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A multi-center cross-sectional study on identification of influencing factors of medical students’ emotional engagement in China

BMC Medical Education volume 23, Article number: 838 (2023) Cite this article

Abstract

Background

Studies exploring influencing factors of emotional engagement among medical students are scarce. Thus, we aimed to identify influencing factors of medical students’ emotional engagement.

Methods

We carried out a multi-center cross-sectional study among 10,901 medical students from 11 universities in China. The Chinese version of Utrecht Work Engagement Scale-Student version (UWES-S) was used to evaluate emotional engagement level of medical students. The predictors related to engagement level were determined by the logistic regression analysis. Furthermore, we constructed a nomogram to predict emotional engagement level of medical students.

Results

A total of 10,576 sample were included in this study. The mean emotional engagement score was 74.61(± 16.21). In the multivariate logistic regression model, we found that males showed higher engagement level compared with females [odds ratio (OR) (95% confidence interval (CI)): 1.263 (1.147, 1.392), P < 0.001]. Medical students from the second batches of medical universities had higher engagement level and from “Project 985” universities had lower engagement level compared with 211 project universities [OR (95%CI): 1.376 (1.093, 1.733), P = 0.007; OR (95%CI): 0.682 (0.535, 0.868), P = 0.002]. Medical students in grade 4 and grade 2 presented lower engagement level compared with in grade 1 [OR (95%CI): 0.860 (0.752, 0.983), P = 0.027; OR (95%CI): 0.861 (0.757, 0.980), P = 0.023]. Medical students lived in provincial capital cities had higher engagement level compared with in country [OR (95%CI): 1.176 (1.022, 1.354), P = 0.024]. Compared with eight-year emotional duration, medical students in other emotional duration (three-year and four-year) had lower engagement level [OR (95%CI): 0.762 (0.628, 0.924), P = 0.006]. Medical students’ engagement level increased with increases of grade point average and interest in studying medicine. Medical students learned by converging style showed lower engagement level [OR (95%CI): 0.827 (0.722, 0.946), P = 0.006] compared with accommodating style. The model showed good discriminative ability (area under curve = 0.778), calibrating ability and clinical utility.

Conclusions

We identified influencing factors of medical students’ emotional engagement and developed a nomogram to predict medical students’ emotional engagement level, providing reference and convenience for educators to assess and improve emotional engagement level of medical students. It is crucial for educators to pay more attention to emotional engagement of medical students and adopt effective strategies to improve their engagement level.

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Introduction

Currently, positive psychology research has become an emerging trend. It focuses on human strengths and optimal functioning rather than on weaknesses and malfunctioning [1]. As a positive mental state, engagement has become hotspots in researches. Emotional engagement refers to a persistent, positive and pervasive emotional and cognitive state related to emotional, scientific research and employment, which is not focused on any particular object, event, individual, or behavior [1, 2]. It is characterized by vigor, dedication and absorption [1]. Emotional engagement is an important indicator of students’ positive psychology in emotional. It can reflect the positive and healthy mental state of students, and is conducive to stimulating students’ positive qualities such as optimism, resilience, sense of meaning and creativity, which effectively promote the maturity and development of students, and lay a solid foundation for them to enter the society [2]. Medical and health service is related to people’s life and health, medical students are the reserve force of the future medical and health service, so medical students shoulder the important mission of improving people’s health. Prior studies have reported that emotional engagement was negatively related to burnout [1, 3], and positively related to well-being [4,5,6]. Numerous studies have revealed that emotional engagement was positively related to academic achievement, and emotional engagement was a key factor in mediating medical students’ motivation and academic performance [6,7,8]. Therefore, it is crucial to understand the emotional engagement of medical students and its influencing factors, which will help them achieve better self-worth in their professional fields.

Studies showed that health workers with lower psychological distress, higher job stress, higher levels of perceived social support, psychological flexibility and subjective well-being had higher levels of work engagement [9, 10]. Previous study suggested that emotional engagement of medical student was associated with exercise, sleep, drugs and alcohol use, maintaining relationships, financial stress, thoughts of dropping out and questioning the decision to enter medical school [11]. Besides, learning adaptability and time management disposition were also influencing factors of emotional engagement of medical students [12]. Numerous studies have studied work engagement in health workers, while, rare studies investigated factors affecting emotional engagement based on large samples in medical students. Moreover, current studies only explored some factors influencing emotional engagement among medical students. There were still some influencing factors that have not been further studied, such as educational system, university category and learning style.

