Literature DB >> 34190150

Prevalence of burnout in medical students in China: A meta-analysis of observational studies.

You Li1, Liang Cao2, Chunbao Mo1, Dechan Tan1, Tingyu Mai1, Zhiyong Zhang1.   

Abstract

ABSTRACT: This meta-analysis aimed to estimate the prevalence of burnout among medical students in China.A systematic search from the following electronic databases: China National Knowledge Infrastructure, Wangfang database, VIP database, Chinese biomedical literature database, PubMed, Embase, Web of Science, and Google Scholar was independently conducted by 2 reviewers from inception to September 2019. The data were analyzed using stata software Version 11. Heterogeneity was assessed using I2 tests, and publication bias was evaluated using funnel plots and Egger's test. The source of heterogeneity among subgroups was determined by subgroup analysis of different parameters.A total of 48 articles with a sample size of 29,020 met the inclusion criteria. The aggregate prevalence of learning burnout was 45.9% (95% confidence interval [CI] = 38.1%-53.8%). The prevalence rate of high emotional exhaustion was 37.5% (95% CI: 21.4%-53.7%). The percentage was 44.0% (95% CI: 29.2%-58.8%) for low personal accomplishment. The prevalence rate was 36.0% (95% CI: 23.0%-48.9%) in depersonalization dimension. In the subgroup analysis by specialty, the prevalence of burnout was 30.3% (95% CI: 28.6%-32.0%) for clinical medicine and 43.8% (95% CI: 41.8%-45.8%) for other medical specialties. The total prevalence of burnout between men and women was 46.4% (95% CI: 44.8%-47.9%) and 46.6% (95% CI: 45.5%-47.6%), respectively. The prevalence of burnout with Rong Lian's scale was 43.7% (42.1%-45.2%), and that with the other scales was 51.4% (50.4%-52.4%). The prevalence rates were 62.9% (61.3%-64.6%), 58.7% (56.3%-61.1%), 46.5% (42.9%-50.2%), and 56.0% (51.6%-60.4%) from Grades 1 to 4, respectively. There was a statistically significant difference among the different grades (P = .000).Our findings suggest a high prevalence of burnout among medical students. Society, universities, and families should take appropriate measures and allot more care to prevent burnout among medical students.
Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

Entities:  

Mesh：

Year:  2021        PMID： 34190150      PMCID： PMC8257868          DOI： 10.1097/MD.0000000000026329

Source DB:  PubMed          Journal:  Medicine (Baltimore)        ISSN： 0025-7974            Impact factor:   1.817


Introduction

According to Maslach and Jackson, burnout is a psychological syndrome involving emotional exhaustion, depersonalization, and reduced personal accomplishment that occurs among individuals from a specific environment.[ Emotional exhaustion in humans is defined as a state of overextension and feeling emotionally drained. Individuals who experience burnout feel empty, lack energy, and fail to communicate well with others. Depersonalization refers to the attitude of employees interacting with colleagues in a negative, cold, and indifferent manner. Gradually, they develop contemptuous conceptions of cynicism. The personal achievement category is affected by low self-esteem, reflecting the feeling of being ineffective at work and not being up for the position.[ Burnout mainly includes job/professional and study/academic/learning burnout. The learning burnout of students includes: emotional exhaustion, which refers to the fatigue caused by students’ strong study needs; depersonalization, considered a development of skepticism and apathy toward the research; and low professional efficacy, manifested as the low learning efficiency of students.[ Presently, the study of medicine is more complex, which highlights the characteristics of professionalism, autonomy, and exploration. Medical students are in a critical period of physical and mental development, while learning knowledge and skills. Given that most medical students will inevitably become doctors and specialize in a particular profession, they experience more mental stress and academic pressure than other college students.[ If medical students are not able to relieve themselves of pressure, negative effects may occur. For example, burnout has been linked to medical errors, job failures, substance abuse, depression and suicidal ideation, and the rates of burnout among doctors have been rising in recent years.[ Previous studies, among residents and medical students,[ have found that the prevalence of burnout ranged from 17.6% to 82%. Although China has the highest number of medical practitioners worldwide, studies on the experiences of Chinese medical students are poorly represented in the English language literature.[ The characteristics and influencing factors in Chinese medical students’ learning burnout must be explored, and possible solutions must be developed to prevent learning burnout among medical students. Through this, medical students can better adapt to the environment and serve in their future careers.

