Meta-Analysis of Observational Studies in Epidemiology Medicine ®

OPEN

Prevalence of burnout in medical students in China

A meta-analysis of observational studies
You Li, MDa , Liang Cao, MBb, Chunbao Mo, MMa, Dechan Tan, MMa, Tingyu Mai, MMa,
∗
Zhiyong Zhang, MDa,

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.
Abbreviation: CI = confidence interval.
Keywords: burnout, medical students, meta-analysis, prevalence

1. Introduction
Editor: Chiedu Eseadi.
YL and LC contributed equally to this work. According to Maslach and Jackson, burnout is a psychological
This study was funded from the National Natural Science Foundation of China syndrome involving emotional exhaustion, depersonalization,
(NSFC-81960583). and reduced personal accomplishment that occurs among
All relevant data are within the paper and its Supporting Information files. individuals from a specific environment.[1] Emotional exhaustion
The authors have no conflict of interest to disclose. in humans is defined as a state of overextension and feeling
Supplemental Digital Content is available for this article.
emotionally drained. Individuals who experience burnout feel
empty, lack energy, and fail to communicate well with others.
All data generated or analyzed during this study are included in this published
article [and its supplementary information files]. The datasets generated during Depersonalization refers to the attitude of employees interacting
and/or analyzed during the current study are publicly available. with colleagues in a negative, cold, and indifferent manner.
a
Department of Environmental Health and Occupational Medicine, School of Gradually, they develop contemptuous conceptions of cynicism.
Public Health, Guilin Medical University, Guilin, b Department of Experimental The personal achievement category is affected by low self-esteem,
Teaching Center, School of Public Health, Guilin Medical University, Guilin, reflecting the feeling of being ineffective at work and not being up
Guangxi Province, China.
∗
for the position.[2,3] Burnout mainly includes job/professional
Correspondence: Zhiyong Zhang, Department of Environmental Health and and study/academic/learning burnout.
Occupational Medicine, School of Public Health, Guilin Medical University, No. 1
Zhiyuan Road, Lingui District, Guilin, Guangxi, 541199, China
The learning burnout of students includes: emotional exhaus-
(e-mail: rpazz@163.com). tion, which refers to the fatigue caused by students’ strong study
Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. needs; depersonalization, considered a development of skepti-
This is an open access article distributed under the terms of the Creative cism and apathy toward the research; and low professional
Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is efficacy, manifested as the low learning efficiency of students.[4,5]
permissible to download, share, remix, transform, and buildup the work provided Presently, the study of medicine is more complex, which
it is properly cited. The work cannot be used commercially without permission
from the journal.
highlights the characteristics of professionalism, autonomy,
and exploration. Medical students are in a critical period of
How to cite this article: Li Y, Cao L, Mo C, Tan D, Mai T, Zhang Z. Prevalence of
burnout in medical students in China: A meta-analysis of observational studies. physical and mental development, while learning knowledge and
Medicine 2021;100:26(e26329). skills. Given that most medical students will inevitably become
Received: 17 August 2020 / Received in final form: 29 April 2021 / Accepted: 26 doctors and specialize in a particular profession, they experience
May 2021 more mental stress and academic pressure than other college
http://dx.doi.org/10.1097/MD.0000000000026329 students.[6]

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Li et al. Medicine (2021) 100:26 Medicine

If medical students are not able to relieve themselves of Additional information was extracted when required. The
pressure, negative effects may occur. For example, burnout has collection information is shown in supplementary Table 1, http://
been linked to medical errors, job failures, substance abuse, links.lww.com/MD2/A256. When necessary, the original authors
depression and suicidal ideation, and the rates of burnout among were contacted for additional information.
doctors have been rising in recent years.[7,8] Previous studies,
among residents and medical students,[9,10] have found that the
2.4. Quality assessment
prevalence of burnout ranged from 17.6% to 82%. Although
China has the highest number of medical practitioners The Newcastle–Ottawa Scale for nonrandomized studies was
worldwide, studies on the experiences of Chinese medical used to assess the quality of our study.[12] The criteria were
students are poorly represented in the English language divided into 3 categories:
literature.[11] (1) selection (4 items),
The characteristics and influencing factors in Chinese medical (2) comparability (1 item), and
students’ learning burnout must be explored, and possible
(3) exposure in case–control studies (2 items).
solutions must be developed to prevent learning burnout among
medical students. Through this, medical students can better adapt A study was awarded a maximum of 1 star for each item. This
to the environment and serve in their future careers. 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
2. Materials and methods
quality, respectively.
2.1. Literature search
A systematic search from the following electronic databases: 2.5. Statistical analysis
China National Knowledge Infrastructure, Wangfang database, The data were analyzed using Stata version 11.0 (Stata
VIP database, Chinese biomedical literature database, PubMed, Corporation, College Station, TX). The prevalence of burnout
Embase, Web of Science, and Google Scholar was independently and 95% confidence intervals (CIs) were calculated using a
conducted by 2 reviewers. Literature retrieval included relevant random-effects model. I2 represents the proportion of total
research papers published in English or Chinese from inception to variation attributable to between-study heterogeneity rather than
September 2019. Primary studies with all possible combinations random error or chance. I2 values were 25%, 50%, and 75%,
of the Medical Subject Heading terms burnout, burn out, indicating low, medium, and high heterogeneity, respectively.
professional burnout, study burnout, medicine, medical students, Generally, a random-effect model was selected to calculate the
and China were identified. Published articles were chosen; hence, corresponding parameters if the value of I2 was greater than
ethical approval was not required. 50%.[13,14] Otherwise, a fixed-effects model was used. Funnel
plot and Egger’s test were used to evaluate publication bias and
2.2. Inclusion criteria and exclusion criteria the statistical publication bias was set at P < .10.[15] The source of
heterogeneity among subgroups was determined by subgroup
These studies were included in this meta-analysis if: analysis of different parameters.
(1) studies on learning burnout were published in China and
abroad; 3. Results
(2) medical students (including all medical specialties);
(3) the study was designed as a cross-sectional study; 3.1. Characteristics of the studies
(4) the literature reported the sample number of medical students The search strategy obtained 1008 articles from all the databases.
and the prevalence rate of learning burnout, or the prevalence A total of 89 studies remained and 919 papers were excluded
rate could be calculated from the data in the light of the because they were reviews, duplicates, or irrelevant studies. After
articles. reading the full text of the 89 papers, 48 articles meeting the
The exclusion criteria were as follows: inclusion criteria in our meta-analysis were selected (Fig. 1).[16–63]
The characteristics of the included studies are summarized in
(1) repeated publications;
Table 1. The included studies were graded as moderate or high
(2) literature whose data cannot be used;
according to the Newcastle–Ottawa Scale (Table 2).
(3) literature with incomplete information; and
(4) non-Chinese or English literature.
3.2. Aggregate prevalence of burnout
Discrepancies were resolved through discussion.
A heterogeneity test was carried out for 48 studies, and the P
value was <.10, and I2 was 99.6%, indicating that considerable
2.3. Data extraction heterogeneity was present. Therefore, the random-effects model
The following data were recorded: was used for the meta-analysis. The aggregate prevalence of
learning burnout was 45.9% (95% CI = 38.1%–53.8%), as
(1) name of the first author,
shown in Figure 2.
(2) year of publication,
(3) sample size,
(4) prevalence rate of learning burnout, 3.3. Analysis of 3 subitems of the incidence of burnout
(5) name of journal, and
(6) questionnaire return ratio. 1) Emotional exhaustion

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1008 articles indentified

through databases
919 records excluded were systemic
review or duplicates or irrelevant
studies or non-English/Chinese writing
language or data not available
89 studies remained for
evaluation
41full-text articles excluded
without enough data

48 papers included for

meta-analysis
Figure 1. A flowchart of study selection.