Hence, we aimed to perform a cross-sectional study among 10,901 medical students from 11 universities in China to identify influencing factors of medical students’ emotional engagement and construct a nomogram to predict emotional engagement level of medical students.

Materials and methods

Study design

The details of study design have been previously published [13]. Briefly, we recruited medical students among 11 universities in China from 20th, February 2020 to 31rd, March 2020. We randomly selected one or two classes in each grade, and all students in each class were selected to complete the electronic questionnaire.

Data collection

For each student, the following information was collected, including age, gender, university category, major, ethnicity, whether he/she was an only child, grade, native place, educational system, grade point average (GPA), parental educational level, parental occupation, learning environment of schools, doctor − patient relationship in their hospitals, interests of medicine, Kolb Learning Style and learning engagement level. Among university category, “Project 985” universities refer to the colleges selected into “Project 985” that aims to build universities with world’s advanced level and is research-oriented, and “Project 211” universities refer to the colleges selected into the “Project 211” that aims to construct key universities facing the 21st century. Major included clinical medicine, nursing, phylaxiology, preclinical medicine and stomatology, phylaxiology referred to preventive medicine. Grade referred to the number of study years after entering the university. Native place referred to the place of living. Education system referred to studies duration. Parental occupation including civil servant, company employee, freelance work, individual household, professional/technical and worker/peasant, individual household was the laborer who ran his own business and earned their own living, worker/peasant meant laborer/farmer.

Assessment tool

The Utrecht Work Engagement Scale (UWES) was widely used internationally and has been translated into multiple languages version [14]. Based on the UWES, Schaufeli et al. developed the UWES-Student (UWES-S) with college students as samples [1]. The Chinese version of UWES-S was used to measure medical students’ emotional engagement level in this study, and it has been confirmed to have good reliability (Cronbach’s alpha = 0.91) and validity [2, 15]. The contents of the scale were shown in Table S1 (English version) and Table S2 (Chinese version). The scale consisted of 3 dimensions (Vigor, Dedication and Absorption) and 17 items. All items were scored on a 7-point Likert scale ranging from 0 (never) to 6 (always). The higher score represented a higher emotional engagement level.

Statistical analysis

Sample calculation by PASS software identified that a sample size of 1537 participants was needed to achieve a 0.05 significance level. Continuous variables were shown as mean ± standard deviation (SD) and categorical variables as number (percentage). Two independent sample t-test and analysis of variance (ANOVA) were analyzed to assess the differences on UWES score related to variables. We divided the samples into low-level and high-level groups according to the median value of UWES score. Firstly, the variables associated with emotional engagement level were identified by the univariate logistic regression analysis. Then, significant variables were integrated into the multivariate logistic regression model, as to identify factors influencing emotional engagement level among medical students. Finally, the nomogram was constructed to predict the probability of high emotional engagement level. Receiver operating characteristic (ROC) curve was performed to assess discriminative ability of the model. Calibration plot was performed to assess calibrating ability. Decision curve analysis (DCA) was performed to assess clinical utility.

In this study, two-sided P value < 0.05 was considered as significantly statistical. All statistics analysis processes were performed with R version 3.6.1 (Institute for Statistics and Mathematics, Vienna, Austria).

Results

Emotional engagement level of medical students

A total of 10,901 questionnaires were received. After eliminating the questionnaires with outliers and missing values, 10,576 questionnaires were used for the final analysis. The age range of medical students was mainly between 16 and 25 years old (98.79%). A total of 4205 participants were males (39.76%), most medical students were ethnic Han (93.54%) and majoring in clinical medicine (79.15%). A total of 61.20% of medical students were from the First Batches of Medical Universities. The mean UWES score of medical students was 74.61(± 16.21). The analysis results were shown in Table 1 and Fig. 1.

Table 1 Emotional engagement level of medical students
Full size table
Fig. 1
figure 1

Heatmap of the UWES score. GPA, grade point average; UWES, Utrecht Work Engagement Scale

Full size image

The identification of influencing factors of medical students’ emotional engagement

