Prevalence of burnout in medical students: A systematic review and meta-analysis

Abstract

Background:

Medical students are at a significant risk of experiencing psychological issues, such as burnout. Over the past few years, more studies have been conducted on this topic, producing different results.

Aims:

The purpose of this review was to determine the global pooled prevalence rate and risk factors associated with burnout and its components among undergraduate (pre-intern) medical students.

Method:

From inception until 30 November 2021, nine electronic databases were used for an electronic search. Using random-effects meta-analysis, we pooled the estimates using the DerSimonian-Laird method. The prevalence of burnout in medical students was the primary outcome of interest. Data were analyzed globally, by country, by research measure. Age and sex were examined as confounders using meta-regression analysis.

Results:

A random-effects meta-analysis of 42 studies involving 26,824 evaluating the prevalence of burnout in medical students showed an overall prevalence rate 37.23% [32.66%; 42.05%], Q = 2,267.15(41), p < .0001, τ² = .42, τ = .65, I² = 98.2%; H = 7.5. Prevalence of emotional exhaustion, depersonalization, and personal accomplishment were 38.08% [30.67%; 46.10%], 35.07% [26.74%; 44.41%], and 37.23% [32.66%; 42.05%], respectively. Variations were observed between countries and research measures. Age (older) and sex (female) were both significant predictors of burnout.

Conclusion:

The prevalence of burnout in medical students was estimated to be 37.23%. It is urgent that future studies serve as a basis for the development of prevention and treatment programs to prevent and treat burnout in students.

Keywords

Burnout college medical training

Introduction

Burnout is a multifaceted, complex construct related to work, that has been created by Freudenberger (1974). The concept has subsequently been transposed to health professions by Maslach and Jackson (1981); then extended to the student population because of similar triggers including complete assignments within deadlines, as well as having to attend classes and pass exams (McCarthy et al., 1990; Schaufeli et al., 2002). Burnout refers to a combination of three categories of symptoms: emotional exhaustion, depersonalization, and personal accomplishment (Freudenberger, 1974, 1989). Emotional Exhaustion (EE) occurs when high exposure to stressors leads to extreme fatigue and feelings of being emotionally drained (Maslach, 2001). It is the most well-known and widely exhibited characteristic of burnout (Maslach, 2001). The second dimension, depersonalization is defined as an attempt to remove oneself from others in order to cope with one’s excessive workload, leading to a rise in cynicism or disinterest (Maslach, 2001). Reduced Personal Accomplishment (PA) is the third dimension, and it can be viewed as a feeling of inefficacy (Maslach, 2001). It shows up as sentiments of sadness and dissatisfaction with one’s achievements (Maslach, 2001). Furthermore, people who are impacted have a negative opinion of themselves, particularly when it comes to the quality and amount of their work (Maslach, 2001).

University students in general and medical students in particular are increasingly facing high stress levels contributing to more mental health concerns and burnout. The WHO World Mental Health International College Student project conducted in 19 colleges across eight countries revealed ‘widely distributed’ and ‘rising’ prevalence rates of mental disorders among students (Auerbach et al., 2018). Medical students have been shown to engage in even more stress exposure and display more mental health problems than non-medical students (Al-Dabal et al., 2010; Seedhom et al., 2019; Shad et al., 2015), and age-matched individuals in the general population (Quek et al., 2019). Subsequently, we suggest that prevalence of burnout symptoms in medical students would have likely increased over the past few years.