Table 1
Basic characteristics of the studies in the meta-analysis.
Sample Number of Response Prevalence of Investigation
Study size burnout rate (%) Mean age burnout (%) Specialty table
YC Zhang, 2017 248 113 93.94 20.51 ± 1.71 45.56 Medicine Rong Lian
YM Wei, 2016 304 187 95 22.16 ± 1.5 61.5 Clinical medicine Rong Lian
LJ Yang, 2015 289 205 94.5 NM 70.9 Medicine Yongxin Li
K Li, 2018 586 72 100 NM 12.3 Medicine Rong Lian
Y Liao, 2011 627 627 98.9 NM 52.15 Medicine Rong Lian
K Zhang, 2017 283 119 81 NM 42.05 Clinical medicine Rong Lian
H Liu, 2015 400 158 100 NM 39.5 Medicine Rong Lian
H Wu, 2015 739 739 92.61 NM 45.06 Rural oriented medical students Rong Lian
HC Zhu, 2012 87 62 87 NM 71.1 Medical students(7 yrs) MBI-GS
X Wang, 2018 1211 934 90.24 NM 77.13 Nurse MBI-SS
TP Wang, 2017 600 224 91.88 NM 37.3 Examination and pharmacy Rong Lian
XH Yang, 2015 775 441 96.9 NM 57.35 Medicine Rong Lian
SX Zhang, 2016 771 344 86 NM 44.6 Medicine Rong Lian
PY Su, 2018 944 684 99.16 17–22 72.5 Medicine Rong Lian
L Liu, 2018 619 216 95.2 NM 34.9 Medicine Rong Lian
SJ Yu, 2018 355 355 88.75 NM 78.9 Medicine Rong Lian
L Li, 2018 1368 492 93.25 NM 36 Medicine Rong Lian
JH Zhai, 2014 635 264 90.71 NM 41.65 Medicine Rong Lian
L Li, 2017 600 224 91.88 NM 37.3 Medicine Rong Lian
PY Liang, 2017 634 243 90.1 NM 38.33 Medicine Rong Lian
Y Zhu, 2012 184 69 76.2 20–25 37.5 Medicine Rong Lian
XF Zeng, 2014 523 142 97.39 NM 27.15 Medicine Qizhi Zhang
YZ Li, 2014 260 67 96.3 NM 25.8 Medicine Rong Lian
Y Zhang, 2018 350 178 91.1 17–24 50.8 Nurse Rong Lian
Tian L, 2019 1814 1516 37 NM 83.6 Neurology postgraduates Maslach C
Liu H, 2018 453 42 58.08 20.21 ± 1.46 9.27 Medicine MBI-SS
Zukelatalaiti, 2012 637 153 96.51 NM 45.13 Medicine Rong Lian
DL Yang, 2011 576 210 96 NM 36.46 Medicine Rong Lian
P Xu, 2009 610 241 93.8 17–24 39.5 Medicine Rong Lian
YJ Hui, 2012 1835 1218 95.32 NM 66.4 Nurse Rong Lian
LH Lu, 2018 2431 1134 97.24 NM 46.65 Medicine Rong Lian
L Chen, 2013 443 68 98.44 NM 15.3 Nurse Rong Lian
YY Li, 2017 282 278 88.1 NM 98.6 Nurse Rong Lian
R Sun, 2012 350 120 100 NM 34.4 Nurse Rong Lian
P Hao, 2015 1092 314 96.98 19.34 ± 1.42 28.75 Nurse Rong Lian
YX Li, 2007 90 69 NM NM 76.7 Medicine Yongxin Li
DB Li, 2016 483 216 96.6 NM 44.72 Medicine NM
HJ Ma, 2018 586 72 100 NM 12.3 Medicine Rong Lian
ZP Li, 2013 367 109 93.62 NM 29.7 Medicine Rong Lian
P Hao, 2013 592 179 97.21 NM 30.24 Nurse Rong Lian
(continued )

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Table 1
(continued).
Sample Number of Response Prevalence of Investigation
Study size burnout rate (%) Mean age burnout (%) Specialty table
SX Lv, 2014 927 697 91.2 NM 75.19 Medicine Rong Lian
F Jiang, 2009 309 117 96.56 NM 37.86 Nurse Rong Lian
T Tang, 2019 588 128 90.46 NM 21.77 Medicine Yongxin Li
XF Yu, 2015 290 137 93.55 NM 47.24 Medicine Rong Lian
L Yang, 2014 202 83 84 NM 41.09 Nurse Rong Lian
Y Pan, 2012 170 117 94.4 NM 68.82 Medicine NM
JH Ma, 2014 192 81 96 21.4 ± 0.5 42.19 Nurse Rong Lian
LY Zhou, 2010 309 112 96.56 19–23 36.25 Nurse Rong Lian
NM = not mentioned.

Table 2
Quality assessment of included studies using the Newcastle-Ottawa scale.
Selection Comparability Outcome
Study Same
controls method of
Is the case Selection Definition Study for any ascertainment Non-
definition Representativeness of of controls additional Ascertainment for cases response
Study adequate of the cases controls controls for –——— factor of exposure and controls rate Score
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
(continued )

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Table 2
(continued).
Selection Comparability Outcome
Study Same
controls method of
Is the case Selection Definition Study for any ascertainment Non-
definition Representativeness of of controls additional Ascertainment for cases response
Study adequate of the cases controls controls for –——— factor of exposure and controls rate Score
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

The prevalence rate of high emotional exhaustion was The prevalence rate was 36.0% (95% CI: 23.0%–48.9%)
37.5% (95% CI: 21.4%–53.7%). Figure 3 shows a forest plot in the depersonalization dimension. A forest plot of a high DP
with high EE. is shown in Figure 5.
2) Low personal accomplishment
The percentage was 44% (95% CI: 29.2%–58.8%) for low
personal accomplishment. Figure 4 illustrates the forest plot of 3.4. Total publication bias
low PA. Publication bias was found through the asymmetric funnel plot
3) Depersonalization and the results of the Egger’s test (Fig. 6) (Begg’s score <0.1).