Firstly, the univariate logistic regression analysis was performed to identify the variables associated with emotional engagement level. We found that 11 variables including gender, university category, major, whether he/she was an only child, grade, native place, educational system, GPA, father occupation, interests of medicine and Kolb Learning Style were associated with emotional engagement of medical students (P < 0.05) (Table S3). Then, 11 variables were incorporated into the multivariate logistic regression model. The model suggested that males had higher emotional engagement level [OR (95%CI): 1.263 (1.147, 1.392), P < 0.001], compared with females; medical students from “Project 985” universities had lower emotional engagement level and the second batches of medical universities had higher emotional engagement level compared with “Project 211” universities [OR (95%CI): 0.682 (0.535, 0.868), P = 0.002; OR (95%CI): 1.376 (1.093, 1.733), P = 0.007]; medical students in grade 4 and grade 2 showed lower emotional engagement level compared with in grade1, respectively [OR (95%CI): 0.860 (0.752, 0.983), P = 0.027; OR (95%CI): 0.861 (0.757, 0.980), P = 0.023]; medical students lived in provincial capital and prefecture city had higher emotional engagement level, respectively [OR (95%CI): 1.176 (1.022, 1.354), P = 0.024; OR (95%CI): 1.265 (1.069, 1.498), P = 0.006]; the higher the GPA was, the higher the emotional engagement level of medical students were [OR (95%CI):1.267 (1.051, 1.531), P = 0.014, for top 5% GPA; OR (95%CI): 1.158 (1.028, 1.304), P = 0.016, for 5–20% GPA; OR (95%CI): 0.732 (0.651, 0.822), P < 0.001, for 50–80% GPA; OR (95%CI): 0.703 (0.591, 0.836), P < 0.001, for 80–100% GPA]; medical students interesting in medicine presented higher emotional engagement level [OR (95%CI): 0.489 (0.261, 0.840), P = 0.016, for uninteresting of medicine; OR (95%CI): 6.121 (5.440, 6.899), P < 0.001, for interesting of medicine; OR (95%CI): 44.421 (36.653, 54.139), P < 0.001, for extremely interesting of medicine]; medical students who learned by accommodating style showed higher engagement level [OR (95%CI): 0.860 (0.768, 0.946), P = 0.009, for assimilating; OR (95%CI): 0.827 (0.722, 0.946), P = 0.006, for converging; OR (95%CI): 0.857 (0.755, 0.972), P = 0.017, for diverging] (Table 2).

Table 2 Multivariate logistic regression analysis of emotional engagement level
Full size table

The nomogram and validation

We established the nomogram based on the variables determined by the univariate logistic regression analysis (Fig. 2). DCA indicated that the model had good clinical utility (Fig. 3A). The ROC curve showed that the model had sufficient discriminative ability (area under curve (AUC) of train set = 0.800, AUC of test set = 0.778) (Fig. 3B). The calibration curve showed good consistency between the predicted emotional engagement level of the model and the actual emotional engagement level (Fig. 3C).

Fig. 2
figure 2

Nomogram of predicting emotional engagement level of medical students. GPA, grade point average; UWES, Utrecht Work Engagement Scale

Full size image
Fig. 3
figure 3

The nomogram validation. A DCA of the nomogram showed the model had good clinical utility. B AUC of ROC indicated that the model had good discriminative ability (Total set AUC = 0.793, Train set AUC = 0.800, Test set AUC = 0.778). C The calibration curve of the nomogram presented the model had good predictive consistency. DCA, decision curve analysis; UWES, Utrecht Work Engagement Scale; ROC, receiver operating characteristic; AUC, area under the curve

Full size image

Discussion

Academic burden of medical students is relatively large. Engagement in learning emotional may be more conducive to achieve better academic performance for them and avoid burnout and anxiety on study and life [11, 16]. Therefore, determining influencing factors of leaning emotional engagement can help medical schools take measures to enhance emotional engagement level of medical students and cultivate more outstanding medical talents.

We carried out a multi-center cross-sectional study to investigate influencing factors on emotional engagement of medical students. The results indicated that gender, university category, grade, native place, educational system, GPA, interests of medicine and Kolb Learning Style were predictors of emotional engagement. Furthermore, we constructed the nomogram to predict the emotional engagement level of medical students, and the nomogram model showed good discriminative and calibrating ability and clinical utility.

Gender and emotional engagement of medical students

There was gender difference in emotional engagement level, and males had higher emotional engagement level compared with females. Medical students’ intrinsic motivation in medicine positively correlated with emotional engagement level, and the male showed higher intrinsic motivation than females, such as having a strong interest in medicine and being confident I can succeed in the medicine filed [8, 17]. In addition, males perceived higher emotional and emotional process support from teachers than females, and the support from teachers had a beneficial impact on emotional engagement of students [18, 19]. However, some studies suggested that females showed higher engagement level in emotional than males [20, 21]. But, studies on gender difference of emotional engagement level in medical students were limited. Thus, more large-sample studies should be conducted to investigate the influence of gender on emotional engagement level of medical students.

