiencies manifesting as knowledge gaps, incomplete data collection, or inadequate hypothesis verification [ 5 ]. Consequently, clinicians' capacity to deliver safe, high-quality care substantially depends on their reasoning proficiency, cognitive patterns, and judgment competence [ 6 ]. Although modern medical curricula prioritize patient safety, the development of clinical reasoning has not been consistently emphasized as an explicit educational objective. Most educators assume that clinical reasoning naturally emerges through experiential learning [ 7 – 9 ]. However, systematic reviews have demonstrated that teaching strategies that integrate both analytical and nonanalytical reasoning significantly improve diagnostic accuracy [ 10 , 11 ]. Understanding physicians' cognitive patterns, decision-making processes, and potential pitfalls is essential not only for clinical trainees and experienced practitioners but also for informing targeted pedagogical approaches. The Cognitive Reflection Test (CRT), a three-item instrument [ 12 ], evaluates the ability to override intuitive responses through deliberate reflection. Standard CRT scoring (intuitive vs. reflective scores) measures this cognitive transition, with evidence suggesting that CRT primarily assesses reflective analytical thinking [ 13 ]. Research on early-stage clinical learners indicates that while clinical expertise development correlates with increasingly automated analytical thinking, novices require dedicated training to strengthen this capacity[ 14 ]. Consequently, contemporary medical education increasingly adopts multimodal approaches, including problem-based learning (PBL), case-based learning (CBL), and evidence-based medicine (EBM). The integrated EBM-CBL-PBL model, which is effective at cultivating both analytical and intuitive thinking while enhancing clinical reasoning, has gained widespread adoption [ 15 , 16 ]. CRT studies among clinical interns reveal that comparing "expert" versus "novice" responses helps trainees understand cognitive differences in clinical decision making a crucial insight for developing clinical reasoning and improving real-world medical judgments. This study aims to assess the performance of intuitive (System 1) and analytical (System 2) thinking across different clinical training stages via the CRT. Additionally, we evaluate the impact of evidence-based medicine (EBM) combined with case-based learning (CBL) and problem-based learning (PBL) on clinical interns' decision-making ability. By analysing trends and gender differences in cognitive processing, evidence-based insights for enhancing clinical reasoning and diagnostic accuracy in medical education can be obtained. Method Study design and participants This study employed a two-phase design to investigate medical education interventions at the First Affiliated Hospital of Anhui University of Science and Technology. The investigation involved medical students undertaking clinical placements (September–December 2024) and hospital-employed clinicians at various career stages. In Phase I, 93 participants were recruited from 200 distributed questionnaires: 34 trainees (22 males/12 females, mean age 22.09 ± 1.62 years), 27 resident physicians (16 males/11 females, mean age 28.41 ± 2.12 years), 21 attending physicians (12 males/9 females, mean age 32.57 ± 3.03 years), and 11 consultant-level physicians (5 males/6 females, mean age 51.29 ± 8.82 years). The participants voluntarily completed cognitive reflection tests (CRTs) distributed via SMS. Completion of the questionnaire implied consent, with anonymity assured. Eligibility criteria included ≥ 2 years of clinical practice for clinicians or appropriate placement status for students, while individuals with prior CRT exposure or unexplained absences were excluded. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Anhui University of Science and Technology. In Phase II, 36 general surgery trainees (21 males/15 females, mean age 21.72 ± 1.04 years) were randomly assigned via a random number table method to either the traditional teaching group or the EBM-CBL-PBL integrated teaching group. Following the instructional program, researchers sent survey links via SMS for participants to complete both the Cognitive Reflection Test (CRT) questionnaire and a teaching satisfaction survey (Supplementary 1). Teaching methods 1. Conventional Teaching Group The conventional teaching group strictly followed the traditional curriculum. Instructors selected typical cases from hospitalized patients to guide students through clinical exposure, explaining disease etiology, pathogenesis, diagnosis, and treatment principles. The students were instructed to integrate theoretical knowledge with clinical practice, gradually developing their clinical reasoning and problem-solving skills. The total teaching duration was 18 credit hours. 2. EBM-CBL-PBL Integrated Teaching Group Building upon the conventional approach, the EBM-CBL-PBL group incorporated evidence-based medicine (EBM), case-based learning (CBL), and problem-based learning (PBL). Implementation details: Instructors designed open-ended questions based on CBL cases, covering etiology, pathogenesis, differential diagnosis, treatment options, and prevention. Missing but clinically common positive signs and test results were supplemented. Students worked in groups to research relevant literature via textbooks and the "5S" pyramid model (evidence hierarchy) of evidence-based medicine (EBM) [ 17 ]. The search strategy will follow the EBM evidence pathway—from systematic reviews to clinical guidelines—and culminate in a group discussion followed by a presentation (Supplementary 2). Total teaching duration: 18 credit hours. Questionnaires 1. Cognitive Reflection Test (CRT) An internationally validated CRT assessed cognitive ability via QR-code surveys. Below are the CRT items with intuitive (incorrect) and analytical (correct) answers: Question 1(Q1) : A bat and a ball cost $ 1.10 in total. The bat costs $ 1.00 more than the ball does. How much does the ball cost? _____ cents. Intuitive answer: $ 0.10 ( since 1.10–1.00 = 0.10). Correct answer: $ 0.05 ( if the ball was $ 0.10, the bat would be $ 1.10, totaling $ 1.20—which is wrong. The right answer is as follows: ball = $ 0.05, bat= $ 1.05, total = $ 1.10). Question 2(Q2) : If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? _____ minutes. Intuitive answer: 100 minutes. Correct answer: Just 5 minutes ( Since each individual machine needs 5 minutes to make one item, having 100 machines working simultaneously means that they can produce 100 items in the same 5-minute time frame). Question 3(Q3) : In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake? _____ days. Intuitive answer: 24 days (half of 48). Correct answer: 47 days (if the area doubles daily, then one day before full coverage, the lily pads must have covered half the pond). 3. Satisfaction Survey A Likert scale[ 18 ] was used to evaluate satisfaction with both methods within 24 hours postintervention (Supplementary 1). The students rated five domains[ 19 ]: satisfaction with the teaching method, depth of content comprehension, proactive learning engagement, teacher‒student interaction, and classroom atmosphere. Statistical analysis All data analyses were performed via SPSS Statistics (version 22.0; SPSS Inc., Chicago, IL, USA). Continuous variables are presented as medians (IQRs) and were compared via the Mann‒Whitney U test. Categorical data are expressed as n (%) and were analysed via either χ² tests or corrected χ² tests, as appropriate. Using the different CRT test items as the primary research units, we employed generalized linear mixed models (GLMMs) to examine potential associations. In these models, correct answers (analytical responses) served as the dependent variable, whereas gender and subject populations were treated as explanatory variables. A two-tailed P value of < 0.05 was considered statistically significant. Results Characteristics of analytical thinking across genders and clinical experience levels The difficulty of the three CRT tests (CRT-Q1, CRT-Q2, and CRT-Q3) increases progressively. We established CRT-Qall as a composite measure representing a uniform answer, requiring reflective (non-intuitive) responses to CRT-Q1, Q2, and Q3, with CRT-Reflective denoting the analytical response mode and exhibiting characteristics of analytical thinking. The analysis revealed distinct patterns in analytical thinking across different clinical experience groups( Table 1 ). Among early clinical trainees, 41.18% (n = 14) demonstrated consistent analytical responses across all three CRT questions, whereas 11.76% (n = 4) relied exclusively on intuitive answers. Notably, 78.57% of the male trainees (n = 11) achieved perfect analytical scores. The results revealed that analytical responses predominated (CRT-Q1: 64.71%, n = 22; CRT-Q2: 70.59%, n = 24; CRT-Q3: 73.53%, n = 25), with males representing 68.18–76.00% of the analytical responders. Intuitive responses accounted for 17.65–29.41% of the responses across questions, with male representation varying from 33.33–50%. Resident physicians showed improved analytical consistency, with 51.85% (n = 14) answering all the questions analytically and 14.81% (n = 4) remaining fully intuitive. Male residents comprised 57.14% (n = 8) of perfect analytical scorers. A question-level analysis revealed higher analytical response rates (CRT-Q1: 64.71%, n = 17; CRT-Q2: 77.78%, n = 21; CRT-Q3: 74.07%, n = 20), with males constituting 58.82–65.00% of the analytical responders. Intuitive responses decreased to 18.52–29.63%, with male representation at 40.00–50%. Attending physicians or consultant-level physicians exhibited the strongest