Materials and methods

Literature search

A systematic search from the following electronic databases: China National Knowledge Infrastructure, Wangfang database, VIP database, Chinese biomedical literature database, PubMed, Embase, Web of Science, and Google Scholar was independently conducted by 2 reviewers. Literature retrieval included relevant research papers published in English or Chinese from inception to September 2019. Primary studies with all possible combinations of the Medical Subject Heading terms burnout, burn out, professional burnout, study burnout, medicine, medical students, and China were identified. Published articles were chosen; hence, ethical approval was not required.

Inclusion criteria and exclusion criteria

These studies were included in this meta-analysis if: studies on learning burnout were published in China and abroad; medical students (including all medical specialties); the study was designed as a cross-sectional study; the literature reported the sample number of medical students and the prevalence rate of learning burnout, or the prevalence rate could be calculated from the data in the light of the articles. The exclusion criteria were as follows: repeated publications; literature whose data cannot be used; literature with incomplete information; and non-Chinese or English literature. Discrepancies were resolved through discussion.

Data extraction

The following data were recorded: name of the first author, year of publication, sample size, prevalence rate of learning burnout, name of journal, and questionnaire return ratio. Additional information was extracted when required. The collection information is shown in supplementary Table 1. When necessary, the original authors were contacted for additional information.

Quality assessment

The Newcastle–Ottawa Scale for nonrandomized studies was used to assess the quality of our study.[ The criteria were divided into 3 categories: selection (4 items), comparability (1 item), and exposure in case–control studies (2 items). A study was awarded a maximum of 1 star for each item. This is true for every term, except for the comparability of the 2 stars. The higher the score, the better the quality. Scores of 0 to 3, 4 to 6, and 7 to 9 were regarded as reflecting low, medium, and high quality, respectively.

Statistical analysis

The data were analyzed using Stata version 11.0 (Stata Corporation, College Station, TX). The prevalence of burnout and 95% confidence intervals (CIs) were calculated using a random-effects model. I2 represents the proportion of total variation attributable to between-study heterogeneity rather than random error or chance. I2 values were 25%, 50%, and 75%, indicating low, medium, and high heterogeneity, respectively. Generally, a random-effect model was selected to calculate the corresponding parameters if the value of I2 was greater than 50%.[ Otherwise, a fixed-effects model was used. Funnel plot and Egger's test were used to evaluate publication bias and the statistical publication bias was set at P < .10.[ The source of heterogeneity among subgroups was determined by subgroup analysis of different parameters.

Results

Characteristics of the studies

The search strategy obtained 1008 articles from all the databases. A total of 89 studies remained and 919 papers were excluded because they were reviews, duplicates, or irrelevant studies. After reading the full text of the 89 papers, 48 articles meeting the inclusion criteria in our meta-analysis were selected (Fig. 1).[ The characteristics of the included studies are summarized in Table 1. The included studies were graded as moderate or high according to the Newcastle–Ottawa Scale (Table 2).
Figure 1

A flowchart of study selection.

Table 1

Basic characteristics of the studies in the meta-analysis.