Native place and emotional engagement of medical students

Compared with medical students from country, students from provincial capital and prefecture cities presented higher emotional engagement level. That might be related to some social and psychological factors, such as the local education level, parents’ education level, and family environment. Medical students from provincial capital or prefecture cities might receive more social support, which was conductive to improve emotional engagement level [22]. Moreover, prior study also reported medical students from cities showed significantly higher learning motivation that was positively associated with emotional engagement [23, 24].

University-related and student-related factors (university category, grade, educational system, GPA, interests of medicine and learning style) and emotional engagement of medical students

In this study, compared with medical students from “Project 211” university, medical students from “Project 985” universities had lower emotional engagement level, followed by the first batches of medical universities, while medical students from the second batches of medical university had higher emotional engagement level. The impact of university category on emotional engagement might be related to the universities’ training goals for students, learning environment of universities and the degree of caring about the physical and mental health of students [25, 26]. Medical students from “Project 985” university might suffer from more academic stress, and stress caught directly affect engagement in learning emotional [27]. At present, studies investigating the impact of university category on the emotional engagement were limited, therefore, it might be necessary to conduct more research on the impact of university category on emotional engagement and related reasons.

Before 2015, China’s medical education system were divided by learning duration, including five-year medicine program leading to a Bachelor’s of medicine degree, seven-year medicine program leading to a Master’s of medicine degree and eight-year medicine program leading to a Doctor of medicine degree. Moreover, to improve medical education quality, all seven-year degree programs were changed into ‘5 + 3’ master degree programs in 2015 [28]. In this study, students in grade 2 and grade 4 had lower emotional engagement level compared with in grade 1, which was consistent with previous study [29]. Most medical students were education periods of five year in this study, they caught face internships, future academic thinking, and accumulated academic and mental pressures in the fourth year [30], which might make them more prone to burnout. Besides, burnout was an important predictor of emotional engagement [31, 32]. Prior study also revealed that medical students at higher-grade experienced increased burnout risk and decreased emotional engagement level [3]. Study also reported that medical students in higher grade experienced higher depression and stress levels compared with in grade 1, which would affect students’ emotional engagement [33]. Depression and stress had negative effect on emotional engagement [34]. Furthermore, this phenomenon might also indicate that medical students have started to experience a decline in emotional engagement level from the early stage. Thus, the medical schools should take measures from the early stage to prevent the decline of medical students’ emotional engagement level. Additionally, this study revealed that eight-year medical students reported higher emotional engagement level in this study. This indicated that the training mode of eight-year clinical medical students might have a positive influence on emotional engagement level of medical students. The educational goals of the eight-year medical program are to cultivate medical talents with a solid foundation in medical theory, strong self-learning ability, practical ability, clinical ability, scientific research ability, communication ability as well as good innovation spirit and comprehensive quality. Due to the longer learning time of the eight-year program, medical students caught achieve better coherence in learning contents and more comprehensive and in-depth understanding on research project. That might be beneficial for cultivating medical students’ emotional engagement. There was a lack of evidence about the relationship between educational system and emotional engagement, so further research was needed to explore their relationship.

The results reported that students with higher GPA and great interest in medicine had higher emotional engagement level, which were consistent with previous studies [35, 36].These students tended to have greater learning motivation and more effective learning strategies [23]. That caught drive them to have a better engagement in emotional.

Medical students with accommodating learning style had higher emotional engagement level compared with other learning styles. kolb conducted an analysis of the learning cycle, and believed the learning cycle was composed of four interconnected links, namely concrete experience (CE), reflective observation (RO), abstract conceptualization (AC) and active experimentation (AE). CE where the student learned through involvement in experience; RO where the student learned through watching and making sense of experience; AC where the student learned by connecting past experience and ideas, and forming an opinion on what that means to them, and considering what could have been done to enhance the outcome; AE where the student directed future practice by using what they have learned. These four links would have different combinations of preferences in each body, thus showing different learning styles: accommodating (CE and AE), assimilating (AC and RO), converging (AC and AE) and diverging (CE and RO) [37, 38]. Individuals with accommodating learning style learned through hands-on experiences and previous attained experience and were willing to devote themselves to new or challenging jobs [39]. Hands-on was helpful to strengthen the connection between teaching and clinic and an important means to cultivate medical students’ clinical ability and skills. Hands-on skills were an important basis for medical students in hospital. Hands-on caught strengthen sensory/perceptual experiences and help to recall more information [40]. Therefore, universities should attach importance to and enhance the experience of clinical practice of medical students in order to increase students’ engagement in emotional.