Study | ES [95% Conf. Interval] % Weight Study %

---------------------+--------------------------------------------------- ID ES (95% CI) Weight
YC Zhang(2017) | 0.456 0.394 0.518 2.07
YM Wei(2016) | 0.615 0.560 0.670 2.08 YC Zhang(2017) 0.46 (0.39, 0.52) 2.07
LJ Yang(2015) | 0.709 0.657 0.761 2.08 YM Wei(2016) 0.62 (0.56, 0.67) 2.08
K Li(2018) | 0.123 0.096 0.150 2.09 LJ Yang(2015) 0.71 (0.66, 0.76) 2.08
Y Liao(2011) | 0.521 0.482 0.561 2.09 K Li(2018) 0.12 (0.10, 0.15) 2.09
K Zhang(2017) | 0.421 0.363 0.478 2.08 Y Liao(2011) 0.52 (0.48, 0.56) 2.09
H Liu(2015) | 0.395 0.347 0.443 2.08 K Zhang(2017) 0.42 (0.36, 0.48) 2.08
H Wu(2015) | 0.451 0.415 0.486 2.09 H Liu(2015) 0.40 (0.35, 0.44) 2.08
Hc Zhu(2012) | 0.711 0.616 0.806 2.04 H Wu(2015) 0.45 (0.41, 0.49) 2.09
X Wang(2018) | 0.771 0.748 0.795 2.09 Hc Zhu(2012) 0.71 (0.62, 0.81) 2.04
TP Wang(2017) | 0.373 0.334 0.412 2.09 X Wang(2018) 0.77 (0.75, 0.79) 2.09
XH Wang(2015) | 0.573 0.539 0.608 2.09 TP Wang(2017) 0.37 (0.33, 0.41) 2.09
SX Zhang(2016) | 0.446 0.411 0.481 2.09 XH Wang(2015) 0.57 (0.54, 0.61) 2.09
Py Su(2018) | 0.725 0.697 0.753 2.09 SX Zhang(2016) 0.45 (0.41, 0.48) 2.09
L Liu(2018) | 0.349 0.311 0.387 2.09
Py Su(2018) 0.73 (0.70, 0.75) 2.09
SJ Yu(2018) | 0.789 0.747 0.831 2.09
L Liu(2018) 0.35 (0.31, 0.39) 2.09
L Li(2018) | 0.360 0.335 0.385 2.09
SJ Yu(2018) 0.79 (0.75, 0.83) 2.09
JH Zhai(2014) | 0.417 0.378 0.455 2.09
L Li(2018) 0.36 (0.33, 0.39) 2.09
L Li(2017) | 0.373 0.334 0.412 2.09
JH Zhai(2014) 0.42 (0.38, 0.45) 2.09
PY Liang(2017) | 0.383 0.345 0.421 2.09
L Li(2017) 0.37 (0.33, 0.41) 2.09
Y Zhu(2012) | 0.375 0.305 0.445 2.06
PY Liang(2017) 0.38 (0.35, 0.42) 2.09
XF Zeng(2014) | 0.271 0.233 0.310 2.09
YZ Li(2014) | 0.258 0.205 0.311 2.08 Y Zhu(2012) 0.38 (0.31, 0.44) 2.06

Y Zhang(2018) | 0.508 0.456 0.560 2.08 XF Zeng(2014) 0.27 (0.23, 0.31) 2.09

Tian L(2019) | 0.836 0.819 0.853 2.10 YZ Li(2014) 0.26 (0.20, 0.31) 2.08

Liu H(2018) | 0.093 0.066 0.119 2.09 Y Zhang(2018) 0.51 (0.46, 0.56) 2.08

Zukelatalaiti(2012) | 0.451 0.412 0.490 2.09 Tian L(2019) 0.84 (0.82, 0.85) 2.10

DL Yang(2011) | 0.365 0.325 0.404 2.09 Liu H(2018) 0.09 (0.07, 0.12) 2.09

P Xu(2009) | 0.395 0.356 0.434 2.09 Zukelatalaiti(2012) 0.45 (0.41, 0.49) 2.09
YJ Hui(2012) | 0.664 0.642 0.686 2.10 DL Yang(2011) 0.36 (0.33, 0.40) 2.09
LH Lu(2018) | 0.467 0.447 0.486 2.10 P Xu(2009) 0.40 (0.36, 0.43) 2.09
L Chen(2013) | 0.153 0.119 0.187 2.09 YJ Hui(2012) 0.66 (0.64, 0.69) 2.10
YY Li(2017) | 0.986 0.972 1.000 2.10 LH Lu(2018) 0.47 (0.45, 0.49) 2.10
R Sun(2012) | 0.344 0.294 0.394 2.08 L Chen(2013) 0.15 (0.12, 0.19) 2.09
P Hao(2015) | 0.287 0.261 0.314 2.09 YY Li(2017) 0.99 (0.97, 1.00) 2.10
YX Li(2007) | 0.767 0.680 0.854 2.05 R Sun(2012) 0.34 (0.29, 0.39) 2.08
DB Li(2016) | 0.447 0.403 0.492 2.08 P Hao(2015) 0.29 (0.26, 0.31) 2.09
HJ Ma(2018) | 0.123 0.096 0.150 2.09 YX Li(2007) 0.77 (0.68, 0.85) 2.05
ZP Li(2013) | 0.297 0.250 0.344 2.08 DB Li(2016) 0.45 (0.40, 0.49) 2.08
P Hao(2013) | 0.302 0.265 0.339 2.09 HJ Ma(2018) 0.12 (0.10, 0.15) 2.09
SX Lv(2014) | 0.752 0.724 0.780 2.09 ZP Li(2013) 0.30 (0.25, 0.34) 2.08
F Jiang(2009) | 0.379 0.325 0.433 2.08 P Hao(2013) 0.30 (0.27, 0.34) 2.09
T Tang(2019) | 0.218 0.184 0.251 2.09 SX Lv(2014) 0.75 (0.72, 0.78) 2.09
XF Yu(2015) | 0.472 0.415 0.530 2.08 F Jiang(2009) 0.38 (0.32, 0.43) 2.08
L Yang(2014) | 0.411 0.343 0.479 2.07 T Tang(2019) 0.22 (0.18, 0.25) 2.09
Y Pan(2012) | 0.688 0.619 0.758 2.06 XF Yu(2015) 0.47 (0.41, 0.53) 2.08
JH Ma(2014) | 0.422 0.352 0.492 2.06 L Yang(2014) 0.41 (0.34, 0.48) 2.07
LY Zhou(2010) | 0.363 0.309 0.416 2.08
Y Pan(2012) 0.69 (0.62, 0.76) 2.06
---------------------+---------------------------------------------------
JH Ma(2014) 0.42 (0.35, 0.49) 2.06
D+L pooled ES | 0.459 0.381 0.538 100.00
LY Zhou(2010) 0.36 (0.31, 0.42) 2.08
---------------------+---------------------------------------------------
Overall (I-squared = 99.6%, p = 0.000) 0.46 (0.38, 0.54) 100.00

Heterogeneity chi-squared = 12126.19 (d.f. = 47) p = 0.000 NOTE: Weights are from random effects analysis
I-squared (variation in ES attributable to heterogeneity) = 99.6%
Estimate of between-study variance Tau-squared = 0.0764 -1 0 1

Test of ES=0 : z= 11.47 p = 0.000

Figure 2. The aggregate prevalence of burnout in all residents.