analytical performance, with 57.14% (n = 20) achieving all CRT-Reflective answers and only 8.57% (n = 3) providing entirely intuitive answers. Interestingly, male representation among perfect scorers declined to 50.00% (n = 10). While the analytical response rates remained high (62.86–74.29%), the male contribution to the analytical responses progressively decreased from CRT-Q1 (50.00%, n = 11) to CRT-Q3 (38.46%, n = 10). Conversely, males represented 58.33–80.00% of intuitive answers, especially in CRT-Q3 (80.00%, n = 4 of 5) Table 1 Summary of CRT questionnaire results across different clinical experience groups Group Trainees Resident physicians Attending physicians consultant-level physicians Pearson χ² P value N 34 27 21 14 Age (years) 22.09 ± 1.62 28.41 ± 2.12 32.57 ± 3.03 51.29 ± 8.82 Male (n, %) 22 (64.71) 16 (59.26) 12 (57.14) 6 (42.86) 1.969 0.579 Q1 CRT-Reflective(n,%) 22 (64.71) 17 (62.96) 14 (66.67) 8 (57.14) CRT-Intuitive (n,%) 10 (29.41) 8 (29.63) 6 (28.57) 6 (42.86) Incorrect (n,%) 2 (5.89) 2 (7.41) 1 (4.76) 0 Q2 CRT-Reflective (n,%) 24 (70.59) 21 (77.78) 17 (80.94) 9 (64.29) CRT-Intuitive (n,%) 6 (17.65) 5 (18.52) 2 (9.53) 3 (21.83) Incorrect (n,%) 4 (11.76) 1 (3.70) 2 (9.53) 2 (13.88) Q3 CRT-Reflective (n,%) 25 (73.53) 20 (74.07) 16 (76.19) 10 (71.42) CRT-Intuitive (n,%) 6 (17.65) 5 (18.52) 3 (14.29) 2 (14.29) Incorrect (n,%) 3 (8.82) 2 (7.41) 2 (9.52) 2 (14.29) All-Reflective (n,%) 14 (41.18) 14 (51.85) 13 (61.90) 7 (50.00) All-Intuitive (n,%) 4 (11.76) 4 (14.81) 1 (4.76) 2 (14.29) CRT-Mixed (n,%) 16 (47.06) 9 (33.33) 7 (33.34) 3 (35.71) Note : CRT-Reflective represents analytical thinking answers; CRT-Intuitive represents intuitive thinking answers; Incorrect indicates wrong answers; All-Reflective means that all three CRT questions were answered analytically; All-Intuitive means that all three CRT questions were answered intuitively; CRT-Mixed indicates mixed answer patterns. Gender differences in CRT-reflective performance There was no significant difference in CRT-Reflective (analytical thinking) scores among the different sex test groups. The results of the Mann‒Whitney U test and chi-square test for each CRT question (Q1‒Q3 and QAll) revealed that there was no statistically significant difference in analytical thinking between men and women (P > 0.05) (Table 2 ). Specifically, the proportion of males with analytical thinking in the CRT-Q1 was 36/56 (64.3%), which was similar to the percentage of females with analytical thinking of 25/40 (62.5%) (P = 0.858). There was no significant difference between males (44/56 (78.6%) and females (27/40 (67.5%)) in the CRT-Q2 score (P = 0.223). CRT-Q3 and QAll also showed no sex differences (P = 0.455 and 0.492). Notably, although males constituted a slightly greater proportion of analytical thinking in the CRT-Q2 and CRT-Q3 (a difference of approximately 8–11 percentage points), the test results support gender homogeneity in analytical thinking performance. Table 2 R×C chi-square test results of CRT-reflective by gender CRT measure Age median (IQR) Gender Test populations Male (n = 56) Female (n = 40) Trainees (n = 34) Residents (n = 27) Attendings (n = 21) consultant-level (n = 14) CRT-Q 1 CRT–Reflective 28.0(23.0;32.0) 36 25 22 17 14 8 Non-Reflective 28.0(23;38.0) 20 15 12 10 7 6 Mann Whitney U/X 2 999.000 a 0.032 b 0.360 b P value 0.601 0.858 0.948 CRT-Q 2 CRT–Reflective 29.0(23.0;34.0) 44 27 24 21 17 9 Non-Reflective 26.0(23.0;31.0) 12 13 10 6 4 5 Mann Whitney U/X 2 876.500 a 1.485 b 1.618 b P value 0.927 0.223 0.655 CRT-Q 3 CRT–Reflective 29.0(23.0;34.0) 43 28 25 20 16 10 Non-Reflective 26.0(23.0;31.0) 13 12 9 7 5 4 Mann Whitney U/X 2 840.000 a 0.558 b 0.104 b P value 0.691 0.455 0.991 CRT-Q All CRT–Reflective 29.0(23.0;34.5) 29 19 14 14 13 7 Non-Reflective 26.0(23.0;32.5) 27 21 20 13 8 7 Mann Whitney U/X 2 1046.000 a 0.473 b 2.286 b P value 0.436 0.492 0.515 Note : a: Mann‒Whitney U test; b: R×C chi‒square test. Interaction effects between gender and clinical experience Taking different CRT test subjects, CRT-reflective as the dependent variable, and gender and different test populations as the explanatory variables, a generalized linear mixed model (GLMM) was used to analyse the associated factors(Table 3 ). There was a significant interaction effect between gender and the test population on CRT-Reflective performance (P = 0.005). Specifically, female attending physicians (or those of higher rank) exhibited the strongest tendency toward analytical thinking, with an odds ratio (OR) of 5.919 (95% CI [1.720, 20.373]), which was significantly greater than that of the male intern control group. Female residents also showed a dominant trend (OR = 2.711, P = 0.036). However, in the intern group, there was no statistically significant gender difference (female vs male OR = 0.580, P = 0.357). There was no significant gender difference between the different CRT questions (Q1-Q3) (P > 0.65), and there was no significant interaction effect between the test population and question type (P > 0.79). This may indicate that senior female physicians (attending and above) may be more inclined to adopt an analytical mindset in clinical decision-making, an advantage that has not been observed in junior physicians. Table 3 GLMM analysis of factors associated with analytical thinking Variable β SE χ² P value OR 95% CI Fixed effects Intercept −0.814 0.4618 3.108 0.078 0.443 0.179–1.059 Test populations (attending physicians or higher) Trainees -0.721 0.6290 1.314 0.252 0.486 [0.142, 1.668] Residents -0.432 0.6588 0.429 0.512 0.649 [0.179, 2.362] Gender ( Male) Female −0.545 0.5921 0.848 0.357 0.580 0.182–1.850 CRT test (CRT-Q3) CRT-Q1 0.662 0.5951 1.239 0.266 1.940 [0.604, 6.226] CRT-Q2 -0.099 0.6226 0.025 0.874 0.906 [0.267, 3.070] Interactions of test populations and gender (male trainees) Female attending and above 1.778 0.6306 7.951 0.005 5.919 [1.720, 20.373] Female residents 0.997 0.6683 2.228 0.036 2.711 [0.732, 10.047] Interactions of CRT test and gender (male answer CRT-Q3) Female answer CRT-Q1 -0.271 0.6462 0.175 0.675 0.763 [0.215, 2.707] Female answer CRT-Q2 0.228 0.6753 0.114 0.736 1.256 [0.334, 4.718] Interactions of test populations and CRT test (attending or higher physicians answer CRT-Q3) Trainees answer CRT-Q1 -0.101 0.7607 0.018 0.895 0.904 [0.204, 4.016] Trainees answer CRT-Q2 0.150 0.7901 0.036 0.849 1.162 [0.247, 5.467] Residents answer CRT-Q1 -0.022 0.7958 0.001 0.978 0.978 [0.206, 4.653] Residents answer CRT-Q2 -0.216 0.8467 0.065 0.798 0.806 [0.153, 4.234] The value of EBM-CBL-PBL pedagogy in developing analytical thinking There were no significant differences in age, gender and baseline CRT question answers (CRT-Q1, CRT-Q2, CRT-Q3, CRT-Qall) between the general teaching group and the EBM-CPL-PBL integrated teaching model group (P > 0.05). However, the satisfaction score of the EBM-CPL-PBL group was significantly greater than that of the general teaching group, and the difference was statistically significant (P < 0.001). Moreover, in the general teaching group, only 44.44% of the students considerred the three questions analytically, whereas in the EBM-CPL-PBL group, the percentage increased to 61.11%. The proportion of intuitive thinking was 22.22% in the general teaching group, but decreased to 16.67% in the EBM-CPL-PBL group, indicating that the latter can effectively reduce students' dependence on intuitive thinking. The analysis of the CRT test questions with different difficulty coefficients revealed that the percentages of analytical thinking in Q1, Q2 and Q3 in the EBM-CPL-PBL group were 66.67%, 77.78% and 83.33%, respectively, which were greater than those in the general teaching group (61.11%, 55.56% and 61.11%, respectively). However, the difference was not statistically significant (P = 0.248, 0.080 and 0.068) (Table 4 ). Table 4 Single Factor Analysis of EBM-CBL-PBL Pedagogy Outcomes Variable Traditional (n = 18) EBM-CBL-PBL (n = 18) Mann‒Whitney U/X 2 P value Age (years) 22.0 (21.0–23.0) 22.0 (21.0-22.25) 150.000 a 0.690 Male (%) 11 (61.11) 10 (55.56) 0.114 b 0.735 Analytical Responses CRT-Q1 (%) 12 (66.67) 15 (83.33) 1.33 b 0.248 CRT-Q2 (%) 9 (50.00) 14 (77.78) 3.06 b 0.080 CRT-Q3 (%) 10 (55.56) 15 (83.33) 3.33 b 0.068 CRT-QAll (%) 8 (44.44) 11 (61.11) 2.22 b 0.136 Satisfaction Score 69.0 (62.0–72.0) 74.0 (71.5–78.0) 58.500 a < 0.001 Note : a: Mann‒Whitney U test; b: R×C chi‒square test. A generalized linear model was employed to further analyse interactions between gender and different teaching modalities concerning their impact on reflective cognitive skills as measured by CRT tests. Notably, students who received instruction through EBM-CPL-PBL were more likely to have enhanced analytical thinking abilities (OR = 3.39; 95% CI: [1.52–7.56]; P = 0.003). Nevertheless, there were no statistically significant differences regarding gender or interaction terms involving groups × gender (P > 0 .05)(Table 5 ). This may suggest that female classmates' advantage in showing CRT-reflective ability is not improved by the EBM-CPL-PBL integrated teaching model. Table 5 Interactive effects of gender and teaching mode on CRT-reflective performance Variable β SE χ² p-value OR 95% CI Intercept 0.85 0.32 7.12 0.008 2.34 [1.25, 4.38] teaching group[EBM-CBL-PBL] 1.22 0.41 8.84 0.003 3.39 [1.52, 7.56] Gender (Female) −0.31 0.38 0.67 0.413 0.73 [0.35, 1.54] Interactions of teaching group and gender 0.45 0.53 0.72 0.396 1.57 [0.56, 4.41] Note : Generalized linear mixed models (GLMMs) Discussion and conclusion In the process of training preclinical medical students, the development of clinical thinking is crucial, particularly in fostering logical reasoning and analytical skills. Research indicates that males tend to excel in logical reasoning