This was a multiple-center large sample to identify influencing factors of medical students’ emotional engagement in China. Thus, the results were more representative. Furthermore, we constructed the nomogram and it provided a convenient way to evaluate the emotional engagement level of medical students. However, there were still some limitations in this study that should be taken into account in interpreting the results. Firstly, this study used a cross-sectional study design, so it was difficult to explain the causality between influencing factors and emotional engagement. Secondly, influence of cohort effects on results could not be completely dismissed. Thus, prospective studies were recommended to solve the limitation. Thirdly, we used self-reporting questionnaires to gather information, students might exaggerate or reduce self-report contents. Fourthly, we did not comprehensively collect the characteristic information of the participants, which might lead to the existence of residual confounding. Fifthly, the results were able to be generalized to students in other healthcare disciplines. Finally, factors identified by the univariate logistic regression analysis were used to construct the nomogram in this study, which might cause overfitting.

Conclusion

In this study, we identified influencing factors of medical students’ emotional engagement, including gender, university category, grade, native place, educational system, GPA, interests of medicine and Kolb Learning Style. Besides, we constructed the nomogram to predict the emotional engagement level of medical students. This study provides help to intervene and improve the emotional engagement of medical students. More studies exploring other influencing factors of emotional engagement of medical students are warranted.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the supplementary material.

References

  1. Schaufeli WB, Martinez IM, Pinto AM, Salanova M, Bakker AB. Burnout and engagement in university students - A cross-national study. J Cross Cult Psychol. 2002;33(5):464–81.

    Article  Google Scholar 

  2. Laitan F, Kan SHI, Fenghua Z. Research on reliability and validity of Utrecht work engagement scale-student. Chin J Clin Psychol. 2008;16(6):618–20.

    Google Scholar 

  3. Liu H, Yansane AI, Zhang Y, Fu H, Hong N, Kalenderian E. Burnout and study engagement among medical students at Sun Yat-sen University, China: a cross-sectional study. Medicine. 2018;97(15):e0326.

    Article  Google Scholar 

  4. Pietarinen J, Soini T, Pyhalto K. Students’ emotional and cognitive engagement as the determinants of well-being and achievement in school. Int J Educ Res. 2014;67:40–51.

    Article  Google Scholar 

  5. Bayoumy HMM, Alsayed S. Investigating relationship of perceived learning engagement, motivation, and academic performance among nursing students: a multisite study. Adv Med Educ Pract. 2021;12:351–69.

    Article  Google Scholar 

  6. Casuso-Holgado MJ, Cuesta-Vargas AI, Moreno-Morales N, Labajos-Manzanares MT, Barón-López FJ, Vega-Cuesta M. The association between academic engagement and achievement in health sciences students. BMC Med Educ. 2013;13:33.

    Article  Google Scholar 

  7. Ito T, Umemoto T. Examining the causal relationships between interpersonal motivation, engagement, and academic performance among university students. PLoS One. 2022;17(9):e0274229.

    Article  Google Scholar 

  8. Wu H, Li S, Zheng J, Guo J. Medical students’ motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Med Educ Online. 2020;25(1):1742964.

    Article  Google Scholar 

  9. Wang Y, Gao Y, Xun Y. Work engagement and associated factors among dental nurses in China. BMC Oral Health. 2021;21(1):402.

    Article  Google Scholar 

  10. Gómez-Salgado J, Domínguez-Salas S, Romero-Martín M, Romero A, Coronado-Vázquez V, Ruiz-Frutos C. Work engagement and psychological distress of health professionals during the COVID-19 pandemic. J Nurs Manag. 2021;29(5):1016–25.

    Article  Google Scholar 

  11. Agarwal G, Mosquera M, Ring M, Victorson D. Work engagement in medical students: an exploratory analysis of the relationship between engagement, burnout, perceived stress, lifestyle factors, and medical student attitudes. Med Teach. 2020;42(3):299–305.

    Article  Google Scholar 

  12. Liu JY, Liu YH, Yang JP. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students. J Prof Nurs. 2014;30(6):502–10.

    Article  Google Scholar 

  13. Zhou Z, Huang R, Zhang G, Gong M, Xian S, Yin H, et al. Nomograms for predicting medical students’ perceptions of the learning environment: multicenter evidence from medical schools in China. Front Public Health. 2022;10:825279.

