Psychology, Health & Medicine

ISSN: 1354-8506 (Print) 1465-3966 (Online) Journal homepage: https://www.tandfonline.com/loi/cphm20

Academic burnout and depression of Chinese

medical students in the pre-clinical years: the
buffering hypothesis of resilience and social
support

J. Cheng, Y. Y. Zhao, J. Wang & Y. H. Sun

To cite this article: J. Cheng, Y. Y. Zhao, J. Wang & Y. H. Sun (2019): Academic burnout
and depression of Chinese medical students in the pre-clinical years: the buffering
hypothesis of resilience and social support, Psychology, Health & Medicine, DOI:
10.1080/13548506.2019.1709651

To link to this article: https://doi.org/10.1080/13548506.2019.1709651

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PSYCHOLOGY, HEALTH & MEDICINE
https://doi.org/10.1080/13548506.2019.1709651

Academic burnout and depression of Chinese medical

students in the pre-clinical years: the buffering hypothesis of
resilience and social support
a
J. Cheng , Y. Y. Zhaob, J. Wangc and Y. H. Sund
a
School of Health Service Management, Anhui Medical University, Hefei, China; bThe Fifth Sanatorium for
Retired Cadres, Anhui Provincial Military Region, Hefei, China; cSchool of Public Health, Anhui Medical
University, Hefei, China; dSchool of Public Health, Anhui Medical University, Hefei, China

ABSTRACT ARTICLE HISTORY

The present study explored whether the two psychosocial Received 24 January 2019
resources including resilience and social support serve as moderat- Accepted 19 December 2019
ing factors in the process between academic burnout and depres- KEYWORDS
sion among medical students, and investigated factors that Resilience; social support;
associated with depression. We applied Learning Burnout Scale of academic burnout;
Undergraduates, Beck Depression Inventory-II, Connor-Davidson depression; Chinese medical
Resilience Scale, and Social Support Rating Scale as tools for an students
investigation with 1722 Chinese medical students. Academic burn-
out positively correlated with depression while resilience and social
support negatively related to depression. Hierarchical regression
implied that resilience moderated burnout and depression while
social support did not show a buffer effect between the same
variables. Building resilience and enhancing their social support
are essential for preventing depression in their college life. It is
also worth noting that resilience can still work against depression
even when academic burnout emerged.

Introduction
The concept of burnout was first introduced in human service professionals and subse-
quently extended to other professionals and students (Maslach, Schaufeli, & Leiter, 2001).
Dyrbye et al. (2009) defined burnout as a syndrome of emotional exhaustion, deperso-
nalization, and low personal accomplishment culminates in decreased effectiveness at
work and is particularly common in residents and physicians, as well as in medical
students (Dyrbye et al., 2009; Pagnin et al., 2013). When studying burnout among
students the term ‘students burnout’ was usually applied (Dyrbye et al., 2009, 2006;
Mazurkiewicz, Korenstein, Fallar, & Ripp, 2012) and Maslach Burnout Inventory (MBI)
is the most commonly used instrument for measuring it, which included three subscales
as exhaustion, cynicism, and professional efficacy (Frajerman, Morvan, Krebs, Gorwood,
& Chaumette, 2019). Other studies also focused on students the term ‘academic burnout’
(Garratt-Reed, Howell, Hayes, & Boyes, 2018; Kristanto, Chen, & Thoo, 2016; Li, Ma, Liu,

CONTACT Y. H. Sun yhsun_ahmu_edu@yeah.net School of Public Health, Anhui Medical University, Meishan
Road 81, Hefei, China
Supplemental data for this article can be accessed here.
© 2019 Informa UK Limited, trading as Taylor & Francis Group
2 J. CHENG ET AL.