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Study | ES [95% Conf. Interval] % Weight

---------------------+---------------------------------------------------
YM Wei(2016) | 0.421 0.366 0.476 8.29
K Li(2018) | 0.247 0.212 0.282 8.34
Y Liao(2011) | 0.549 0.510 0.588 8.33
H Wu(2015) | 0.349 0.315 0.384 8.34
Hc Zhu(2012) | 0.069 0.016 0.122 8.30
X Wang(2018) | 0.765 0.741 0.789 8.36
SX Zhang(2016) | 0.565 0.530 0.600 8.34
LH Lu(2018) | 0.509 0.489 0.529 8.36
L Chen(2013) | 0.025 0.010 0.040 8.37
R Sun(2012) | 0.311 0.263 0.359 8.31
HJ Ma(2018) | 0.247 0.212 0.282 8.34
F Jiang(2009) | 0.443 0.388 0.499 8.29
---------------------+---------------------------------------------------
D+L pooled ES | 0.375 0.214 0.537 100.00
---------------------+---------------------------------------------------

Heterogeneity chi-squared = 3732.76 (d.f. = 11) p = 0.000

I-squared (variation in ES attributable to heterogeneity) = 99.7%
Estimate of between-study variance Tau-squared = 0.0812

Test of ES=0 : z= 4.55 p = 0.000

Study %

ID ES (95% CI) Weight

YM Wei(2016) 0.42 (0.37, 0.48) 8.29

K Li(2018) 0.25 (0.21, 0.28) 8.34

Y Liao(2011) 0.55 (0.51, 0.59) 8.33

H Wu(2015) 0.35 (0.31, 0.38) 8.34

Hc Zhu(2012) 0.07 (0.02, 0.12) 8.30

X Wang(2018) 0.76 (0.74, 0.79) 8.36

SX Zhang(2016) 0.56 (0.53, 0.60) 8.34

LH Lu(2018) 0.51 (0.49, 0.53) 8.36

L Chen(2013) 0.03 (0.01, 0.04) 8.37

R Sun(2012) 0.31 (0.26, 0.36) 8.31

HJ Ma(2018) 0.25 (0.21, 0.28) 8.34

F Jiang(2009) 0.44 (0.39, 0.50) 8.29

Overall (I-squared = 99.7%, p = 0.000) 0.38 (0.21, 0.54) 100.00

NOTE: Weights are from random effects analysis

-.789 0 .789

Figure 3. The aggregate prevalence of emotional exhaustion.

3.5. The result of trim and filling significantly, indicating that the effect of publication bias was not
The following figure shows the funnel plot obtained after the significant and the results were relatively stable (Fig. 8).
addition of the 11 studies. The “squares” in the figure are
additional studies. The funnel plot obtained after the addition of 3.7. Subgroup analysis
11 studies showed no obvious asymmetry, indicating no
Factors that may lead to heterogeneity were analyzed, such as
publication bias (Fig. 7).
gender, specialty, and the scale of burnout by subgroup. The
results showed high heterogeneity; hence, the random-effects
3.6. The results of combined effect before trim and filling model was adopted to combine the effect size.
The results of fixed- and random-effects were all statistically In the subgroup analysis by specialty, the prevalence of
different (P = .0000) in the values before and after trim and filling. burnout was 30.3% (95% CI: 28.6%–32.0%) for clinical
The estimated values of the combined effect did not change medicine and 43.8% (95% CI: 41.8%–45.8%) for other medical

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Li et al. Medicine (2021) 100:26 www.md-journal.com

Study | ES [95% Conf. Interval] % Weight

---------------------+---------------------------------------------------
YM Wei(2016) | 0.135 0.097 0.173 8.33
K Li(2018) | 0.184 0.153 0.215 8.35
Y Liao(2011) | 0.640 0.602 0.677 8.33
H Wu(2015) | 0.444 0.408 0.480 8.34
Hc Zhu(2012) | 0.966 0.928 1.004 8.33
X Wang(2018) | 0.794 0.772 0.817 8.36
SX Zhang(2016) | 0.446 0.411 0.481 8.34
LH Lu(2018) | 0.330 0.312 0.349 8.37
L Chen(2013) | 0.325 0.281 0.369 8.32
R Sun(2012) | 0.406 0.355 0.457 8.30
HJ Ma(2018) | 0.184 0.153 0.215 8.35
F Jiang(2009) | 0.424 0.369 0.479 8.28
---------------------+---------------------------------------------------
D+L pooled ES | 0.440 0.292 0.588 100.00
---------------------+---------------------------------------------------

Heterogeneity chi-squared = 2685.44 (d.f. = 11) p = 0.000

I-squared (variation in ES attributable to heterogeneity) = 99.6%
Estimate of between-study variance Tau-squared = 0.0679

Test of ES=0 : z= 5.83 p = 0.000

Study %

ID ES (95% CI) Weight

YM Wei(2016) 0.14 (0.10, 0.17) 8.33

K Li(2018) 0.18 (0.15, 0.22) 8.35

Y Liao(2011) 0.64 (0.60, 0.68) 8.33

H Wu(2015) 0.44 (0.41, 0.48) 8.34

Hc Zhu(2012) 0.97 (0.93, 1.00) 8.33

X Wang(2018) 0.79 (0.77, 0.82) 8.36

SX Zhang(2016) 0.45 (0.41, 0.48) 8.34

LH Lu(2018) 0.33 (0.31, 0.35) 8.37

L Chen(2013) 0.32 (0.28, 0.37) 8.32

R Sun(2012) 0.41 (0.35, 0.46) 8.30

HJ Ma(2018) 0.18 (0.15, 0.22) 8.35

F Jiang(2009) 0.42 (0.37, 0.48) 8.28

Overall (I-squared = 99.6%, p = 0.000) 0.44 (0.29, 0.59) 100.00

NOTE: Weights are from random effects analysis

-1 0 1

Figure 4. The aggregate prevalence of low personal accomplishment.

specialties. There was a statistically significant difference in the 38.1%–53.8%) of Chinese medical students reported burnout
prevalence rate between different specialties. In the subgroup syndrome. The results showed that low personal accomplishment
analysis by gender, the prevalence of burnout was 46.4% (95% was the most widespread dimension affecting medical students’
CI: 44.8%–47.9%) for males and 46.6% (95% CI: 45.5%– learning burnout accounting for 44% of the sample. This was
47.6%) for females. The difference in the prevalence rate between followed by high emotional exhaustion, which occurred in
men and women was not statistically significant (P = .093). In the 37.5% of the medical students in our meta-analysis. The lowest
subgroup analysis by selecting the scale, the prevalence of prevalence was depersonalization, which affected 36% of
burnout was 43.7% (42.1%–45.2%) with the scale conducted by medical students. These mean that students showed high levels
Rong Lian, and the prevalence of burnout was 51.4% (50.4%– of emotional exhaustion, low personal accomplishment, and high
52.4%) with the other scale. The difference in prevalence rates depersonalization. The burnout prevalence among medical
with different scales was statistically significant (P = .000). The students is around 44% in the worldwide according to the
prevalence rates were 62.9% (61.3%–64.6%), 58.7% (56.3%– findings of Frajerman et al.[64] The prevalence of learning
61.1%), 46.5% (42.9%–50.2%), and 56.0% (51.6%–60.4%) burnout among Chinese medical students is on par with the
from Grades 1 to 4, respectively. Statistical significance was worldwide burnout prevalence. The prevalence and trend of
observed among the different grades (P = .000) (Table 3). burnout in personal accomplishment, emotional exhaustion, and
depersonalization were similar to Kansoun Ziad’s study of
4. Discussion
French physicians.[65] The prevalence of burnout was higher than
The results of our meta-analysis, which included 48 articles and that of medical students (35% in Germany),[66] 40.4% for
29,020 subjects, can be summarized as follows: 45.9% (95% CI: medical students in 2016 (in Spanish),[67] in Australia (6%),[68]