StudySample sizeNumber of burnoutResponse rate (%)Mean agePrevalence of burnout (%)SpecialtyInvestigation table
YC Zhang, 201724811393.9420.51 ± 1.7145.56MedicineRong Lian
YM Wei, 20163041879522.16 ± 1.561.5Clinical medicineRong Lian
LJ Yang, 201528920594.5NM70.9MedicineYongxin Li
K Li, 201858672100NM12.3MedicineRong Lian
Y Liao, 201162762798.9NM52.15MedicineRong Lian
K Zhang, 201728311981NM42.05Clinical medicineRong Lian
H Liu, 2015400158100NM39.5MedicineRong Lian
H Wu, 201573973992.61NM45.06Rural oriented medical studentsRong Lian
HC Zhu, 2012876287NM71.1Medical students(7 yrs)MBI-GS
X Wang, 2018121193490.24NM77.13NurseMBI-SS
TP Wang, 201760022491.88NM37.3Examination and pharmacyRong Lian
XH Yang, 201577544196.9NM57.35MedicineRong Lian
SX Zhang, 201677134486NM44.6MedicineRong Lian
PY Su, 201894468499.1617–2272.5MedicineRong Lian
L Liu, 201861921695.2NM34.9MedicineRong Lian
SJ Yu, 201835535588.75NM78.9MedicineRong Lian
L Li, 2018136849293.25NM36MedicineRong Lian
JH Zhai, 201463526490.71NM41.65MedicineRong Lian
L Li, 201760022491.88NM37.3MedicineRong Lian
PY Liang, 201763424390.1NM38.33MedicineRong Lian
Y Zhu, 20121846976.220–2537.5MedicineRong Lian
XF Zeng, 201452314297.39NM27.15MedicineQizhi Zhang
YZ Li, 20142606796.3NM25.8MedicineRong Lian
Y Zhang, 201835017891.117–2450.8NurseRong Lian
Tian L, 20191814151637NM83.6Neurology postgraduatesMaslach C
Liu H, 20184534258.0820.21 ± 1.469.27MedicineMBI-SS
Zukelatalaiti, 201263715396.51NM45.13MedicineRong Lian
DL Yang, 201157621096NM36.46MedicineRong Lian
P Xu, 200961024193.817–2439.5MedicineRong Lian
YJ Hui, 20121835121895.32NM66.4NurseRong Lian
LH Lu, 20182431113497.24NM46.65MedicineRong Lian
L Chen, 20134436898.44NM15.3NurseRong Lian
YY Li, 201728227888.1NM98.6NurseRong Lian
R Sun, 2012350120100NM34.4NurseRong Lian
P Hao, 2015109231496.9819.34 ± 1.4228.75NurseRong Lian
YX Li, 20079069NMNM76.7MedicineYongxin Li
DB Li, 201648321696.6NM44.72MedicineNM
HJ Ma, 201858672100NM12.3MedicineRong Lian
ZP Li, 201336710993.62NM29.7MedicineRong Lian
P Hao, 201359217997.21NM30.24NurseRong Lian
SX Lv, 201492769791.2NM75.19MedicineRong Lian
F Jiang, 200930911796.56NM37.86NurseRong Lian
T Tang, 201958812890.46NM21.77MedicineYongxin Li
XF Yu, 201529013793.55NM47.24MedicineRong Lian
L Yang, 20142028384NM41.09NurseRong Lian
Y Pan, 201217011794.4NM68.82MedicineNM
JH Ma, 2014192819621.4 ± 0.542.19NurseRong Lian
LY Zhou, 201030911296.5619–2336.25NurseRong Lian

NM = not mentioned.

Table 2

Quality assessment of included studies using the Newcastle-Ottawa scale.