& Jing, 2018; Rios-Risquez, Garcia-Izquierdo, Sabuco-Tebar, Carrillo-Garcia, & Solano-

Ruiz, 2018) was adopted with the core elements including emotional exhaustion, cyni-
cism, and academic inefficacy (Schaufeli, Martínez, Pinto, Salanova, & Bakker, 2002).
Both ‘students burnout’ and ‘academic burnout’ originated from the definition of burn-
out contains the same core elements while particularly focus on student population or
restrict to academic-induced. In some studies, the two terms were used without distinc-
tion (Chunming, Harrison, MacIntyre, Travaglia, & Balasooriya, 2017; Yang & Chen,
2015).
In the Chinese culture and social context, even college students live in a test-
oriented education system, which made learning itself became a vast source of
pressure. Therefore, researchers paid great emphasis on academic-induced pres-
sure among Chinese students. Lian, Yang, and Wu (2005) defined academic
burnout or learning burnout (the same term in Chinese) as the negative attitudes
and behaviors involving tiredness from learning because of the pressure of or lack
of interest in learning. Learning Burnout Scale of Undergraduates (LBS) (Lian
et al., 2005) has been widely used to assess burnout among Chinese students. The
LBS instructions explicitly mention ‘academic/learning’ as the context of reference
for the questionnaire. According to the latest review (Frajerman et al., 2019) which
abstracted information from English publishing data, the prevalence of current
burnout was 44.2% [33.4–55.0] among medical students with most studies
included adopting MBI instrument. While in Chinese medical students, literature
(Chunming et al., 2017) reported 25.8% to 52.1% of students had above moderate
levels of burnout in which more than half of the studies included implementing
the instrument of LBS. Although there existed differences of burnout assessment
among medical students on the practical level due to culture and social context
diversities, it was universally recognized that medical students are often exposed to
academic pressure and competitive environment which favor the onset of burnout
(Chunming et al., 2017; Dyrbye et al., 2009; Pagnin et al., 2013).
Some researchers believed that burnout can increase the possibility of depres-
sion (Grover, Sahoo, Bhalla, & Avasthi, 2018) or vice versa (Ahola & Hakanen,
2007; Iacovides, Fountoulakis, Kaprinis, & Kaprinis, 2003; Mcknight & Glass,
1995). While others (Bianchi & Brisson, 2017; Bianchi, Schonfeld, & Laurent,
2015) found that burnout and depressive symptoms exist overlap. Although
there were inconsistencies about the relationship between burnout and depression,
when specified to academic burnout, researchers put great emphasis on the rela-
tionship from academic-induced pressure to depressive symptoms. Studies demon-
strated that academic burnout paves the way for the development of negative
psychological consequences such as depressive symptoms (Mazurkiewicz et al.,
2012; Salmela-Aro & Upadyaya, 2014). Noam and Hermann (2002) put forward
that students who struggle academically are at further risk for the development of
mental health problems.
Evidence showed higher depression among medical students compared with their peers
(Goebert et al., 2009) or substantial distress among medical students (Hope & Henderson,
2014) worldwide. One study carried out in China (Shi, Liu, Wang, & Wang, 2016) even
reported as high as 66.8% (Center for Epidemiologic Studies Depression Scale ≥16)
prevalence of depressive symptoms among Chinese medical students. It is very important
 PSYCHOLOGY, HEALTH & MEDICINE 3