The emergence of the concept of burnout has led to relatively new studies and greater attention to the extent of this phenomenon among this population. A systematic review and meta-analysis published by Erschens et al. (2019) on the topic of burnout among medical students concluded that depending on country factors, the use of instruments, and the cutoff-criterion for burnout symptoms, the prevalence of professional burnout ranged from 7.0% to 75%. Authors analyzed 58 studies involving 35,166 medical students EE had a weighted mean value of 22.93 ± 10.25, DP had a weighted mean value of 8.88 ± 5.64, and PA had a weighted mean value of 35.11 ± 8.03 (Erschens et al., 2019). The previous meta-analysis focused on weighted mean value of the individual characteristics of burnout (i.e. EE, DP, and PA). Interpreting the weighted means is challenging and therefore the present review aimed to investigate the pooled prevalence rate of burnout and its components among medical students during their undergraduate training. Another meta-analysis was designed to estimate the prevalence of burnout among medical students worldwide (Frajerman et al., 2019). The review included period from January 1, 2010 to December 31, 2017, Medline was systematically searched for English-language articles (Frajerman et al., 2019). Burnout was found to be 44.2% [33.4%–55.0%], specifically it was 40.8% for EE [32.8%–48.9%], 35.1% for DP [27.2%–43.0%], and 27.4% for PA [20.5%–34.3%] (Frajerman et al., 2019).

Studies on burnout in medical students have revealed an increased risk in females (Dyrbye, Thomas, Huschka, et al., 2006) and older age groups (Dyrbye, Satele, & West, 2021); however, inconsistencies across studies have been found (Dyrbye, Thomas, Huntington, et al., 2006). Furthermore, differences between countries regarding burnout in medical students have been reported in the existing literature, with higher prevalence in Oceania (Frajerman et al., 2019) and US (St Onge et al., 2022) as compared to other parts of the world. These differences have been related to several country-specific factors, including a wide variation in lifestyle patterns (Lee et al., 2020), family support (Dorrance Hall et al., 2017), and learning environments of medical schools (Bilge et al., 2014; Brown et al., 2007).

Burnout leads to detrimental effects on medical students’ overall mental health including depression, sleep disturbances, substance abuse and even increased suicide risk (Dyrbye & Shanafelt, 2016; Mazurkiewicz et al., 2012; Pacheco et al., 2017). Burnout may also negatively impact academic achievements, specialty and career choice, as well as patients’ care (Blanchard et al., 2010; Campbell et al., 2010; Dyrbye et al., 2008, 2019; Dyrbye & Shanafelt, 2016; Enoch et al., 2013; National Academies of Sciences, Medicine, National Academy of & Committee on Systems Approaches to Improve Patient Care by Supporting Clinician, 2019). Burnout in medical students are thus worth more attention in research and more consideration as a target of prevention and intervention efforts in school settings.

Through the present review, we aimed to perform the largest and most updated systematic review and meta-analysis on burnout among medical students; given that the latest reviews were published in 2019 and only included data up to 2017. Our goals were to: (1) examine the cumulative prevalence rate of burnout among medical students of various ages, genders, and cultural backgrounds and (2) to assess if the prevalence of burnout differs by age, gender, or culture (Western vs. non-Western). In light of the available literature, we expect to observe an increased incidence of burnout symptoms in medical students compared to previously reported rates. Furthermore, we expected that burnout symptoms would be more prevalent among young females and students from Western countries.

Methods

We defined the review problem using the population or problem, intervention or exposure, comparison, and outcome (PICO) approach. Burnout and its components were the review problem. The exposure was being a medical student. Comparative analysis was conducted by age, gender, research tool, country, and culture within each group. Prevalence rates were the outcome (Methley et al., 2014). We examined the cumulative prevalence rate of burnout among medical students of various ages, genders, and cultural backgrounds. Burnout encompasses a large variety of heterogeneous symptoms, and we assessed whether they meet threshold levels, as well as identify those who may be at risk of developing them. As a result, we focused on studies that utilized a psychometric tool with established cutoff values to investigate the prevalence of burnout.

As a guide for conducting and reporting this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) statement was used (Methley et al., 2014). The meta-analysis was pre-registered on the database for open science framework (OSF) https://osf.io/bq2wf. The Data extraction for complex meta-analysis (DECiMAL) guide was used to extract all the data (Pedder et al., 2016). The systematic review was screened and coded using RAYYAN, a free online tool for digital technologies like natural language processing, artificial intelligence, and machine learning (Ouzzani et al., 2016). EndNote 9.3.3 was used to manage references.