SelectionComparabilityOutcome
StudyIs the case definition adequateRepresentativeness of the casesSelection of controlsDefinition of controlsStudy controls for –———Study controls for any additional factorAscertainment of exposureSame method of ascertainment for cases and controlsNon-response rateScore
YC Zhang, 2017★★—★★—★★—6
YM Wei, 2016★★—★★★★★—7
LJ Yang, 2015★★—★★—★★—6
K Li, 2018★★—★★—★★★7
Y Liao, 2011★★—★★—★★—6
K Zhang, 2017★★—★★★★★—7
H Liu, 2015★★—★★—★★★7
H Wu, 2015★★—★★—★★—6
HC Zhu, 2012★★—★★★★★—7
X Wang, 2018★★—★★—★★—6
TP Wang, 2017★★—★★—★★—6
XH Yang, 2015★★—★★—★★—6
SX Zhang, 2016★★—★★—★★—6
PY Su, 2018★★—★★—★★—6
L Liu, 2018★★—★★—★★—6
SJ Yu, 2018★★—★★—★★—6
L Li, 2018★★—★★—★★—6
JH Zhai, 2014★★—★★—★★—6
L Li, 2017★★—★★—★★—6
PY Liang, 2017★★—★★—★★—6
Y Zhu, 2012★★—★★—★★—6
XF Zeng, 2014★★—★★—★★—6
YZ Li, 2014★★—★★—★★—6
Y Zhang, 2018★★—★★★★—6
Tian L, 2019★★—★★★★★—7
Liu H, 2018★★—★★—★★—6
Zukelatalaiti, 2012★★—★★—★★—6
DL Yang, 2011★★—★★—★★—6
P Xu, 2009★★—★★—★★—6
YJ Hui, 2012★★—★★—★★—6
LH Lu, 2018★★—★★—★★—6
L Chen, 2013★★—★★—★★—6
YY Li, 2017★★—★★—★★—6
R Sun, 2012★★—★★—★★★7
P Hao, 2015★★—★★—★★—6
YX Li, 2007★★—★★—★★—6
DB Li, 2016★★—★★—★★—6
HJ Ma, 2018★★—★★—★★★7
ZP Li, 2013★★—★★—★★—6
P Hao, 2013★★—★★—★★—6
SX Lv, 2014★★—★★—★★—6
F Jiang, 2009★★—★★—★★—6
T Tang, 2019★★—★★—★★—6
XF Yu, 2015★★—★★—★★—6
L Yang, 2014★★—★★—★★—6
Y Pan, 2012★★—★★—★★—6
JH Ma, 2014★★—★★—★★—6
LY Zhou, 2010★★—★★—★★—6
A flowchart of study selection. Basic characteristics of the studies in the meta-analysis. NM = not mentioned. Quality assessment of included studies using the Newcastle-Ottawa scale.

Aggregate prevalence of burnout

A heterogeneity test was carried out for 48 studies, and the P value was <.10, and I2 was 99.6%, indicating that considerable heterogeneity was present. Therefore, the random-effects model was used for the meta-analysis. The aggregate prevalence of learning burnout was 45.9% (95% CI = 38.1%–53.8%), as shown in Figure 2.
Figure 2

The aggregate prevalence of burnout in all residents.

The aggregate prevalence of burnout in all residents. Emotional exhaustion The prevalence rate of high emotional exhaustion was 37.5% (95% CI: 21.4%–53.7%). Figure 3 shows a forest plot with high EE.
Figure 3

The aggregate prevalence of emotional exhaustion.

Low personal accomplishment The percentage was 44% (95% CI: 29.2%–58.8%) for low personal accomplishment. Figure 4 illustrates the forest plot of low PA.
Figure 4

The aggregate prevalence of low personal accomplishment.

Depersonalization The prevalence rate was 36.0% (95% CI: 23.0%–48.9%) in the depersonalization dimension. A forest plot of a high DP is shown in Figure 5.
Figure 5

The aggregate prevalence of depersonalization.

The aggregate prevalence of emotional exhaustion. The aggregate prevalence of low personal accomplishment. The aggregate prevalence of depersonalization.

Total publication bias

Publication bias was found through the asymmetric funnel plot and the results of the Egger's test (Fig. 6) (Begg's score <0.1).
Figure 6

The asymmetric funnel plot of publication bias.

The asymmetric funnel plot of publication bias.

The result of trim and filling

The following figure shows the funnel plot obtained after the addition of the 11 studies. The “squares” in the figure are additional studies. The funnel plot obtained after the addition of 11 studies showed no obvious asymmetry, indicating no publication bias (Fig. 7).
Figure 7

The asymmetric funnel plot of publication bias after trim and filling.

The asymmetric funnel plot of publication bias after trim and filling.

The results of combined effect before trim and filling

The results of fixed- and random-effects were all statistically different (P = .0000) in the values before and after trim and filling. The estimated values of the combined effect did not change significantly, indicating that the effect of publication bias was not significant and the results were relatively stable (Fig. 8).
Figure 8

The results of combined effect before trim and filling.