and analytical abilities, demonstrating a greater propensity for rapid logical integration [ 20 ]. Conversely, females exhibit greater potential for progressive learning[ 21 ], which may be attributed to physiological and cognitive development as well as sociocultural factors. In the field of medical education, especially in the process of clinical diagnosis and decision-making, the strength of an individual's logical reasoning ability directly affects the accuracy of clinical judgment [ 22 ]. However, there remains a paucity of research materials available for reference on the characteristics of gender differences in the cultivation of logical analytical thinking. Long-term clinical experience plays a pivotal role in enhancing the analytical thinking abilities of medical students. Unlike novices, who predominantly rely on intuitive thinking, the accumulation of clinical experience enables medical students to develop a more systematic and structured thought process, thereby improving the precision of clinical decision-making. Studies reveal that novices in medical diagnosis often depend on intuitive methods, drawing conclusions primarily through pattern recognition or rapid judgment[ 23 ]. However, this approach is vulnerable to cognitive biases, increasing the likelihood of misdiagnosis or missed diagnoses[ 24 ]. In contrast, prolonged clinical training facilitates the gradual development of more sophisticated analytical modes among medical students. This encompasses decision-making on the basis of systematic information gathering, hypothesis testing, logical reasoning, and critical analysis [ 25 ]. Consequently, experienced physicians often exhibit enhanced analytical thinking skills closely correlated with extensive clinical experience. Throughout the diagnostic process, they demonstrate an exceptional capacity to integrate empirical knowledge, reasoning, and clinical evidence, ultimately reducing diagnostic errors [ 26 ]. Males may possess an innate predisposition for logical thinking, a finding corroborated by our research. Additionally, our study reveals that senior female physicians exhibit high levels of analytical thinking during the decision-making process, surpassing their male counterparts in certain instances. This enhancement is attributable primarily to long-term clinical practice rather than solely to educational training at the medical school level. This suggests that practical training can mitigate innate gender differences in women, promoting their analytical capabilities. In other words, long-term clinical experience significantly enhances the analytical ability of female physicians in clinical diagnosis. Interestingly, short-term training via the EBM-CPL-PBL integrated teaching model did not sustain such positive effects. Therefore, in the design of preclinical medical education, in addition to classroom teaching, training on the basis of actual clinical experience should be emphasized. Examples include clerkship, case discussion, and simulation-based training. Educators should pay more attention to developing students' analytical thinking so that they can make clinical reasoning and decision making more efficient in their future careers. In this study, the EBM-CPL-PBL integrated teaching model effectively improved the analytical thinking ability of preclinical interns, but in terms of gender, there was no significant difference in the rate of improvement in thinking ability between males and females. This finding indicates that although the preclinical pedagogical training mode plays a positive role in improving the analytical thinking of medical students, its advantage for female students is not obvious, and long-term clinical experience training is more beneficial to the training of female doctors' analytical thinking. Therefore, to maximize the effect of medical education, we recommend strengthening analytical thinking training at the preclinical stage while providing more opportunities for experience accumulation at the clinical practice stage, especially for female physicians, to further narrow the gender gap and improve the quality of overall medical decision-making. Abbreviations CRT Cognitive Reflection Test GLMM Generalized Linear Mixed Model EBM Evidence-Based Medicine CBL Case Based Learning PBL Problem Based Learning SMS Short Message Service IQR Interquartile Range OR Odds Ratio Declarations Acknowledgements We would like to extend our sincere gratitude to the Medical Administration Department and Education Office for providing researcher information. Authors’ contributions Xianzhi Chen: Conceptualization, methodology, investigation, writing original draft, writing review & editing, supervision, project administration. Manman Xu: Conceptualization, Methodology, Investigation, Writing - Original Draft, Writing - Review & Editing. Deshun Liu: Project administration, Funding acquisition. Fang Yang: Methodology, Validation, Resources. Guobao Xu: Software, formal analysis, data curation. Huaichen Yang: Investigation, Visualization. Qizhu Feng: Resources, Supervision. Lei Xu: Conceptualization, methodology, writing - original draft, writing - review & editing. Funding This work has been funded by the Quality Engineering Program for Higher Education Institutions in Anhui Province (2023) (Funding No. 2023xm1073); The Quality Engineering Program for Higher Education Institutions in Anhui Province (2024); 2023 Medical Special Cultivation Project of Anhui University of Science and Technology (Funding No. YZ2023H2C018 ). Data availability No datasets were generated or analysed during the current study. Ethics approval and consent to participate: This study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and received formal approval from the Institutional Review Board (IRB) of the Ethics Committee of the First Affiliated Hospital of Anhui University of Science and Technology, China(No.2023-KY-H2C018-001). All participants were fully informed and consented to participate in the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Pelaccia T, Tardif J, Triby E, Charlin B. An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory. Med Educ Online. 2011 ;16. Epstein RM, Hammond KR. Dual-process theories in clinical decision-making. Journal of Clinical Psychology. 2011;67(4):456-467. Kahneman D. Thinking, Fast and Slow. Farrar, Straus and Giroux; 2011. Tay SW, Ryan P, Ryan CA. 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Of studies, syntheses, synopses, summaries, and systems: the "5S" evolution of information services for evidence-based healthcare decisions. Evid Based Med. 2006, 11(6): 162-164. Likert, R. A technique for the measurement of attitudes. Archives of Psychology, 1932,140, 1-55. Qi Y, Li C, Sun P, Zhang X, Kong Q, Wu D, et al. Application of PBL combined with 3D anatomy software in clinical internship teaching of Traditional Chinese Medicine traumatology based on questionnaire survey. Chinese Continuing Medical Education,2024, 16(12): 152-156. Chen CS, Knep E, Han A, Ebitz RB, Grissom NM. Sex differences in learning from exploration. Elife. 2021, 19; 10:e69748. Halpern DF, Sex differences in cognitive abilities (4th ed.). Psychology Press. 2012. Norman GR, Monteiro SD, Sherbino J, Ilgen JS, Schmidt HG, Mamede S. The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking. Acad Med. 2017, 92(1):23-30. Eva KW, What every teacher needs to know about clinical reasoning. Medical Education. 2005, 39(1), 98-106. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013,22(Suppl 2) : ii58-ii64. Norman GR, Grierson LEM, Sherbino J, Hamstra SJ, Schmidt HG, Mamede S. Expertise in Medicine and Surgery. In: Ericsson KA, Hoffman RR, Kozbelt A, Williams AM, eds. The Cambridge Handbook of Expertise and Expert Performance. Cambridge Handbooks in Psychology. Cambridge University Press. 2018:331-355. Schmidt HG, Boshuizen, HPA., On acquiring expertise in medicine. Educ Psychol Rev. 1993, 5, 205–221. Additional Declarations No competing interests reported. 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Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Manman","middleName":"","lastName":"Xu","suffix":""},{"id":453899054,"identity":"6f340e7c-f917-483b-b499-6b011633a828","order_by":2,"name":"Deshun Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Deshun","middleName":"","lastName":"Liu","suffix":""},{"id":453899055,"identity":"c081e5ae-c0fb-42bf-90db-e3667b98d404","order_by":3,"name":"Fang Yang","email":"","orcid":"","institution":"Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Yang","suffix":""},{"id":453899056,"identity":"a3adfe44-6b71-42fe-9d34-098e90c1e09b","order_by":4,"name":"Guobao Xu","email":"","orcid":"","institution":"Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Guobao","middleName":"","lastName":"Xu","suffix":""},{"id":453899057,"identity":"2fed2be6-3f1b-481d-8530-030571310ae3","order_by":5,"name":"Huaichen Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Huaichen","middleName":"","lastName":"Yang","suffix":""},{"id":453899058,"identity":"1f2d872f-93e3-4e37-9a1f-b0850f73f2a9","order_by":6,"name":"Qizhu Feng","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Qizhu","middleName":"","lastName":"Feng","suffix":""},{"id":453899059,"identity":"924c3d9e-2ea1-4c1c-a4ef-2f1bb0e2a8ae","order_by":7,"name":"Lei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACfmbmAwcS//xntj/eQKQWyXa2xAMPG5jZGc4cIFKLQT+P8UGgFn6GGwnEamHmMTiQuINNmnHm4403GGpsoglqMWdmKziQeIbHmFk6rdiC4VhabgMhLZbNzBsOJLBJJLNJ55hJMDYcJqzF4DCDAVCLQX2P5BmitbAA/dKWwCwhwUOkFslmtoQDCWcOMBvwAP2SQIxf+PkPH/74owKohf3wxhsfamwIa0FxpEQCKcohWkjVMQpGwSgYBSMDAAA4kECAq9VWEwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-04-13 14:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6439748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6439748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82488756,"identity":"9df06130-3778-4fe4-8f4b-007771ee35b3","added_by":"auto","created_at":"2025-05-12 06:16:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6439748/v1/01981376-5fcb-4c0d-ba1d-6ba46f877ed4.pdf"},{"id":82344335,"identity":"23be41f3-3d92-4866-8958-61d24803dbef","added_by":"auto","created_at":"2025-05-09 09:45:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15514,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1Questionnairesurvey.docx","url":"https://assets-eu.researchsquare.com/files/rs-6439748/v1/da596c00dd73f3a074914850.docx"},{"id":82344331,"identity":"b4f3e2d3-abf4-4784-b32b-a1a2e038d504","added_by":"auto","created_at":"2025-05-09 09:45:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17185,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2TeachingDesign.docx","url":"https://assets-eu.researchsquare.com/files/rs-6439748/v1/b0eafe8f729938bfaa32ac4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Analytical Thinking in Early-Career Physicians: Evaluating the EBM-CBL-PBL Integrated Teaching Model","fulltext":[{"header":"Background","content":"\u003cp\u003eThe clinical clerkship phase represents a critical learning period in medical education, particularly for developing diagnostic clinical reasoning. Accurate diagnosis not only facilitates efficient implementation of examinations and treatments but also enables precise disease management. This process relies on \"clinical reasoning\", which is the cognitive ability medical students require when evaluating and managing patient cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Diagnostic decision-making is a complex process involving two primary modes of thinking according to Epstein and Hammond's dual-process theory: intuitive and analytical [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Intuitive thinking, which is based on rapid, unconscious pattern recognition, is suitable for routine cases or time-sensitive situations but remains vulnerable to emotional fluctuations that may compromise decision accuracy. Analytical thinking, which is more deliberate and logic-driven, yields superior judgment in complex or uncertain scenarios. These complementary cognitive modes are flexibly employed on the basis of case complexity, experience level, and clinician confidence. However, studies reveal that clinical novices disproportionately rely on intuitive thinking [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with approximately 75% of diagnostic errors linked to clinical reasoning deficiencies manifesting as knowledge gaps, incomplete data collection, or inadequate hypothesis verification [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, clinicians' capacity to deliver safe, high-quality care substantially depends on their reasoning proficiency, cognitive patterns, and judgment competence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough modern medical curricula prioritize patient safety, the development of clinical reasoning has not been consistently emphasized as an explicit educational objective. Most educators assume that clinical reasoning naturally emerges through experiential learning [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, systematic reviews have demonstrated that teaching strategies that integrate both analytical and nonanalytical reasoning significantly improve diagnostic accuracy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Understanding physicians' cognitive patterns, decision-making processes, and potential pitfalls is essential not only for clinical trainees and experienced practitioners but also for informing targeted pedagogical approaches.\u003c/p\u003e \u003cp\u003eThe Cognitive Reflection Test (CRT), a three-item instrument [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], evaluates the ability to override intuitive responses through deliberate reflection. Standard CRT scoring (intuitive vs. reflective scores) measures this cognitive transition, with evidence suggesting that CRT primarily assesses reflective analytical thinking [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Research on early-stage clinical learners indicates that while clinical expertise development correlates with increasingly automated analytical thinking, novices require dedicated training to strengthen this capacity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consequently, contemporary medical education increasingly adopts multimodal approaches, including problem-based learning (PBL), case-based learning (CBL), and evidence-based medicine (EBM). The integrated EBM-CBL-PBL model, which is effective at cultivating both analytical and intuitive thinking while enhancing clinical reasoning, has gained widespread adoption [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. CRT studies among clinical interns reveal that comparing \"expert\" versus \"novice\" responses helps trainees understand cognitive differences in clinical decision making a crucial insight for developing clinical reasoning and improving real-world medical judgments.\u003c/p\u003e \u003cp\u003eThis study aims to assess the performance of intuitive (System 1) and analytical (System 2) thinking across different clinical training stages via the CRT. Additionally, we evaluate the impact of evidence-based medicine (EBM) combined with case-based learning (CBL) and problem-based learning (PBL) on clinical interns' decision-making ability. By analysing trends and gender differences in cognitive processing, evidence-based insights for enhancing clinical reasoning and diagnostic accuracy in medical education can be obtained.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and participants\u003c/h2\u003e\n \u003cp\u003eThis study employed a two-phase design to investigate medical education interventions at the First Affiliated Hospital of Anhui University of Science and Technology. The investigation involved medical students undertaking clinical placements (September\u0026ndash;December 2024) and hospital-employed clinicians at various career stages. In Phase I, 93 participants were recruited from 200 distributed questionnaires: 34 trainees (22 males/12 females, mean age 22.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 years), 27 resident physicians (16 males/11 females, mean age 28.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12 years), 21 attending physicians (12 males/9 females, mean age 32.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03 years), and 11 consultant-level physicians (5 males/6 females, mean age 51.29\u0026thinsp;\u0026plusmn;\u0026thinsp;8.82 years). The participants voluntarily completed cognitive reflection tests (CRTs) distributed via SMS. Completion of the questionnaire implied consent, with anonymity assured. Eligibility criteria included\u0026thinsp;\u0026ge;\u0026thinsp;2 years of clinical practice for clinicians or appropriate placement status for students, while individuals with prior CRT exposure or unexplained absences were excluded. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Anhui University of Science and Technology. In Phase II, 36 general surgery trainees (21 males/15 females, mean age 21.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04 years) were randomly assigned via a random number table method to either the traditional teaching group or the EBM-CBL-PBL integrated teaching group. Following the instructional program, researchers sent survey links via SMS for participants to complete both the Cognitive Reflection Test (CRT) questionnaire and a teaching satisfaction survey (Supplementary 1).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTeaching methods\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Conventional Teaching Group\u003c/h3\u003e\n\u003cp\u003eThe conventional teaching group strictly followed the traditional curriculum. Instructors selected typical cases from hospitalized patients to guide students through clinical exposure, explaining disease etiology, pathogenesis, diagnosis, and treatment principles. The students were instructed to integrate theoretical knowledge with clinical practice, gradually developing their clinical reasoning and problem-solving skills. The total teaching duration was 18 credit hours.\u003c/p\u003e\n\u003ch3\u003e2. EBM-CBL-PBL Integrated Teaching Group\u003c/h3\u003e\n\u003cp\u003eBuilding upon the conventional approach, the EBM-CBL-PBL group incorporated evidence-based medicine (EBM), case-based learning (CBL), and problem-based learning (PBL). Implementation details: Instructors designed open-ended questions based on CBL cases, covering etiology, pathogenesis, differential diagnosis, treatment options, and prevention. Missing but clinically common positive signs and test results were supplemented. Students worked in groups to research relevant literature via textbooks and the \u0026quot;5S\u0026quot; pyramid model (evidence hierarchy) of evidence-based medicine (EBM) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. The search strategy will follow the EBM evidence pathway\u0026mdash;from systematic reviews to clinical guidelines\u0026mdash;and culminate in a group discussion followed by a presentation (Supplementary 2). Total teaching duration: 18 credit hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaires\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Cognitive Reflection Test (CRT)\u003c/p\u003e\n\u003cp\u003eAn internationally validated CRT assessed cognitive ability via QR-code surveys. Below are the CRT items with intuitive (incorrect) and analytical (correct) answers:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestion 1(Q1)\u003c/strong\u003e: A bat and a ball cost \u003cspan\u003e$\u003c/span\u003e1.10 in total. The bat costs \u003cspan\u003e$\u003c/span\u003e1.00 more than the ball does. How much does the ball cost? _____ cents.