to decrease the risk of being distress of this population which not only about their own well-
being but also about the quality of health care they will provide in the future career
(Fahrenkopf et al., 2008). Researchers put great attention to explore factors which may
prevent or protect them from depression. Resilience and social support are outstanding
factors among those positive resources.
Resilience was introduced as a protective factor that can decrease the level of
distress (Campbell-Sills, Cohan, & Stein, 2006; Hjemdal, Vogel, Solem, Hagen, &
Stiles, 2011). It refers to patterns of positive adaptation or development manifested
in the context of adverse experiences (Masten, Best, & Garmezy, 1990). Individuals
with high resilience can manage stress and stay well and can learn from what
might otherwise be significant or overwhelming psychological threats
(Antonovsky, 1987). When exploring the associations between academic burnout
and psychological well-being, Rísquez (Rios-Risquez et al., 2018) found that resi-
lience has an important positive effect on psychological health in a sample of
university nursing students. Tafoya et al. (2019) also found that higher resilience
can explain the decrease in depression symptoms among medical students.
The moderation effect of resilience also drew the attention by researchers but
showed inconsistent findings. A study reported no moderate influence of resilience
between perceived stress and depression in heroin addict (Wang, Xu, Gu, Zhu, &
Liang, 2018), while others indicated resilience potentially moderates the risk of
depression on suicidal ideation in patient with depression (Min, Lee, & Chae,
2015). One study (Peng et al., 2012) carried out in Chinese medical students
implied the moderation effect of resilience between negative life events and mental
health problems. It was also reported that resilience moderated the relationship
between stress and symptoms of depression among adolescents (Anyan &
Hjemdal, 2016). When academic burnout regarded as an adversity or a stressful
circumstance, can resilience still work for preventing depression among medical
students? To our knowledge, no study has answered this question at present.
Social support refers to the resources that an individual receives or perceives to be
available from his/her social networks (Sneed & Cohen, 2014). The social support
constructs commonly included three dimensions as emotional (empathy, someone to
talk to and acts of affection), informational (advice or education), and instrumental
support (financial support, actual helping behaviors) (Rueger, Malecki, Pyun, Aycock,
& Coyle, 2016). It was assumed as an important protective factor against depression both
directly through the benefits of social relationships and indirectly as a buffer against
stressful circumstances (Barger, Messerli-Burgy, & Barth, 2014; Gariepy, Honkaniemi, &
Quesnel-Vallee, 2016; Mccorkle, Rogers, Dunn, Lyass, & Wan, 2008). It also found that
support from social networks can help college students lessen general distress (Elliott &
Gramling, 1990).
Abundant literature explored the direct influence of resilience and social support to
depression, some studies suggested moderation effect of these two psychosocial factors to
depression under stressful circumstances; however, little research paid attention to their
roles on the relationship between academic burnout and depression. Based on the fact
that half of the medical students will experience burnout during medical school
(Chunming et al., 2017; Frajerman et al., 2019), studies on positive factors at every
stage even when facing academic burnout can provide valuable information for
4 J. CHENG ET AL.

understanding the role of psychosocial variables and thus inform interventions for the
whole process of mental health improvement for medical students.
Therefore, the present study aimed to evaluate the moderating roles of resilience and
social support on the relationship between academic burnout and depression among
Chinese medical students. The following hypotheses were tested: (1) Academic burnout
and depression will be positively correlated, while resilience and social support negatively
related to depression; (2) The relationship between academic burnout and depression
will be modified by resilience; (3) The relationship between academic burnout and
depression will be modified by social support.

2. Method
2.1 Subjects and procedures
The study was approved by the institutional review boards of Anhui Medical University,
conducted at the biggest medical school of Anhui Province which located in East China.
This was a cross-sectional design. We adopted a stratified-cluster sampling method to
recruit the clinical medical undergraduates of the University from pre-clinical stages
including Grade 1, 2 and 3. Non-clinical majors within the same university such as health
service management and medical law were excluded based on the purpose of focusing on
the population who might be a clinician in the future.
Students who met with the criteria that are officially registered as a member of the class
were all invited to participate during summary sessions delivered at the end of the
academic year. Researchers attended at the beginning of each session, delivered informed
consent, and administrated the paper-based questionnaire to the potential participants.
Participation was voluntary and can withdraw anytime during the survey. The ques-
tionnaires were self-administered by respondents independently in the classroom and
recovered by the researchers on the spot.
In total, 1865 questionnaires were distributed to every student on the spot and 1722
respondents completed the questionnaire. The response rate was 92.33%. The final
sample composed of 864 (50.2%) males and 858 (49.8%) females; 769 (44.8%) with
urban origin students and 946 (55.2%) with rural origin students; 1038 (61.7%) with
no experience of being left, 297 (17.6%) experienced one parent left, and 348 (20.7%)
experienced both parents left; 468 (27.3%) Grade 1 students, 649 (37.9%) Grade 2
students, and 597 (34.8) Grade 3 students.

2.2 Measures/instruments
2.2.1 Demographic characteristics
Demographics such as age, gender, students’ origin (urban and rural areas),
academic year, and left behind experience were obtained in this study.