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Li et al. Medicine (2021) 100:26 Medicine

Study | ES [95% Conf. Interval] % Weight

---------------------+---------------------------------------------------
YM Wei(2016) | 0.059 0.033 0.085 8.39
K Li(2018) | 0.084 0.062 0.106 8.40
Y Liao(2011) | 0.405 0.367 0.444 8.36
H Wu(2015) | 0.617 0.581 0.652 8.37
Hc Zhu(2012) | 0.529 0.424 0.634 7.98
X Wang(2018) | 0.323 0.297 0.349 8.39
SX Zhang(2016) | 0.441 0.406 0.476 8.37
LH Lu(2018) | 0.580 0.560 0.600 8.40
L Chen(2013) | 0.325 0.281 0.369 8.34
R Sun(2012) | 0.482 0.430 0.534 8.31
HJ Ma(2018) | 0.084 0.062 0.106 8.40
F Jiang(2009) | 0.401 0.347 0.456 8.30
---------------------+---------------------------------------------------
D+L pooled ES | 0.360 0.230 0.489 100.00
---------------------+---------------------------------------------------

Heterogeneity chi-squared = 2291.59 (d.f. = 11) p = 0.000

I-squared (variation in ES attributable to heterogeneity) = 99.5%
Estimate of between-study variance Tau-squared = 0.0519

Test of ES=0 : z= 5.44 p = 0.000

Study %

ID ES (95% CI) Weight

YM Wei(2016) 0.06 (0.03, 0.09) 8.39

K Li(2018) 0.08 (0.06, 0.11) 8.40

Y Liao(2011) 0.41 (0.37, 0.44) 8.36

H Wu(2015) 0.62 (0.58, 0.65) 8.37

Hc Zhu(2012) 0.53 (0.42, 0.63) 7.98

X Wang(2018) 0.32 (0.30, 0.35) 8.39

SX Zhang(2016) 0.44 (0.41, 0.48) 8.37

LH Lu(2018) 0.58 (0.56, 0.60) 8.40

L Chen(2013) 0.32 (0.28, 0.37) 8.34

R Sun(2012) 0.48 (0.43, 0.53) 8.31

HJ Ma(2018) 0.08 (0.06, 0.11) 8.40

F Jiang(2009) 0.40 (0.35, 0.46) 8.30

Overall (I-squared = 99.5%, p = 0.000) 0.36 (0.23, 0.49) 100.00

NOTE: Weights are from random effects analysis

-.652 0 .652

Figure 5. The aggregate prevalence of depersonalization.

and in Brazil (26.4%).[69] It was lower than dental students conclusions of at least 15% to 21% of the meta-analysis. The
(50.3%),[67] medical students (55%, 56% in the US),[70,71] and main conclusions were obtained by correcting for potential
(52%) in Trinidad and Tobago.[72] The rate of learning burnout publication bias using the trim and fill method.[76] Thus, the trim
is similar to that of foreign medical students. Concurrently, there and fill method was chosen to reanalyze the publication bias and
are also higher and lower rates. These differences may be related found that the estimated value of the combined effect size did not
to differences in the educational system between domestic and change significantly, indicating that publication bias had little
foreign medical students. The reason for this is that differences effect and the result was relatively stable.
exist in the curriculum and the essential requirements of medical In the subgroup analysis, male participants reported lower
students in various countries. For example, some medical schools levels of burnout than female participants, which is consistent
require a preliminary bachelor’s degree.[73] However, some with many beliefs that burnout is more commonly experienced by
medical staff begin their studies without any preliminary higher female employees. However, further studies are needed to
education.[74,75] Concurrently, medical students have greater elucidate the relationship between gender and burnout among
study pressure than do other professional college students. medical students. The prevalence of burnout was 30.3%
The asymmetric funnel plot and the results of Egger’s test in our (28.6%–32.0%) and 43.8% (41.8%–45.8%) for clinical medi-
meta-analysis showed that publication bias was present. The cine and other medical specialties, respectively, in the subgroup
research found that publication bias may affect the main analysis. The prevalence was lower for clinical medicine than for

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Li et al. Medicine (2021) 100:26 www.md-journal.com

Begg's Test

adj. Kendall's Score (P-Q) = -312

Std. Dev. of Score = 112.51 (corrected for ties)
Number of Studies = 48
z = -2.77
Pr > |z| = 0.006
z = 2.76 (continuity corrected)
Pr > |z| = 0.006 (continuity corrected)

Egger's test

Std_Eff Coef. Std. Err. t P>|t| [95% Conf. Interval]

slope .0146262 .0365596 0.40 0.691 -.0589645 .0882169

bias -16.08793 1.277418 -12.59 0.000 -18.65924 -13.51662

Begg's funnel plot with pseudo 95% confidence limits

-1
log[r]

-2

-3
0 .05 .1 .15
s.e. of: log[r]
Figure 6. The asymmetric funnel plot of publication bias.

other medical specialties. This trend was consistent with other and high values, that is, motivation for the first-year students
studies conducted by Montiel-Company José María[70] and before a high workload (e.g., information to be learned)
Montiel-Company.[67] In the subgroup analysis, the prevalence coming.[80]
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
5. Limitations
selected Rong-Lian scale. Based on the burnout scale by Marlach,
Rong Lian compiled a burnout scale suitable for Chinese college This meta-analysis has several limitations. First, high heteroge-
students according to their characteristics. The prevalence of neity existed in the subgroup analysis of all influencing factors.
burnout was different, partly due to the different scales. The Second, certain specialties in this meta-analysis were underrep-
investigators mainly chose the Maslach Burnout Inventory (MBI- resented. The distribution of the number of residents per specialty
SS) to study the burnout of college students in foreign is uneven. Many references were included for the selected scale,
papers.[77,78] Our results showed that the burnout rate was the and few were included for the major and gender, which had some
highest at 62.9% (61.3%–64.6%) in freshman year and the influence on the results of the subgroup analysis. Third,
lowest at 46.5% (42.9%–50.2%) in junior year. Ultimately, a publication bias was present because unpublished literature or
statistically significant difference was observed. This is similar to data were not collected. Therefore, subgroup analysis based on
Altannir Youssef’s results that the first-year medical students continents should be interpreted with caution.
have higher levels of burnout compared with other year medical
students.[79] It may be concerned with the freshmen merely 6. Conclusions
entering the campus and not adapting well to the environment.
The results of Thun-Hohenstein et al showed that the first-year Our findings suggest a high prevalence of burnout among medical
medical students have lower levels of burnout compared with students. Society, universities, and families should take appro-
other year medical students. This is the opposite of what we priate measures and allot more care to prevent burnout among
found. The cause may be related with feeling for good fairness medical students.