\u003c/p\u003e\n\u003cp\u003eIntuitive answer: \u003cspan\u003e$\u003c/span\u003e0.10 ( since 1.10\u0026ndash;1.00\u0026thinsp;=\u0026thinsp;0.10).\u003c/p\u003e\n\u003cp\u003eCorrect answer: \u003cspan\u003e$\u003c/span\u003e0.05 ( if the ball was \u003cspan\u003e$\u003c/span\u003e0.10, the bat would be \u003cspan\u003e$\u003c/span\u003e1.10, totaling \u003cspan\u003e$\u003c/span\u003e1.20\u0026mdash;which is wrong. The right answer is as follows: ball = \u003cspan\u003e$\u003c/span\u003e0.05, bat=\u003cspan\u003e$\u003c/span\u003e1.05, total = \u003cspan\u003e$\u003c/span\u003e1.10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestion 2(Q2)\u003c/strong\u003e: If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? _____ minutes.\u003c/p\u003e\n\u003cp\u003eIntuitive answer: 100 minutes.\u003c/p\u003e\n\u003cp\u003eCorrect answer: Just 5 minutes ( Since each individual machine needs 5 minutes to make one item, having 100 machines working simultaneously means that they can produce 100 items in the same 5-minute time frame).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestion 3(Q3)\u003c/strong\u003e: In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake? _____ days.\u003c/p\u003e\n\u003cp\u003eIntuitive answer: 24 days (half of 48).\u003c/p\u003e\n\u003cp\u003eCorrect answer: 47 days (if the area doubles daily, then one day before full coverage, the lily pads must have covered half the pond).\u003c/p\u003e\n\u003ch3\u003e3. Satisfaction Survey\u003c/h3\u003e\n\u003cp\u003eA Likert scale[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to evaluate satisfaction with both methods within 24 hours postintervention (Supplementary 1). The students rated five domains[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]: satisfaction with the teaching method, depth of content comprehension, proactive learning engagement, teacher‒student interaction, and classroom atmosphere.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll data analyses were performed via SPSS Statistics (version 22.0; SPSS Inc., Chicago, IL, USA). Continuous variables are presented as medians (IQRs) and were compared via the Mann‒Whitney U test. Categorical data are expressed as n (%) and were analysed via either \u0026chi;\u0026sup2; tests or corrected \u0026chi;\u0026sup2; tests, as appropriate. Using the different CRT test items as the primary research units, we employed generalized linear mixed models (GLMMs) to examine potential associations. In these models, correct answers (analytical responses) served as the dependent variable, whereas gender and subject populations were treated as explanatory variables. A two-tailed P value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of analytical thinking across genders and clinical experience levels\u003c/h2\u003e \u003cp\u003eThe difficulty of the three CRT tests (CRT-Q1, CRT-Q2, and CRT-Q3) increases progressively. We established CRT-Qall as a composite measure representing a uniform answer, requiring reflective (non-intuitive) responses to CRT-Q1, Q2, and Q3, with CRT-Reflective denoting the analytical response mode and exhibiting characteristics of analytical thinking. The analysis revealed distinct patterns in analytical thinking across different clinical experience groups( Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong early clinical trainees, 41.18% (n = 14) demonstrated consistent analytical responses across all three CRT questions, whereas 11.76% (n = 4) relied exclusively on intuitive answers. Notably, 78.57% of the male trainees (n = 11) achieved perfect analytical scores. The results revealed that analytical responses predominated (CRT-Q1: 64.71%, n = 22; CRT-Q2: 70.59%, n = 24; CRT-Q3: 73.53%, n = 25), with males representing 68.18–76.00% of the analytical responders. Intuitive responses accounted for 17.65–29.41% of the responses across questions, with male representation varying from 33.33–50%.\u003c/p\u003e \u003cp\u003eResident physicians showed improved analytical consistency, with 51.85% (n = 14) answering all the questions analytically and 14.81% (n = 4) remaining fully intuitive. Male residents comprised 57.14% (n = 8) of perfect analytical scorers. A question-level analysis revealed higher analytical response rates (CRT-Q1: 64.71%, n = 17; CRT-Q2: 77.78%, n = 21; CRT-Q3: 74.07%, n = 20), with males constituting 58.82–65.00% of the analytical responders. Intuitive responses decreased to 18.52–29.63%, with male representation at 40.00–50%.\u003c/p\u003e \u003cp\u003eAttending physicians or consultant-level physicians exhibited the strongest analytical performance, with 57.14% (n = 20) achieving all CRT-Reflective answers and only 8.57% (n = 3) providing entirely intuitive answers. Interestingly, male representation among perfect scorers declined to 50.00% (n = 10). While the analytical response rates remained high (62.86–74.29%), the male contribution to the analytical responses progressively decreased from CRT-Q1 (50.00%, n = 11) to CRT-Q3 (38.46%, n = 10). Conversely, males represented 58.33–80.00% of intuitive answers, especially in CRT-Q3 (80.00%, n = 4 of 5)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of CRT questionnaire results across different clinical experience groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrainees\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResident physicians\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttending physicians\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003econsultant-level physicians\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ²\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.09 ± 1.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.41 ± 2.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.57 ± 3.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.29 ± 8.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (64.71)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (59.26)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (57.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (42.86)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.969\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT-Reflective(n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (64.71)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (62.96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (66.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (57.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT-Intuitive (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (29.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (29.63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (28.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (42.86)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncorrect (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (7.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (4.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT-Reflective (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (70.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (77.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (80.94)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (64.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT-Intuitive (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (17.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (18.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.53)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (21.83)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncorrect (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.70)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.53)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (13.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT-Reflective (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (73.53)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (74.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (76.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (71.42)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT-Intuitive (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (17.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (18.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (14.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (14.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncorrect (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (7.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (14.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAll-Reflective (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (41.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (51.85)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (61.90)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (50.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAll-Intuitive (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (14.81)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (4.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (14.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCRT-Mixed (n,%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (47.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (33.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (33.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (35.71)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: CRT-Reflective represents analytical thinking answers; CRT-Intuitive represents intuitive thinking answers; Incorrect indicates wrong answers; All-Reflective means that all three CRT questions were answered analytically; All-Intuitive means that all three CRT questions were answered intuitively; CRT-Mixed indicates mixed answer patterns.