The results of combined effect before trim and filling.

Subgroup analysis

Factors that may lead to heterogeneity were analyzed, such as gender, specialty, and the scale of burnout by subgroup. The results showed high heterogeneity; hence, the random-effects model was adopted to combine the effect size. In the subgroup analysis by specialty, the prevalence of burnout was 30.3% (95% CI: 28.6%–32.0%) for clinical medicine and 43.8% (95% CI: 41.8%–45.8%) for other medical specialties. There was a statistically significant difference in the prevalence rate between different specialties. In the subgroup analysis by gender, the prevalence of burnout was 46.4% (95% CI: 44.8%–47.9%) for males and 46.6% (95% CI: 45.5%–47.6%) for females. The difference in the prevalence rate between men and women was not statistically significant (P = .093). In the subgroup analysis by selecting the scale, the prevalence of burnout was 43.7% (42.1%–45.2%) with the scale conducted by Rong Lian, and the prevalence of burnout was 51.4% (50.4%–52.4%) with the other scale. The difference in prevalence rates with different scales was statistically significant (P = .000). The prevalence rates were 62.9% (61.3%–64.6%), 58.7% (56.3%–61.1%), 46.5% (42.9%–50.2%), and 56.0% (51.6%–60.4%) from Grades 1 to 4, respectively. Statistical significance was observed among the different grades (P = .000) (Table 3).
Table 3

Prevalence of burnout in residents by subgroup analysis.

ParameterDocument numberSample size (n)Burnout prevalence (%) and 95% CII2 (%)PPz
Gender
 Male11244346.4% (44.8–47.9)99.0.0000.093
 Female11501646.6% (45.5–47.6)99.6.000
Specialty
 Clinical medicine5165930.3% (28.6–32.0)99.3.0000.000
 Other medicine5134343.8% (41.8–45.8)99.4.000
Scale
 Rong Lian3823,31243.7% (42.1–45.2)99.6.0000.000
 Other scale10570851.4% (50.4–52.4)99.7.000
Grade
 18271662.9% (61.3–64.6)98.9.0000.000
 26132258.7% (56.3–61.1)98.3.000
 3455546.5% (42.9–50.2)98.3.000
 4338056.0% (51.6–60.4)98.2.000

CI, confidence interval; Pz, the comparison between subgroups.

Prevalence of burnout in residents by subgroup analysis. CI, confidence interval; Pz, the comparison between subgroups.