    Article  Google Scholar 

  14. Schaufeli WB, Bakker AB. UWES Utrecht Work Engagement Scale Preliminarty Manual. J Occup Health Psychol. 2003;1:58.

  15. Meng L, Jin Y. A confirmatory factor analysis of the Utrecht Work Engagement Scale for students in a Chinese sample. Nurse Educ Today. 2017;49:129–34.

    Article  Google Scholar 

  16. Gómez HP, Pérez VC, Parra PP, Ortiz ML, Matus BO, McColl CP, et al. Academic achievement, engagement and burnout among first year medical students. Rev Med Chil. 2015;143(7):930–7.

    Google Scholar 

  17. Ghasemi MR, Moonaghi HK, Heydari A. Student-related factors affecting academic engagement: a qualitative study exploring the experiences of Iranian undergraduate nursing students. Electron Physician. 2018;10(7):7078–85.

    Article  Google Scholar 

  18. Bru E, Virtanen T, Kjetilstad V, Niemiec CP. Gender differences in the strength of association between perceived support from teachers and student engagement. Scand J Educ Res. 2019;65(1):153.

    Article  Google Scholar 

  19. Li W, Gao W, Sha J. Perceived teacher autonomy support and school engagement of Tibetan students in elementary and middle schools: mediating effect of self-efficacy and academic emotions. Front Psychol. 2020;11:50.

    Article  Google Scholar 

  20. Engels MC, Colpin H, Van Leeuwen K, Bijttebier P, Van Den Noortgate W, Claes S, et al. Behavioral engagement, peer status, and teacher-student relationships in adolescence: a longitudinal study on reciprocal influences. J Youth Adolesc. 2016;45(6):1192–207.

    Article  Google Scholar 

  21. Lietaert S, Roorda D, Laevers F, Verschueren K, De Fraine B. The gender gap in student engagement: the role of teachers’ autonomy support, structure, and involvement. Br J Educ Psychol. 2015;85(4):498–518.

    Article  Google Scholar 

  22. Yin X, Huang Y, Zhang X, Chen Y, Wang M, Qian H. Influencing factors and improvement path of academic engagement among college students in the context of epidemic prevention and control. Int J Environ Res Public Health. 2022;19(19):12939.

    Article  Google Scholar 

  23. Yang J, Zhang G, Huang R, Yan P, Hu P, Huang L, et al. Nomograms predicting self-regulated learning levels in Chinese undergraduate medical students. Front Psychol. 2019;10:2858.

    Article  Google Scholar 

  24. Xing L, Lu Y, Shi Z, Liu W, Zhang H. Moderating effect of organizational climate on the relationship between research motivation and learning engagement among nurses taking part-time master’s program: a cross-sectional study. Nurse Educ Pract. 2023;68:103604.

    Article  Google Scholar 

  25. Ngussa BM, Fitriyah FK, Diningrat SWM. Correlation between Facebook use, mental health and learning engagement: a case of universities in Surabaya City, Indonesia. Turkish Online J Distance Educ. 2021;22(1):229–45.

    Article  Google Scholar 

  26. Rhee BS, 공희정. What determines student engagement?: Focusing on the effects of college students’ perceived learning environment and institutional features. J Educ Admin. 2014;32(3):177–207.

  27. Bangasser DA, Shors TJ. Critical brain circuits at the intersection between stress and learning. Neurosci Biobehav Rev. 2010;34(8):1223–33.

    Article  Google Scholar 

  28. Zhang G, Wu H, Xie A, Cheng H. The association between medical student research engagement with learning outcomes. Med Educ Online. 2022;27(1):2100039.

    Article  Google Scholar 

  29. Cao YQ, Huang RY. Investigation on the status quo of student engagement in a medical college based on demographic characteristics. Zhonghua Yi Xue Jiao Yu Za Zhi. 2022;42(12):1089–92.

    Google Scholar 

  30. Slavin SJ. Medical student mental health: culture, environment, and the need for change. JAMA. 2016;316(21):2195–6.

    Article  Google Scholar 

  31. Akbasli S, Arastaman G, Gun F, Turabik T. School engagement as a predictor of burnout in University students. Pamukkale Univ J Educ. 2019;45:293–309.

    Article  Google Scholar 

  32. Wang J, Bu L, Li Y, Song J, Li N. The mediating effect of academic engagement between psychological capital and academic burnout among nursing students during the COVID-19 pandemic: a cross-sectional study. Nurse Educ Today. 2021;102:104938.