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGender differences in CRT-reflective performance\u003c/h2\u003e \u003cp\u003eThere was no significant difference in CRT-Reflective (analytical thinking) scores among the different sex test groups. The results of the Mann‒Whitney U test and chi-square test for each CRT question (Q1‒Q3 and QAll) revealed that there was no statistically significant difference in analytical thinking between men and women (P \u0026gt; 0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the proportion of males with analytical thinking in the CRT-Q1 was 36/56 (64.3%), which was similar to the percentage of females with analytical thinking of 25/40 (62.5%) (P = 0.858). There was no significant difference between males (44/56 (78.6%) and females (27/40 (67.5%)) in the CRT-Q2 score (P = 0.223). CRT-Q3 and QAll also showed no sex differences (P = 0.455 and 0.492). Notably, although males constituted a slightly greater proportion of analytical thinking in the CRT-Q2 and CRT-Q3 (a difference of approximately 8–11 percentage points), the test results support gender homogeneity in analytical thinking performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR×C chi-square test results of CRT-reflective by gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCRT measure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge median (IQR)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTest populations\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003e(n = 56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003e(n = 40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrainees\u003c/p\u003e \u003cp\u003e(n = 34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResidents\u003c/p\u003e \u003cp\u003e(n = 27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAttendings\u003c/p\u003e \u003cp\u003e(n = 21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003econsultant-level\u003c/p\u003e \u003cp\u003e(n = 14)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCRT-Q\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT–Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0(23.0;32.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0(23;38.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann Whitney U/X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e999.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.032\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e0.360\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCRT-Q\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT–Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0(23.0;34.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0(23.0;31.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann Whitney U/X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e876.500\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.485\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e1.618\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCRT-Q\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT–Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0(23.0;34.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0(23.0;31.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann Whitney U/X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e840.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.558\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e0.104\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCRT-Q\u003csub\u003eAll\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT–Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0(23.0;34.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Reflective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0(23.0;32.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann Whitney U/X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1046.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.473\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e2.286\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote\u003c/b\u003e: a: Mann‒Whitney U test; b: R×C chi‒square test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInteraction effects between gender and clinical experience\u003c/h2\u003e \u003cp\u003eTaking different CRT test subjects, CRT-reflective as the dependent variable, and gender and different test populations as the explanatory variables, a generalized linear mixed model (GLMM) was used to analyse the associated factors(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There was a significant interaction effect between gender and the test population on CRT-Reflective performance (P = 0.005). Specifically, female attending physicians (or those of higher rank) exhibited the strongest tendency toward analytical thinking, with an odds ratio (OR) of 5.919 (95% CI [1.720, 20.373]), which was significantly greater than that of the male intern control group. Female residents also showed a dominant trend (OR = 2.711, P = 0.036). However, in the intern group, there was no statistically significant gender difference (female vs male OR = 0.580, P = 0.357). There was no significant gender difference between the different CRT questions (Q1-Q3) (P \u0026gt; 0.65), and there was no significant interaction effect between the test population and question type (P \u0026gt; 0.79). This may indicate that senior female physicians (attending and above) may be more inclined to adopt an analytical mindset in clinical decision-making, an advantage that has not been observed in junior physicians.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGLMM analysis of factors associated with analytical thinking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ²\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e−0.814\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4618\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.108\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.179–1.059\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTest populations (attending physicians or higher)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrainees\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.721\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6290\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.314\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.142, 1.668]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidents\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.432\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6588\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.179, 2.362]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGender ( Male)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e−0.545\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5921\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.182–1.850\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCRT test (CRT-Q3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRT-Q1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5951\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.940\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.604, 6.226]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRT-Q2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6226\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.267, 3.070]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eInteractions of test populations and gender (male trainees)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale attending and above\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.778\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6306\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.951\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.919\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.720, 20.373]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale residents\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6683\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.228\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.711\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.732, 10.047]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eInteractions of CRT test and gender (male answer CRT-Q3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale answer CRT-Q1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6462\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.215, 2.707]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale answer CRT-Q2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6753\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.334, 4.718]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eInteractions of test populations and CRT test\u003c/p\u003e \u003cp\u003e(attending or higher physicians answer CRT-Q3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrainees answer CRT-Q1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7607\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.204, 4.016]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrainees answer CRT-Q2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7901\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.247, 5.467]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidents answer CRT-Q1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7958\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.206, 4.653]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidents answer CRT-Q2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.216\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8467\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.153, 