2.2.2 Learning burnout

Learning Burnout Scale of Undergraduates (Lian et al., 2005) which developed by
Chinese researchers was applied. The scale included 20 items. Each item rated from 1
(‘Not at all like me’) to 5 (‘Very much like me’), which makes the total score ranging from
 PSYCHOLOGY, HEALTH & MEDICINE 5

20 to 100. Examples of the items were as the following: ‘I felt exhausted after learning for
a whole day.’ ‘I have this ability to get my bachelor degree.’ LBS was the most widely used
instrument in China. Higher scores indicate heavier academic burnout. Mean item
score≥3 also means academic burnout. The Cronbach α was 0.86 in this study.

2.2.3 Depression
Beck Depression Inventory-II (BDI-II) was designed to measure the severity of
depression in adolescents and adults (Beck, Steer, & Brown, 1996). The Chinese
version was translated by Chinese psychologists (Wang et al., 2011), tested among
populations and applied widely (Yang, Wu., & Peng, 2014). Questions of the
inventory based on a 4-point scale ranging from 0 to 3, which are related to the
subject’s symptoms during the previous 2 weeks. Items are summed to create
a total score, ranging from 0 to 63. In Chinses context researchers recommend
BDI cutoff scores for classifying people as non-depressed (0–13), mildly (14–19),
moderate (20–28) and depressed (29–63). The Cronbach α was 0.92 of this study.

2.2.4 Psychological resilience

Psychological resilience was assessed by the 25-item Connor–Davidson Resilience Scale
(CD-RISC) (Connor & Davidson, 2003). Each item is scored from 0 (not true at all) to 4
(true nearly all the time), making the total score range from 0 to 100. Higher scores reflect
greater resilience. Normative score of CD-RISC in Chinese medical students was 61.7 ±
10.6 (Peng et al., 2012). The author of this study had obtained written approval from
Dr. Davidson to use the Chinese version of the CD-RISC scale for academic research. The
Cronbach α was 0.92 in the current study.

2.2.5 Social support

The Social support scale is comprised of 10 items (Xiao, 1994). Each item (for items 1–5, and
8–10) rated on a 4-point scale ranging from 1 to 4. For items (6–7) assessed the source of
social support, the number of sources counted as scores. The total score is calculated by
adding the 10-item scores together. Higher scores indicate greater social support. This scale
showed good predictive validity and internal consistency among Chinese adults (Zhang,
2007). A national norm of servicemen showed a score of 40.11 ± 7.55 (Yang et al., 2006), while
a survey among 600 college students was 37.51 ± 6.96 (Pan, 2011). The Cronbach α was 0.64
in the present study.

2.3 Statistical analysis

Statistical analysis was performed using SPSS 23.0. Continuous variables were reported as
mean ± standard deviation. Categorical variables were reported as numbers and percentages.
Spearman correlation was applied to examine the relationships among variables such as
burnout, depression, resilience, social support, and demographic factors. Hierarchical multi-
ple regression analyses were conducted to examine the moderation hypothesis.
To further explore the interaction relationship among variables, we adopted the pick-
a – point approach (Toothaker, 1994) using a figure. Firstly we computed simple slopes of
depression on learning burnout at high and low levels of social support or resilience;
6 J. CHENG ET AL.

then, we tested whether simple slopes at high and low values of social support or
resilience differ significantly from zero in predicting depression.

3. Results
3.1 Distribution of variables
Academic burnout, resilience and social support fitted normal distribution curves.
Depression fitted a skewed distribution curve and higher scores accompanied by low
frequencies (see supplemental material 1).

3.2 Scale scores by demographics

Table 1 displays the means ± SD of scores among demographic groups. Scores of
resilience, social support, academic burnout, and depression showed differences
among most groups. Especially, scores of all variables showed significant differences
regarding years in medical school and types of being left. For example, significant
differences were found in the total score of resilience based on the number of years
in medical school (F = 9.35, P < 0.01) and types of being left (F = 8.57, P < 0.01).

Table 1. Distribution of the scale according to demographic characteristic groups.