    Article  Google Scholar 

  33. Chang WW, Shi LX, Zhang L, Jin YL, Yu JG. The mental health status and associated factors among medical students engaged in online learning at home during the pandemic: a cross-sectional study from China. Front Psych. 2021;12:755503.

    Article  Google Scholar 

  34. Liu S, Zou S, Zhang D, Wang X, Wu X. Problematic Internet use and academic engagement during the COVID-19 lockdown: the indirect effects of depression, anxiety, and insomnia in early, middle, and late adolescence. J Affect Disord. 2022;309:9–18.

    Article  Google Scholar 

  35. McCoy L, Pettit RK, Lewis JH, Allgood JA, Bay C, Schwartz FN. Evaluating medical student engagement during virtual patient simulations: a sequential, mixed methods study. BMC Med Educ. 2016;16:20.

    Article  Google Scholar 

  36. Thill M, Rosenzweig JW, Wallis LC. The relationship between student demographics and student engagement with online library instruction modules. Evid Based Libr Inf Pract. 2016;11(3):4.

    Article  Google Scholar 

  37. Yardley S, Teunissen PW, Dornan T. Experiential learning: AMEE Guide No. 63. Med Teach. 2012;34(2):e102–115.

    Article  Google Scholar 

  38. Stander J, Grimmer K, Brink Y. Learning styles of physiotherapists: a systematic scoping review. BMC Med Educ. 2019;19(1):2.

    Article  Google Scholar 

  39. Mpwanya MF, Dockrat S. Assessing learning styles of undergraduate logistics students using Kolb’s learning style inventory: a cross-sectional survey. S Afr J High Educ. 2020;34(3):210–28.

    Google Scholar 

  40. Hartman BA, Miller BK, Nelson DL. The effects of hands-on occupation versus demonstration on children’s recall memory. Am J Occup Ther. 2000;54(5):477–83.

    Article  Google Scholar 

Download references

Acknowledgements

We thank all the participants for their help.

Patient and public involvement

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

Funding

This study was supported in part by the National Natural Science Foundation of China (Grant No. 81702659; 81772856; 82173168; 82073207; 81801620; 81930057; 81772076; 81971836); CAMS Innovation Fund for Medical Sciences (2019-I2M-5-076), Deep Blue Talent Project of Naval Medical University, 234 Academic Climbing Programme of Changhai hospital and Achievements Supportive Fund (2018-CGPZ-B03); Shanghai Rising-Star Program (Sailing Special Program) (No. 23YF1458400); Youth Fund of Shanghai Municipal Health Planning Commission (No.2017YQ054; 2017Y0117); Interdisciplinary Program of Shanghai Jiao Tong University (No.YG2017MS26); Shanghai Rising-Star Program (No. 21QA1407500); Shanghai Talent Development Fund (No.2018094); Shanghai Municipal Health Commission (No.201940306); Henan medical science and technology research project (No. 201602031); Key project of provincial and ministerial co-construction of Henan Medical Science and Technology (No. SBGJ202002031). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Runzhi Huang, Guoyang Zhang and Zhitong Zhou contributed equally to this work, and all should be considered first author.

Authors and Affiliations

  1. Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China

    Runzhi Huang, Shuyuan Xian, Dayuan Xu & Shizhao Ji

  2. Research Unit of key techniques for treatment of burns and combined burns and trauma injury, Chinese Academy of Medical Sciences, Shanghai, People’s Republic of China

    Runzhi Huang, Shuyuan Xian, Dayuan Xu & Shizhao Ji

  3. Maastricht University School of Health Professions Education, Maastricht, The Netherlands

    Guoyang Zhang & Jie Zhang

  4. Tongji University School of Medicine, Shanghai, 200092, China

    Zhitong Zhou

  5. Mental Health Education and Consultation Center, Chongqing Medical University, 61 Daxuecheng Middle Road, Chongqing, 401331, China

    Min Lin

  6. Office of Educational Administration, Shanghai University, Shanghai, 200444, China

    Meiqiong Gong

  7. Department of Orthopedics, School of Medicine, Shanghai General Hospital, Shanghai Jiaotong University, 100 Haining Road, Shanghai, China

    Huabin Yin & Tong Meng

  8. Department of Rheumatology and Immunology, Second Affiliated Hospital of Naval Medical University, Shanghai, China

    Xin Liu

  9. Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Beijing, 100069, China

    Xiaonan Wang

  10. Department of Health Statistics, School of Public Health, Air Force Medical University, No.169, Changle West Road, Xi’an, 710032, China