4.234]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe value of EBM-CBL-PBL pedagogy in developing analytical thinking\u003c/h2\u003e \u003cp\u003eThere were no significant differences in age, gender and baseline CRT question answers (CRT-Q1, CRT-Q2, CRT-Q3, CRT-Qall) between the general teaching group and the EBM-CPL-PBL integrated teaching model group (P \u0026gt; 0.05). However, the satisfaction score of the EBM-CPL-PBL group was significantly greater than that of the general teaching group, and the difference was statistically significant (P \u0026lt; 0.001). Moreover, in the general teaching group, only 44.44% of the students considerred the three questions analytically, whereas in the EBM-CPL-PBL group, the percentage increased to 61.11%. The proportion of intuitive thinking was 22.22% in the general teaching group, but decreased to 16.67% in the EBM-CPL-PBL group, indicating that the latter can effectively reduce students' dependence on intuitive thinking. The analysis of the CRT test questions with different difficulty coefficients revealed that the percentages of analytical thinking in Q1, Q2 and Q3 in the EBM-CPL-PBL group were 66.67%, 77.78% and 83.33%, respectively, which were greater than those in the general teaching group (61.11%, 55.56% and 61.11%, respectively). However, the difference was not statistically significant (P = 0.248, 0.080 and 0.068) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSingle Factor Analysis of EBM-CBL-PBL Pedagogy Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraditional (n = 18)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEBM-CBL-PBL (n = 18)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMann‒Whitney U/X\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0 (21.0–23.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0 (21.0-22.25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (61.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (55.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalytical Responses\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRT-Q1 (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (66.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (83.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRT-Q2 (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (50.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (77.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRT-Q3 (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (55.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (83.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRT-QAll (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (44.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (61.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.0 (62.0–72.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.0 (71.5–78.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.500\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: a: Mann‒Whitney U test; b: R×C chi‒square test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eA generalized linear model was employed to further analyse interactions between gender and different teaching modalities concerning their impact on reflective cognitive skills as measured by CRT tests. Notably, students who received instruction through EBM-CPL-PBL were more likely to have enhanced analytical thinking abilities (OR = 3.39; 95% CI: [1.52–7.56]; P = 0.003). Nevertheless, there were no statistically significant differences regarding gender or interaction terms involving groups × gender (P \u0026gt; 0 .05)(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This may suggest that female classmates' advantage in showing CRT-reflective ability is not improved by the EBM-CPL-PBL integrated teaching model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteractive effects of gender and teaching mode on CRT-reflective performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ²\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.25, 4.38]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eteaching group[EBM-CBL-PBL]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.52, 7.56]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Female)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e−0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.35, 1.54]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteractions of teaching group\u003c/p\u003e \u003cp\u003eand gender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.56, 4.41]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: Generalized linear mixed models (GLMMs)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion and conclusion","content":"\u003cp\u003eIn the process of training preclinical medical students, the development of clinical thinking is crucial, particularly in fostering logical reasoning and analytical skills. Research indicates that males tend to excel in logical reasoning and analytical abilities, demonstrating a greater propensity for rapid logical integration [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Conversely, females exhibit greater potential for progressive learning[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which may be attributed to physiological and cognitive development as well as sociocultural factors. In the field of medical education, especially in the process of clinical diagnosis and decision-making, the strength of an individual's logical reasoning ability directly affects the accuracy of clinical judgment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, there remains a paucity of research materials available for reference on the characteristics of gender differences in the cultivation of logical analytical thinking.\u003c/p\u003e\u003cp\u003eLong-term clinical experience plays a pivotal role in enhancing the analytical thinking abilities of medical students. Unlike novices, who predominantly rely on intuitive thinking, the accumulation of clinical experience enables medical students to develop a more systematic and structured thought process, thereby improving the precision of clinical decision-making. Studies reveal that novices in medical diagnosis often depend on intuitive methods, drawing conclusions primarily through pattern recognition or rapid judgment[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, this approach is vulnerable to cognitive biases, increasing the likelihood of misdiagnosis or missed diagnoses[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, prolonged clinical training facilitates the gradual development of more sophisticated analytical modes among medical students. This encompasses decision-making on the basis of systematic information gathering, hypothesis testing, logical reasoning, and critical analysis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consequently, experienced physicians often exhibit enhanced analytical thinking skills closely correlated with extensive clinical experience. Throughout the diagnostic process, they demonstrate an exceptional capacity to integrate empirical knowledge, reasoning, and clinical evidence, ultimately reducing diagnostic errors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Males may possess an innate predisposition for logical thinking, a finding corroborated by our research. Additionally, our study reveals that senior female physicians exhibit high levels of analytical thinking during the decision-making process, surpassing their male counterparts in certain instances. This enhancement is attributable primarily to long-term clinical practice rather than solely to educational training at the medical school level. This suggests that practical training can mitigate innate gender differences in women, promoting their analytical capabilities. In other words, long-term clinical experience significantly enhances the analytical ability of female physicians in clinical diagnosis.\u003c/p\u003e\u003cp\u003eInterestingly, short-term training via the EBM-CPL-PBL integrated teaching model did not sustain such positive effects. Therefore, in the design of preclinical medical education, in addition to classroom teaching, training on the basis of actual clinical experience should be emphasized. Examples include clerkship, case discussion, and simulation-based training. Educators should pay more attention to developing students' analytical thinking so that they can make clinical reasoning and decision making more efficient in their future careers. In this study, the EBM-CPL-PBL integrated teaching model effectively improved the analytical thinking ability of preclinical interns, but in terms of gender, there was no significant difference in the rate of improvement in thinking ability between males and females. This finding indicates that although the preclinical pedagogical training mode plays a positive role in improving the analytical thinking of medical students, its advantage for female students is not obvious, and long-term clinical experience training is more beneficial to the training of female doctors' analytical thinking. Therefore, to maximize the effect of medical education, we recommend strengthening analytical thinking training at the preclinical stage while providing more opportunities for experience accumulation at the clinical practice stage, especially for female physicians, to further narrow the gender gap and improve the quality of overall medical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRT \u0026nbsp; \u0026nbsp; \u0026nbsp;Cognitive Reflection Test\u003c/p\u003e\n\u003cp\u003eGLMM \u0026nbsp; Generalized Linear Mixed Model\u003c/p\u003e\n\u003cp\u003eEBM \u0026nbsp; \u0026nbsp; Evidence-Based Medicine\u003c/p\u003e\n\u003cp\u003eCBL \u0026nbsp; \u0026nbsp; \u0026nbsp;Case