Scales Mean SD F/t P Scales Mean SD F/t P
Resilience Social support
Gender Gender
Male 66.54 13.23 1.32 0.19 Male 34.26 6.17 −2.38 0.02
Female 65.73 12.47 Female 34.97 5.42
years in medical school years in medical school
1 year 68.28 11.80 9.35 <0.01 1 year 36.38 5.55 35.88 <0.01
2 year 65.05 12.40 2 year 34.58 5.55
3 year 65.63 13.96 3 year 33.24 5.89
Place of Residence Place of Residence
urban 67.08 13.25 2.75 0.01 urban area 34.82 5.96 1.16 0.25
rural 65.38 12.33 rural area 34.48 5.65
Left behind experience Left behind experience
no parents left 66.96 12.87 8.57 <0.01 no parents left 35.01 5.78 4.87 0.01
single parent left 66.17 12.16 single parent left 34.18 5.79
both parents left 63.70 12.67 both parents left 33.97 5.76
Depression Learning burnout
Gender Gender
Male 7.94 9.19 2.47 0.01 Male 55.76 10.31 3.51 <0.01
Female 6.96 7.09 Female 54.01 10.03
years in medical school years in medical school
1 year 6.78 6.94 26.61 <0.01 1 year 53.90 10.59 3.17 0.04
2 year 8.20 8.02 2 year 55.46 10.34
3 year 7.23 9.36 3 year 54.95 9.70
Place of Residence Place of Residence
urban 6.84 7.78 −2.77 0.01 urban area 54.22 10.30 −2.44 0.02
rural 7.93 8.49 rural area 55.45 10.10
Left behind experience Left behind experience
no parents left 6.99 8.15 19.47 <0.01 no parents left 54.40 10.55 5.65 <0.01
single parent left 7.93 8.45 single parent left 54.45 9.38
both parents left 8.71 8.43 both parents left 56.48 9.66
 PSYCHOLOGY, HEALTH & MEDICINE 7

Table 2. Means, standard deviations, and correlations of all the measures (Spearman rho).
Variables Mean SD 1 2 3 4
1.academic burnout 54.889 10.201 - .374** −.434** −.276**
2.Depression 7.472 8.252 - −.371** −.217**
3.Psychological resilience 66.12 12.873 - .336**
4.Social support 34.615 5.813 -
Note:*p< 0.05, **p< 0.01.

3.3 Correlation analysis between depression and other variables

Table 2 demonstrates correlations among burnout, depression, resilience and social
support. Depression positively correlated with academic burnout (r = 0.374, p < 0.01),
while negatively correlated with resilience (r = −0.371, p < 0.01) and social support (r =
−0.217, p < 0.01).

3.4 Regression models of all variables

Before testing the moderating effect, academic burnout, resilience, and social support
variables were centered to reduce problems related to multicollinearity. Thus, z-scores
were generated and calculated for the interaction terms. Diagnostic tests showed that the
variance inflation factors (VIFs) were well ranging from 1.006 to 1.327 in all the regression
analyses, indicating that there were no problems with multicollinearity in the data.
The control variables (gender, grade, place of residence, and types of being left) were
entered into the regression equation in the first step. Then, in model 2, academic burnout was
entered and showed a significant prediction of depression (ß = 0.381, p < 0.001). (see Table 3)
In model 3, 4, and 5, the moderator variables (resilience and social support) and
interaction effects of academic burnout*resilience, and academic burnout *social support
were entered into the regression analysis in sequence. The results indicated that resilience
(ß = −0.217, p < 0.001) and social support (ß = −0.131, p < 0.001) also predicted

Table 3. Hierarchical multiple linear regression of academic burnout and depression: the moderate
effect of resilience and social support.
Model 1 Model 2 Model 3 Model 4 Model 5
Variables ß P ß P ß P ß P ß P
Controlling Variables
Left behind experience 0.048 0.091 0.021 0.426 0.006 0.823 0.011 0.652 0.010 0.694
Place of residence 0.038 0.185 0.029 0.276 0.021 0.416 0.022 0.385 0.022 0.381
Gender −0.037 0.156 −0.013 0.598 −0.018 0.441 −0.019 0.423 −0.017 0.459
Grade 0.006 0.831 −0.007 0.764 −0.048 0.045 −0.044 0.063 −0.044 0.063
Main effect
Academic burnout (AB) 0.381 <0.001 0.245 <0.001 0.267 <0.001 0.267 <0.001
Moderate effect
resilience (R) −0.217 <0.001 −0.206 <0.001 −0.206 <0.001
social support (SS) −0.131 <0.001 −0.125 <0.001 −0.126 <0.001
Interaction effect
(AB)*(R) −0.124 <0.001 −0.103 <0.001
(AB)*(SS) −0.046 0.080
R2 0.007 0.150 0.212 0.227 0.229
Adjusted R2 0.004 0.147 0.209 0.223 0.224
Δ R2 0.007 0.045 0.144 <0.001 0.062 <0.001 0.015 <0.001 0.002 0.080
8 J. CHENG ET AL.