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Li et al. Medicine (2021) 100:26 Medicine

Meta-analysis

| Pooled 95% CI Asymptotic No. of

Method | Est Lower Upper z_value p_value studies
-------+----------------------------------------------------
Fixed | -0.323 -0.331 -0.315 -78.253 0.000 48
Random | -0.876 -0.987 -0.766 -15.532 0.000

Test for heterogeneity: Q= 7396.045 on 47 degrees of freedom (p= 0.000)

Moment-based estimate of between studies variance = 0.149

Trimming estimator: Linear

Meta-analysis type: Random-effects model

iteration | estimate Tn # to trim diff

----------+--------------------------------------
1 | -0.876 613 1 1176
2 | -0.893 645 2 64
3 | -0.909 673 4 56
4 | -0.940 725 6 104
5 | -0.971 763 7 76
6 | -0.987 786 8 46
7 | -1.003 811 9 50
8 | -1.020 833 10 44
9 | -1.037 850 11 34
10 | -1.051 861 11 22
11 | -1.051 861 11 0

Warning: iterative algorithm did not converge

Filled
Meta-analysis (exponential form)

| Pooled 95% CI Asymptotic No. of

Method | Est Lower Upper z_value p_value studies
-------+----------------------------------------------------
Fixed | 0.357 0.355 0.360 -331.156 0.000 59
Random | 0.346 0.275 0.435 -9.051 0.000

Test for heterogeneity: Q= 7.5e+04 on 58 degrees of freedom (p= 0.000)

Moment-based estimate of between studies variance = 0.807

Filled funnel plot with pseudo 95% confidence limits

-1
theta, filled

-2

-3
0 .05 .1 .15
s.e. of: theta, filled
Figure 7. The asymmetric funnel plot of publication bias after trim and filling.

Table 3
Prevalence of burnout in residents by subgroup analysis.
Parameter Document number Sample size (n) Burnout prevalence (%) and 95% CI I2 (%) P Pz
Gender
Male 11 2443 46.4% (44.8–47.9) 99.0 .000 0.093
Female 11 5016 46.6% (45.5–47.6) 99.6 .000
Specialty
Clinical medicine 5 1659 30.3% (28.6–32.0) 99.3 .000 0.000
Other medicine 5 1343 43.8% (41.8–45.8) 99.4 .000
Scale
Rong Lian 38 23,312 43.7% (42.1–45.2) 99.6 .000 0.000
Other scale 10 5708 51.4% (50.4–52.4) 99.7 .000
Grade
1 8 2716 62.9% (61.3–64.6) 98.9 .000 0.000
2 6 1322 58.7% (56.3–61.1) 98.3 .000
3 4 555 46.5% (42.9–50.2) 98.3 .000
4 3 380 56.0% (51.6–60.4) 98.2 .000

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

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Li et al. Medicine (2021) 100:26 www.md-journal.com