Based Learning\u003c/p\u003e\n\u003cp\u003ePBL \u0026nbsp; \u0026nbsp; \u0026nbsp;Problem Based Learning\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSMS \u0026nbsp; \u0026nbsp; Short Message Service \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp;Interquartile Range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; Odds Ratio\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe would like to extend our sincere gratitude to the Medical Administration Department and Education Office for providing researcher information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianzhi Chen: Conceptualization, methodology, investigation, writing original draft, writing review \u0026amp; editing, supervision, project administration. Manman Xu: Conceptualization, Methodology, Investigation, Writing - Original Draft, Writing - Review \u0026amp; Editing. Deshun Liu: Project administration, Funding acquisition. Fang Yang: Methodology, Validation, Resources. Guobao Xu: Software, formal analysis, data curation. Huaichen Yang: Investigation, Visualization. Qizhu Feng: Resources, Supervision. Lei Xu: Conceptualization, methodology, writing - original draft, writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been funded by the Quality Engineering Program for Higher Education Institutions in Anhui Province (2023) (Funding No. 2023xm1073); The Quality Engineering Program for Higher Education Institutions in Anhui Province (2024); 2023 Medical Special Cultivation Project of Anhui University of Science and Technology (Funding No. YZ2023H2C018 ).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: This study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and received formal approval from the Institutional Review Board (IRB) of the Ethics Committee of the First Affiliated Hospital of Anhui University of Science and Technology, China(No.2023-KY-H2C018-001). All participants were fully informed and consented to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePelaccia T, Tardif J, Triby E, Charlin B. An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory. Med Educ Online. 2011 ;16. \u003c/li\u003e\n\u003cli\u003eEpstein RM, Hammond KR. Dual-process theories in clinical decision-making. Journal of Clinical Psychology. 2011;67(4):456-467. \u003c/li\u003e\n\u003cli\u003eKahneman D. Thinking, Fast and Slow. Farrar, Straus and Giroux; 2011. \u003c/li\u003e\n\u003cli\u003eTay SW, Ryan P, Ryan CA. Systems 1 and 2 thinking processes and cognitive reflection testing in medical students. Can Med Educ J. 2016;7(2):e97-e103. \u003c/li\u003e\n\u003cli\u003eThammasitboon S, Cutrer WB. Diagnostic decision-making and strategies to improve diagnosis. Curr Probl Pediatr Adolesc Health Care. 2013;43(9):232-241.\u003c/li\u003e\n\u003cli\u003eVan den Brink N, Holbrechts B, Brand PLP, Stolper ECF, Van Royen P. Role of intuitive knowledge in the diagnostic reasoning of hospital specialists: a focus group study. BMJ Open. 2019 ;9(1):e022724. \u003c/li\u003e\n\u003cli\u003eBowen JL. Educational strategies to promote clinical diagnostic reasoning. N Engl J Med. 2006 ;355(21):2217-2225.\u003c/li\u003e\n\u003cli\u003eCroskerry P. A universal model of diagnostic reasoning. Acad Med. 2009 ;84(8):1022-1028. \u003c/li\u003e\n\u003cli\u003eWartman SA. The Empirical Challenge of 21st-Century Medical Education. Acad Med. 2019 ;94(10):1412-1415.\u003c/li\u003e\n\u003cli\u003eSchmidt HG, Mamede S. How to improve the teaching of clinical reasoning: a narrative review and a proposal. Med Educ. 2015;49(10):961-973. \u003c/li\u003e\n\u003cli\u003eCutrer WB, Sullivan WM, Fleming AE. Educational strategies for improving clinical reasoning. Curr Probl Pediatr Adolesc Health Care. 2013;43(9):248-257. \u003c/li\u003e\n\u003cli\u003eFrederick, S.Cognitive reflection and decision making. Journal of Economic Perspectives. 2005; 19:25\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003ePennycook G, Cheyne JA, Koehler DJ, Fugelsang JA. Is the cognitive reflection test a measure of both reflection and intuition? Behav Res Methods. 2016;48(1):341-348.\u003c/li\u003e\n\u003cli\u003eVinaykumar N, Gugapriya TS, Kalaiselvi S. Exploring Knowledge of Cognitive Disposition to Respond in Clinical Decision-Making among Early Clinical Learners. Maedica (Bucur). 2023 ;18(2):317-322. \u003c/li\u003e\n\u003cli\u003eLiu X. The effect of EBM-PPL-CBL integrated teaching method in the teaching of external urinary clinical practice . Chinese Journal of Science and Technology Database Medicine, 2023, (8): 0029-0032.\u003c/li\u003e\n\u003cli\u003eLiu XX. Application of CPL-PPL-EBM integrated teaching method in standardized training of tumor radiation therapy residents. Sichuan Journal of Physiological Sciences,2023,45(12): 2455-2458.\u003c/li\u003e\n\u003cli\u003eHaynes RB. Of studies, syntheses, synopses, summaries, and systems: the \u0026quot;5S\u0026quot; evolution of information services for evidence-based healthcare decisions. Evid Based Med. 2006, 11(6): 162-164. \u003c/li\u003e\n\u003cli\u003eLikert, R. A technique for the measurement of attitudes. Archives of Psychology, 1932,140, 1-55.\u003c/li\u003e\n\u003cli\u003eQi Y, Li C, Sun P, Zhang X, Kong Q, Wu D, et al. Application of PBL combined with 3D anatomy software in clinical internship teaching of Traditional Chinese Medicine traumatology based on questionnaire survey. Chinese Continuing Medical Education,2024, 16(12): 152-156.\u003c/li\u003e\n\u003cli\u003eChen CS, Knep E, Han A, Ebitz RB, Grissom NM. Sex differences in learning from exploration. Elife. 2021, 19; 10:e69748.\u003c/li\u003e\n\u003cli\u003eHalpern DF, Sex differences in cognitive abilities (4th ed.). Psychology Press. 2012. \u003c/li\u003e\n\u003cli\u003eNorman GR, Monteiro SD, Sherbino J, Ilgen JS, Schmidt HG, Mamede S. The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking. Acad Med. 2017, 92(1):23-30.\u003c/li\u003e\n\u003cli\u003eEva KW, What every teacher needs to know about clinical reasoning. Medical Education. 2005, 39(1), 98-106.\u003c/li\u003e\n\u003cli\u003eCroskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013,22(Suppl 2) : ii58-ii64.\u003c/li\u003e\n\u003cli\u003eNorman GR, Grierson LEM, Sherbino J, Hamstra SJ, Schmidt HG, Mamede S. Expertise in Medicine and Surgery. In: Ericsson KA, Hoffman RR, Kozbelt A, Williams AM, eds. The Cambridge Handbook of Expertise and Expert Performance. Cambridge Handbooks in Psychology. Cambridge University Press. 2018:331-355.\u003c/li\u003e\n\u003cli\u003eSchmidt HG, Boshuizen, HPA., On acquiring expertise in medicine. Educ Psychol Rev. 1993, 5, 205\u0026ndash;221. \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":"Dual-process theory, CRT, EBM-CBL-PBL integrated teaching model, GLMM, Reflective response","lastPublishedDoi":"10.21203/rs.3.rs-6439748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6439748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Clinical reasoning is a critical skill in medical education. Dual-process theory highlights the interaction between intuitive and analytical thinking, where the former may lead to diagnostic errors. This study employed the cognitive reflection test (CRT) to assess cognitive reflection patterns across different experience levels and genders, while evaluating the impact of an integrated EBM-CBL-PBL teaching model on the development of analytical thinking in medical trainees.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e A cross-sectional study was conducted among trainees, Resident Physicians, Attending Physicians and Consultant-level Physicians registered at the First Affiliated Hospital of Anhui University of Science and Technology. A generalized linear mixed model (GLMM) was used to analyse the relationships among sex, clinical experience, and analytical thinking. Participants comprising clinical medicine trainees were randomly allocated to either the traditional pedagogy group (n\u0026thinsp;=\u0026thinsp;18) or the EBM-CBL-PBL intervention group (n\u0026thinsp;=\u0026thinsp;18), with comparisons made on the basis of CRT responses (intuitive vs. reflective) and teaching satisfaction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Analytical thinking (CRT\u0026ndash;Reflective) increased with increasing clinical experience (trainees: 41.18%, residents: 51.85%, attending physicians: 57.14%). Senior female physicians presented the strongest analytical tendency (OR\u0026thinsp;=\u0026thinsp;5.919, p\u0026thinsp;=\u0026thinsp;0.005). The EBM-CBL-PBL approach enhanced reflective cognition (61.11% vs. 44.44%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and satisfaction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no significant gender interaction was observed (P\u0026thinsp;=\u0026thinsp;0.396).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e Clinical experience contributes to the development of analytical thinking, particularly among female physicians. The EBM-CBL-PBL teaching model improves analytical understanding in early-career physicians but does not eliminate gender differences. Sustained effects require long-term practice.\u003c/p\u003e","manuscriptTitle":"Enhancing Analytical Thinking in Early-Career Physicians: Evaluating the EBM-CBL-PBL Integrated Teaching Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 09:45:46","doi":"10.21203/rs.3.rs-6439748/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":"e197c5bd-8e1e-4734-8cfe-5c81e2f8e3f5","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T06:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 09:45:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6439748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6439748","identity":"rs-6439748","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}