Figure 1. Moderate effect of resilience between academic burnout and depression.

depression. Regarding interaction effects, resilience played a moderator effect (ß =

−0.124, p < 0.001) in the relationship between learning burnout and depression, mean-
while social support (ß = −0.046, p = 0.080) did not show the buffer effects between the
two variables.
To analyze the interaction effect further, we plotted the simple slopes of depression on
academic burnout at high and low values of resilience (see Figure 1) using worksheets
designed by Jeremy Dawson (http://www.jeremydawson.co.uk/slopes.htm). The simple
slope of resilience M-SD = 0.302 (SE = 0.028, t = 10.754, p < 0.001), while the simple slope
of resilience M+SD = 0.137 (SE = 0.023, t = 5.848, p < 0.001).There was a positive
relationship between academic burnout and depression at both high and low resilience
groups. However, the regression coefficient was less at high levels of resilience group
compared to that of low levels of resilience group, which demonstrated that resilience
moderated the impact of academic burnout on depression.

4 Discussion
Our study provides evidence that resilience is not only negatively correlated with
depression which had reported by literature (Nakazawa et al., 2018; Tafoya et al.,
2019), but also a preventive factor from academic burnout to depression. The findings
also imply a negative correlation between social support and depression, which is
consistent with studies among different populations (Sibalija, Savundranayagam,
Orange, & Kloseck, 2018). However, in contrast to what we expected, no moderate effect
of social support between burnout and depression was observed. It was indicated that
social support had a moderation effect between stress and distress (Frese, 1999), while the
researchers focused on the support within the workplace. Another study carried out
among nurses (Barnett, Martin, & Garza, 2018) also implied that workplace social
support may be more important than personal social support to facilitate mental health
 PSYCHOLOGY, HEALTH & MEDICINE 9

under occupational stressful environment. Different from personal social support which
measures support from families, friends, relatives or colleagues, workplace social support
cares about whether there are persons concerned about individual’s welfare at work,
listen to individual’s problems at work, and can be relied upon or helpful when things get
difficult at work. (Barnett et al., 2018). Our findings showed that once academic burnout
emerged under professional circumstances, personal social support cannot be a buffer
factor anymore. Whether workplace social support as literature implied can play
a moderation effect (Bussing, 1999) need further exploration.
Based on the evidence implied by our findings that resilience and social support
negatively correlated with depression while academic burnout positively related to
depression, building resilience and enhancing social support of medical students are
essential for preventing adverse psychological outcomes in their college life. Meantime it
is important to pay attention to students who showed academic burnout in case of
developing to more severe mental impairment. Furthermore, findings showed that
resilience can still work as a psychological protective resource on the process from
burnout to depression, and great attention should be paid to resilience building in the
Chinese medical training system.
However, lacking longitudinal design, we were not able to track the variables of the
student along with their medical education process. Second, we measured student
burnout with LBS instead of MBI, which considered culture context whereas set obstacles
comparing with peers over the world. Regarding LBS itself, some researchers (Mao,
Chen, Huang, Yang, & Zhang, 2015) pointed out it needs further improvement. Third,
considering there have been relatively few studies on the protective effects of resilience
and social support when academic burnout emerged, our results should still be verified in
other student populations.

Acknowledgments
The authors thank Jonathan Davidson and Kathryn Connor for providing the Connor-Davidson
Resilience Scale.

Disclosure statement
No potential conflict of interest was reported by the authors.

Funding
This work was supported by the National Natural Science Foundation of China under Grant
number 81872704.

ORCID
J. Cheng http://orcid.org/0000-0001-6364-0206
10 J. CHENG ET AL.

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