[8] Shanafelt TD, Hasan O, Dyrbye LN, et al. Changes in burnout and
Meta-analysis
satisfaction with work-life balance in physicians and the general US
| Pooled 95% CI Asymptotic No. of
Method | Est Lower Upper z_value p_value studies working population between 2011 and 2014. Mayo Clin Proc 2015;
-------+----------------------------------------------------
Fixed | -0.323 -0.331 -0.315 -78.253 0.000 48 90:1600–13.
Random | -0.876 -0.987 -0.766 -15.532 0.000
[9] Ishak W, Nikravesh R, Lederer S, Perry R, Ogunyemi D, Bernstein C.
Test for heterogeneity: Q= 7396.045 on 47 degrees of freedom (p= 0.000)
Moment-based estimate of between studies variance = 0.149 Burnout in medical students: a systematic review. Clin Teach 2013;
Trimming estimator: Linear 10:242–5.
Meta-analysis type: Random-effects model
[10] Fares J, Al Tabosh H, Saadeddin Z, El Mouhayyar C, Aridi H. Stress,
iteration | estimate Tn # to trim diff
----------+--------------------------------------
1 | -0.876 613 1 1176
burnout and coping strategies in preclinical medical students. North Am
2
3
|
|
-0.893
-0.909
645
673
2
4
64
56
J Med Sci 2016;8:75–81.
4
5
|
|
-0.940
-0.971
725
763
6
7
104
76
[11] WHO. Global Health Observatory Data Repository. Available at: http://
6
7
|
|
-0.987
-1.003
786
811
8
9
46
50
www.who. int/gho/database/en/2016.
8
9
|
|
-1.020
-1.037
833
850
10
11
44
34
[12] Stang A. Critical evaluation of the Newcastle-Ottawa scale for the
10
11
|
|
-1.051
-1.051
861
861
11
11
22
0
assessment of the quality of nonrandomized studies in meta-analyses. Eur
Warning: iterative algorithm did not converge J Epidemiol 2010;25:603–5.
Filled [13] Quek YH, Tam WWS, Zhang MWB, Ho RCM. Exploring the
Meta-analysis (exponential form)
association between childhood and adolescent obesity and depression:
| Pooled 95% CI Asymptotic No. of
Method | Est Lower Upper z_value p_value studies a meta-analysis. Obes Rev 2017;18:742–54.
-------+----------------------------------------------------
Fixed | 0.357 0.355 0.360 -331.156 0.000 59 [14] Tung YJ, Lo KKH, Ho RCM, Tam WSW. Prevalence of depression
Random | 0.346 0.275 0.435 -9.051 0.000
among nursing students: a systematic review and meta-analysis. Nurse
Test for heterogeneity: Q= 7.5e+04 on 58 degrees of freedom (p= 0.000)
Moment-based estimate of between studies variance = 0.807 Educ Today 2018;63:119–29.
. meta r _LCI _UCI, ci eform [15] Puthran R, Zhang MW, Tam WW, Ho RC. Prevalence of depression
Meta-analysis (exponential form) amongst medical students: a meta-analysis. Med Educ 2016;50:456–68.
| Pooled 95% CI Asymptotic No. of [16] Zhang YC, Mi SB, Zhang FY, Liu YR, Yang JQ. Analysis of current
Method | Est Lower Upper z_value p_value studies
-------+----------------------------------------------------
Fixed | 0.724 0.718 0.730 -78.253 0.000 48
situation and influencing factors of medical students’ learning burnout
Random | 0.416 0.373 0.465 -15.532 0.000 among the post-90s only child (in Chinese). Soft Sci Health 2017;31:43–6.
Test for heterogeneity: Q= 7396.045 on 47 degrees of freedom (p= 0.000)
Moment-based estimate of between studies variance = 0.149
[17] Wei YM, Mo HP, Huang LR, et al. Study on the characteristics of
learning burnout of eight year medical students (in Chinese). Chin J Med
Figure 8. The results of combined effect before trim and filling. Edu 2016;36:381–4. 361.
[18] Yang LJ, Peng QL, Zhou XN. Analysis of job burnout situation in TCM
medical interns from perspective of emotion regulation self-efficacy (in
Chinese). J Changchun Univ Chin Med 2015;31:1288–90.
Acknowledgments [19] Li K, Tang L. Survey on study burnout of college students (in Chinese).
Health Vocat Educ 2018;36:121–2.
We would like to thank all the authors. [20] Liao Y, Liu JY, Wu HF, et al. A preliminary study on the study burnout of
medical students in advanced vocational colleges (in Chinese). Chongq-
ing Med 2011;40:924–6.
Author contributions [21] Zhang K, Wu CX, Zhang XH. The reflection and countermeasure of
burnout of clinical medicine students in basic medical education stage (in
Conceptualization: You Li, Zhiyong Zhang.
Chinese). China Higher Med Educ 2017;23–4.
Data curation: Liang Cao, Chunbao Mo, Dechan Tan, Tingyu [22] Liu H, Jiang L, Liu HB. Investigation and analysis of study burnout of
Mai. senior students on the current situation in a private medical college (in
Formal analysis: Liang Cao. Chinese). Sci Technol 2015;25:295.
Investigation: Liang Cao. [23] Wu H, Li Q, Zhang HX, Wu WD. Study on the relationship between
time management tendency and learning burnout of rural order-oriented
Methodology: Chunbao Mo. undergraduate medical students (in Chinese). Chongqing Med
Supervision: Dechan Tan. 2015;44:4449–51.
Validation: Tingyu Mai. [24] Zhu HC, Tan S, Li QQ, Jiang CY, Wang LJ, Sun SL. Study on the
Visualization: Liang Cao. relationship between job burnout and mental health problems of seven-
year medical students during their internship (in Chinese). Chin J Pract
Writing – original draft: You Li.
Nerv Dis 2012;15:64–5.
Writing – review & editing: Zhiyong Zhang. [25] Wang X, Chen H, Liu YQ, Zhang H. The effects of extracurricular
activities on the stress and learning burnout of nursing students before
internship polytechnic (in Chinese). J Youjiang Med Univ Nationalities
References
2018;40:502–5. 508.
[1] Maslach C, Jackson SE. The measurement of experienced burnout. J [26] Wang TP. Investigation on the study burnout of five-year medical
Occup Behav 1981;2:99–113. students in advanced vocational colleges (in Chinese). Mingri 2017;
[2] Maslach C. Burnout—The Cost of Caring. Englewood Cliffs, NJ: 10–1.
Prentice-Hall; 1982. [27] Yang XH, Dong ZJ. Study on the relationship between learning burnout
[3] Maslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol and social support of student in a medical college under the condition of
2001;52:397–422. modern medical model (in Chinese). China Higher Med Educ
[4] Schaufeli WB, Martínez IM, Pinto AM, et al. Burnout and engagement in 2015;8:48–9.
university students. J Cross Cult Psychol 2002;33:464–81. [28] Zhang SX, Yang XY, Zhang Y, Ning L. Correlation analysis on sleep
[5] Hederich-Martínez C, Caballero-Domínguez CC. Validation of Maslach quality and learning burnout among students in a medical college in
Burnout Inventory-Student Survey (MBI-SS) in Colombian academic Xinjiang (in Chinese). China Occup Med 2016;43:181–4.
context. Revista CES Psicol 2016;9:1–15. [29] Su PY, Yu WW, Zhao M, Fang GX, Chen DJ. Research on
[6] Xie YJ, Cao P, Sun T, Yang LB. The effects of academic adaptability on the relationship between the professional- attitude stability and
academic burnout, immersion in learning, and academic performance learning burnout in first-year medical students (in Chinese). J Chengdu
among Chinese medical students: a cross-sectional study. BMC Med Univ Tradit Chin Med (Educational Science Edition) 2018;20:
Educ 2019;19:211. 97–101.
[7] Rotenstein Lisa S, Ramos Marco A, Torre Matthew , et al. Prevalence of [30] Liu L, Chen JP, Lu H, Wang JM, Liu SJ. The relationship between
depression, depressive symptoms, and suicidal ideation among medical positive psychological capital and learning burnout among medical
students: a systematic review and meta-analysis. JAMA 2016;316: university students (in Chinese). Acta Universitatis Medicinalis Nanjing
2214–36. (Social Sciences) 2018;1:39–42.