Discussion

The results of our meta-analysis, which included 48 articles and 29,020 subjects, can be summarized as follows: 45.9% (95% CI: 38.1%–53.8%) of Chinese medical students reported burnout syndrome. The results showed that low personal accomplishment was the most widespread dimension affecting medical students’ learning burnout accounting for 44% of the sample. This was followed by high emotional exhaustion, which occurred in 37.5% of the medical students in our meta-analysis. The lowest prevalence was depersonalization, which affected 36% of medical students. These mean that students showed high levels of emotional exhaustion, low personal accomplishment, and high depersonalization. The burnout prevalence among medical students is around 44% in the worldwide according to the findings of Frajerman et al.[ The prevalence of learning burnout among Chinese medical students is on par with the worldwide burnout prevalence. The prevalence and trend of burnout in personal accomplishment, emotional exhaustion, and depersonalization were similar to Kansoun Ziad's study of French physicians.[ The prevalence of burnout was higher than that of medical students (35% in Germany),[ 40.4% for medical students in 2016 (in Spanish),[ in Australia (6%),[ and in Brazil (26.4%).[ It was lower than dental students (50.3%),[ medical students (55%, 56% in the US),[ and (52%) in Trinidad and Tobago.[ The rate of learning burnout is similar to that of foreign medical students. Concurrently, there are also higher and lower rates. These differences may be related to differences in the educational system between domestic and foreign medical students. The reason for this is that differences exist in the curriculum and the essential requirements of medical students in various countries. For example, some medical schools require a preliminary bachelor's degree.[ However, some medical staff begin their studies without any preliminary higher education.[ Concurrently, medical students have greater study pressure than do other professional college students. The asymmetric funnel plot and the results of Egger's test in our meta-analysis showed that publication bias was present. The research found that publication bias may affect the main conclusions of at least 15% to 21% of the meta-analysis. The main conclusions were obtained by correcting for potential publication bias using the trim and fill method.[ Thus, the trim and fill method was chosen to reanalyze the publication bias and found that the estimated value of the combined effect size did not change significantly, indicating that publication bias had little effect and the result was relatively stable. In the subgroup analysis, male participants reported lower levels of burnout than female participants, which is consistent with many beliefs that burnout is more commonly experienced by female employees. However, further studies are needed to elucidate the relationship between gender and burnout among medical students. The prevalence of burnout was 30.3% (28.6%–32.0%) and 43.8% (41.8%–45.8%) for clinical medicine and other medical specialties, respectively, in the subgroup analysis. The prevalence was lower for clinical medicine than for other medical specialties. This trend was consistent with other studies conducted by Montiel-Company José María[ and Montiel-Company.[ In the subgroup analysis, the prevalence of burnout was 43.7% and 51.4% for selecting the scale by Rong Lian et al. In our meta-analysis, the vast majority of researchers selected Rong-Lian scale. Based on the burnout scale by Marlach, Rong Lian compiled a burnout scale suitable for Chinese college students according to their characteristics. The prevalence of burnout was different, partly due to the different scales. The investigators mainly chose the Maslach Burnout Inventory (MBI-SS) to study the burnout of college students in foreign papers.[ Our results showed that the burnout rate was the highest at 62.9% (61.3%–64.6%) in freshman year and the lowest at 46.5% (42.9%–50.2%) in junior year. Ultimately, a statistically significant difference was observed. This is similar to Altannir Youssef's results that the first-year medical students have higher levels of burnout compared with other year medical students.[ It may be concerned with the freshmen merely entering the campus and not adapting well to the environment. The results of Thun-Hohenstein et al showed that the first-year medical students have lower levels of burnout compared with other year medical students. This is the opposite of what we found. The cause may be related with feeling for good fairness and high values, that is, motivation for the first-year students before a high workload (e.g., information to be learned) coming.[

Limitations

This meta-analysis has several limitations. First, high heterogeneity existed in the subgroup analysis of all influencing factors. Second, certain specialties in this meta-analysis were underrepresented. The distribution of the number of residents per specialty is uneven. Many references were included for the selected scale, and few were included for the major and gender, which had some influence on the results of the subgroup analysis. Third, publication bias was present because unpublished literature or data were not collected. Therefore, subgroup analysis based on continents should be interpreted with caution.

Conclusions

Our findings suggest a high prevalence of burnout among medical students. Society, universities, and families should take appropriate measures and allot more care to prevent burnout among medical students.

Acknowledgments

We would like to thank all the authors.

Author contributions

Conceptualization: You Li, Zhiyong Zhang. Data curation: Liang Cao, Chunbao Mo, Dechan Tan, Tingyu Mai. Formal analysis: Liang Cao. Investigation: Liang Cao. Methodology: Chunbao Mo. Supervision: Dechan Tan. Validation: Tingyu Mai. Visualization: Liang Cao. Writing – original draft: You Li. Writing – review & editing: Zhiyong Zhang.
  29 in total

1.  Job burnout.

Authors:  C Maslach; W B Schaufeli; M P Leiter
Journal:  Annu Rev Psychol       Date:  2001       Impact factor: 24.137

2.  Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses.

Authors:  Andreas Stang
Journal:  Eur J Epidemiol       Date:  2010-07-22       Impact factor: 8.082

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Journal:  Mayo Clin Proc       Date:  2015-12       Impact factor: 7.616

4.  Personal life events and medical student burnout: a multicenter study.

Authors:  Liselotte N Dyrbye; Matthew R Thomas; Jefrey L Huntington; Karen L Lawson; Paul J Novotny; Jeff A Sloan; Tait D Shanafelt
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