    Yue Wang

  11. Faculty of Medicine, Jinggangshan University, 28 Xueyuan Road, Ji’An, 343009, China

    Wenfang Chen

  12. Basic Medical College, Harbin Medical University, 157 Baojian Road, Harbin, Heilongjiang, 150081, China

    Chongyou Zhang

  13. Frist Clinical Medical College, Mudanjiang Medical University, 66 Tongxiang Street, Mudanjiang, 157011, China

    Erbin Du

  14. Department of Human Anatomy, Laboratory of Clinical Applied Anatomy, School of Basic Medical Sciences, Fujian Medical University, 1 Xuefu North Road, Fuzhou, 350122, China

    Qing Lin

  15. National Centre for Health Professions Education Development, Peking University, Beijing, 100191, China

    Hongbin Wu

  16. Institute of Medical Education, Peking University, 5 YiHeYuan Road, Beijing, 100871, China

    Hongbin Wu

  17. Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China

    Zongqiang Huang

  18. Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, 2699 Gaoke West Road, Shanghai, 201204, China

    Jie Zhang

Authors
  1. Runzhi Huang
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  2. Guoyang Zhang
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  3. Zhitong Zhou
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  4. Min Lin
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  5. Shuyuan Xian
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  6. Meiqiong Gong
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  7. Huabin Yin
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  8. Tong Meng
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  9. Xin Liu
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  10. Xiaonan Wang
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  11. Yue Wang
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  12. Wenfang Chen
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  13. Chongyou Zhang
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  14. Erbin Du
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  15. Qing Lin
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  16. Hongbin Wu
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  17. Zongqiang Huang
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  18. Jie Zhang
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  19. Dayuan Xu
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  20. Shizhao Ji
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Contributions

Conception/design: Runzhi Huang, Guoyang Zhang, Zhitong Zhou, Min Lin, Shuyuan Xian, Meiqiong Gong, Huabin Yin, Tong Meng, Xin Liu, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Erbin Du, Qing Lin, Dayuan Xu, Hongbin Wu, Zongqiang Huang, Jie Zhang, Shizhao Ji. Collection and/or assembly of data: Runzhi Huang, Guoyang Zhang, Zhitong Zhou, Min Lin, Shuyuan Xian, Meiqiong Gong, Huabin Yin, Tong Meng, Xin Liu, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Erbin Du, Qing Lin, Dayuan Xu, Hongbin Wu, Zongqiang Huang, Jie Zhang, Shizhao Ji. Data analysis and interpretation: Runzhi Huang, Guoyang Zhang, Zhitong Zhou, Min Lin, Shuyuan Xian, Meiqiong Gong, Huabin Yin, Tong Meng, Xin Liu, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Erbin Du, Qing Lin, Dayuan Xu, Hongbin Wu, Zongqiang Huang, Jie Zhang, Shizhao Ji. Manuscript writing: Runzhi Huang, Guoyang Zhang, Zhitong Zhou, Min Lin, Shuyuan Xian, Meiqiong Gong, Huabin Yin, Tong Meng, Xin Liu, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Erbin Du, Qing Lin, Dayuan Xu, Hongbin Wu, Zongqiang Huang, Jie Zhang, Shizhao Ji. Final approval of manuscript: Runzhi Huang, Guoyang Zhang, Zhitong Zhou, Min Lin, Shuyuan Xian, Meiqiong Gong, Huabin Yin, Tong Meng, Xin Liu, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Erbin Du, Qing Lin, Dayuan Xu, Hongbin Wu, Zongqiang Huang, Jie Zhang, Shizhao Ji.

Corresponding authors

Correspondence to Min Lin, Huabin Yin, Tong Meng, Xin Liu, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Erbin Du, Qing Lin, Hongbin Wu, Zongqiang Huang, Jie Zhang, Dayuan Xu or Shizhao Ji.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of The First Affiliated Hospital of Zhengzhou University. All participant centers provided ‘written informed consent’ to take part in the study. All authors confirmed that all methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Information

Additional file 1: Table S1.

Utrecht work engagement scale - student version (English version). Table S2. Utrecht work engagement scale - student version (English version). Table S3. Univariate logistic regression analysis of learning engagement level.

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Huang, R., Zhang, G., Zhou, Z. et al. A multi-center cross-sectional study on identification of influencing factors of medical students’ emotional engagement in China. BMC Med Educ 23, 838 (2023). https://doi.org/10.1186/s12909-023-04504-w

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Keywords

BMC Medical Education

ISSN: 1472-6920

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