11
Li et al. Medicine (2021) 100:26 Medicine

[31] Yu SJ, Ling CG, Zhu H. Study on the relationship between study [57] Jiang F, Wu YP, Zhang QH. Learning burnout in nursing students and its
burnout, time management tendency and professional commitment of relationship with academic self-efficacy. J Nurs Sci 2009;24:56–8.
medical students (in Chinese). Educ Modern 2018;19:328–30. [58] Tang T, Li S, Wang YJ, Huang DY. Learning burnout and its influencing
[32] Li L, Song HT, Wu T, Xu HS. A brief survey of medical students’ learning factors of medical students in Jishou University (in Chinese). J Commun
burnout, psychological capital and coping style (in Chinese). J Jinzhou Med 2019;17:328–31.
Med Univ (Social Science Edition) 2018;16:67–70. [59] Yu XF. Investigation on the study of medical college students – taking the
[33] Zhai JH, Yang HX, Song AQ, Guo LY, Liu X, Zhang Y. Study on investigation of a medical college as an example (in Chinese). China
learning burnout of medical college students and its influential factors (in Train 2015;12:179.
Chinese). China J Health Psychol 2014;22:1255–7. [60] Yang L, Luo SR, Su AH. Investigation on the current situation and
[34] Li L, Wang XY, Wang LJ. Investigation of the learning burnout in influencing factors of learning burnout among undergraduate nursing
medical students (in Chinese). J Bengbu Med Coll 2017;42:367–9. 373. students (in Chinese). China Higher Med Educ 2014;5–6.
[35] Liang PY, Yin XR, Han JM, Cen ZD, Li B, Liu S. The study of [61] Pan Y. A preliminary study on students’ learning burnout in a vocational
current situation, factors and correlation of learning burnout and college (in Chinese). Contemp Vocat Educ 2012;76–9.
anxiety in medical students (in Chinese). Henan J Prev Med 2017; [62] Ma JH, Zhang ZZ, Yang RF, Feng HL, Wang H. To explore learning
28:401–4. 422. burnout and professional commitment of nursing students studying at
[36] Zhu Y, Bao C, Zhang ZH. Study on medical students’ learning burnout the traditional Chinese medicine college (in Chinese). Chin J Prac Nurs
and learning achievements and their relationship (in Chinese). Chin J 2014;30:31–3.
Med Educ 2012;32:536–8. [63] Zhou LY, Jiang F. The impact of professional identity and academic self-
[37] Zeng XF, Wang XC. A research on medical college students’ English efficacy on nursing students’ learning burnout (in Chinese). J Nurs Sci
learning burnout (in Chinese). Sci Soc Psychol 2014;29:129–35. 2010;25:69–71.
[38] Li YZ, Wu ML. Research on conditions of medical undergraduates’ [64] Frajerman A, Morvan Y, Krebs MO, Gorwood P, Chaumette B. Burnout
learning burnout and the influence factor in University of Medicine in medical students before residency: a systematic review and meta-
College (in Chinese). J Chengdu Univ Tradit Chin Med (Educational analysis. Eur Psychiatry 2019;55:36–42.
Science Edition) 2014;16:65–9. [65] Kansoun Z, Boyer L, Hodgkinson M, Villes V, Lançon C, Fond B.
[39] Zhang Y, Wang JL, Ye J, Shuai HQ. Study on the status quo and Burnout in French physicians: a systematic review and meta-analysis. J
influencing factors of learning burnout of undergraduate nursing Affect Disord 2019;246:132–47.
students in traditional Chinese medicine (in Chinese). Chin Gen Pract [66] Erschens R, Loda T, Herrmann-Werner A, et al. Behaviour-based
Nurs 2018;16:392–5. functional and dysfunctional strategies of medical students to cope with
[40] Tian L, Pu J, Liu Y, et al. Relationship between burnout and career choice burnout. Med Educ Online 2018;23:1535738.
regret among Chinese neurology postgraduates. BMC Med Educ [67] Montiel-Company JM, Subirats-Roig C, Flores-Martí P, Bellot-Arcís C,
2019;19:162. Almerich-Silla JM. Validation of the Maslach Burnout Inventory-Human
[41] Liu H, Yansane AI, Zhang Y, Fu H, Hong N, Kalenderian E. Burnout Services Survey for estimating burnout in dental students. J Dent Educ
and study engagement among medical students at Sun Yat-sen 2016;80:1368–75.
University, China: a cross-sectional study. Medicine (Baltimore) 2018; [68] Boni RADS, Paiva CE, de Oliveira MA, et al. Burnout among medical
97:e0326. students during the first years of undergraduate school: prevalence and
[42] Zukela T. Free medical students’ learning burnout and influencing associated factors. PLoS One 2018;13:e0191746.
factors (in Chinese). Xinjiang Med Univ 2012. [69] Soh N, Ma C, Lampe L, et al. Depression, financial problems and other
[43] Yang DL. The study on group intervention in learning burnout of reasons for suspending medical studies, and requested support services:
medical students (in Chinese). Shanxi Med Univ 2011. findings from a qualitative study. Australas Psychiatry 2012;20:
[44] Xu P. A Study on medical students’ learning burnout and its influential 518–23.
factors (in Chinese). Nanjing Med Univ 2009. [70] Chang E, Eddins-Folensbee F, Coverdale J. Survey of the prevalence of
[45] Hui YJ. The study on learning burnout present status and determinants burnout, stress, depression, and the use of supports by medical students
among nursing students from a three-year medical college (in Chinese). at one school. Acad Psychiatry 2012;36:177–82.
Shandong Univ 2012. [71] Dyrbye Liselotte N, West Colin P, Satele Daniel , et al. Burnout among U.
[46] Lu LH. Study on the current situation and countermeasures of S. medical students, residents, and early career physicians relative to the
undergraduate study burnout in Guangxi medical university (in general U.S population. Acad Med: journal of the Association of
Chinese). Guangxi Med Univ 2018. American Medical Colleges 2014;89:443–51.
[47] Chen L. The study on the relationship between learning self-efficacy and [72] Youssef FF. Medical student stress, burnout and depression in Trinidad
learning burnout of college student nurses (in Chinese). Hebei Normal and Tobago. Acad Psychiatry 2016;40:69–75.
Univ 2013. [73] Dyrbye LN, Thomas MR, Huntington JL, et al. Personal life events and
[48] Li YY, Jiang RR, Zhang DP, Zhao YX, Liu TT. Analysis on the study medical student burnout: a multicenter study. Acad Med 2006;81:
burnout status and influencing factors of 282 undergraduate nursing 374–84.
students (in Chinese). J Nurs (China) 2017;24:57–60. [74] Dahlin M, Joneborg N, Runeson B. Performance-based self-esteem and
[49] Sun R, Li LP, Gao J. Study on relationship between psychological capital burnout in a cross-sectional study of medical students. Med Teach
and learning burnout of nursing undergraduates (in Chinese). Chin Nurs 2007;29:43–8.
Res 2012;26:3005–6. [75] Galán F, Sanmartín A, Polo J, Giner L. Burnout risk in medical students
[50] Hao P, Siyiti MHDS, Liu YB. Correlation study on academic self- in Spain using the Maslach Burnout Inventory-Student survey. Int Arch
efficacy, social support and learning burnout for undergraduate nursing Occup Environ Health 2011;84:453–9.
student (in Chinese). J Nurs Rehabil 2015;14:217–20. [76] Jennions MD, Møller AP. Publication bias in ecology and evolution: an
[51] Li YX, Tan YM. A primary study on learning burnout of college students empirical assessment using the ‘trim and fill’ method. Biol Rev Camb
(in Chinese). China J Health Psychol 2007;15:730–2. Philos Soc 2002;77:211–22.
[52] Li DB, Yuan C. Research on status and cause of learning burnout of [77] Al-Alawi M, Al-Sinawi H, Al-Qubtan A, et al. Prevalence and
college students (in Chinese). China Health Industry 2016;13:30–3. determinants of burnout syndrome and depression among medical
[53] Ma HJ, Tang L. A study on the differences of college students’ academic students at Sultan Qaboos University: a cross-sectional analytical study
level of learning burnout (in Chinese). Educ Modern 2018;5:135–6. from Oman. Arch Environ Occup Health 2019;74:130–9.
[54] Li ZP, Zhang HX, Zhang X. The relationship between freshman learning [78] Barbosa J, Silva Á, Ferreira MA, Severo M. Transition from secondary
burnout and school mal adjustment (in Chinese). China J Health Psychol school to medical school: the role of self-study and self-regulated learning
2013;21:1418–9. skills in freshman burnout. Acta Med Port 2016;29:803–8.
[55] Hao P, Cui R, Dai YL. Survey and analysis on present situation and [79] Youssef A, Wedad A, Osama AS, et al. Assessment of burnout in medical
related factors of learning burnout for higher vocational nursing student undergraduate students in Riyadh, Saudi Arabia. BMC Med Educ
(in Chinese). Nurs Rehabil J 2013;12:930–2. 2019;19:34.
[56] Lv SX, Li LX, Ke BB, Liang YY, Mai JL. Logistic regression analysis of [80] Thun-Hohenstein L, Höbinger-Ablasser C, Geyerhofer S, Lampert K,
college students’ learning burnout and its influencing factors in a college Schreuer M, Fritz C. Burnout in medical students. Neuropsychiatr
in Guang-zhou (in Chinese). Chin J School Health 2014;35:120–3. 2021;35:17–27.

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