Database searches

APA PsychINFO, EBSCOhost Research Platform, Embase, MEDLINE, ProQuest, ScienceDirect, Scopus, and Web of Science were used for electronic search by two reviewers FFR and HJ. The databases were searched from the inception of each source to 30th November 2021. As part of the search strategy, we cross-matched keywords based on keyword phrases and Medical Subject Headings (MESH). To create a [All Fields] search, the Boolean logic operators ([OR] within lists), ([AND] between lists) were used. The lists were: List A: medical student [OR] medical intern [OR] student doctor [OR] medical trainee [OR] medical pupil. List B: burnout [OR] emotional exhaution [OR] depersonalization [OR] personal accolplishment. Both American English and British English langauges were used for the term depersonalization (and depersonalisation) to ensure large coverage within the literature.

Inclusion and exclusion criteria

We included observational studies examining the prevalence rate of burnout among undergraduate (pre-intern) medical students. There were four specific inclusion criteria for this study: (1) studies must have been published in English; (2) published before 1st December 2021; (3) focused on undergraduate medical students; and (4) reported burnout prevalence among the target population. Studies should have used validated, standardized tools to screen for burnout, such as: The Work-Related Behavior and Experience Patterns Scale (Schaarschmidt & Fischer, 1996), the Oldenburg Burnout Inventory (Demerouti & Nachreiner, 1998), the burnout symptom scale (Hagemann & Geuenich, 2009), the Hamburg Burnout Inventory (Burisch, 2007), the Copenhagen Burnout Inventory (Kristensen et al., 2005), and the Maslach Burnout Inventory (MBI; Maslach, 2001; Maslach et al., 1986, 1996).

The following specific exclusion criteria were used: (1) studies that grouped medical and non-medical students in the same group without analyzing subgroup data for medical students; (2) studies that assessed mental health problems or stress or distress rather than burnout prevalence; and (3) inability to access the full text even after contacting the authors.

Outcomes and measures

The sample size and event rate were reported for each study. The primary outcomes was to determine burnout in medical students by using pre-defined cut-off scores from continuous measures of the syndrome. Secondary outcomes included a comparison of variability in the prevalence of burnout based on age and sex of participants, country of study, Western versus non-Western culture, and the tool used in the study. Two members of the review team independently checked titles, abstracts, and full text for eligibility. Two independent members of the team performed initial data extraction and quality assessment for each study. Any disagreements regarding the suitability of the study to include in the review were resolved through a discussion with the senior reviewer/expert clinician, HJ. Communication by email was used to request missing data from the corresponding author of the included studies.

The following variables were collected during data extraction: author name, year, sample size, country (Western/non-Western), and measure used. Participants’ characteristics in each study, including mean age (years), sex (proportion of female participants), as well as the main outcomes of burnout experienced by medical students, were coded. In terms of the United Nations, Western countries are defined as the countries of Western Europe and Other States (Andorra, Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland Ireland, Israel, Italy, Liechtenstein, Luxembourg, Malta, Monaco, Netherlands, New Zealand, Norway, Portugal, San Marino, Spain, Sweden, Switzerland, Turkey, United Kingdom of Great Britain and Northern Ireland, and United States of America; UN, 2022).

Quality assessment using the Newcastle-Ottawa Scale (NOS)

The NOS (Wells et al., 2000) was used to assess quality. Using the NOS checklist, we examined three factors (participant selection, comparisons, and results and statistics). In NOS, studies are rated from 1 to 10 using a star system, with a maximum of 9 stars (cross-sectional and cohort studies) or 10 stars (case-control studies; Luchini et al., 2017). In this review, studies with a score of >8 have good quality and low risk of bias, studies with a score of 5 to 7 have moderate quality and moderate risk of bias, and studies with a score of 0 to 4 have low quality and high bias risk.

Data synthesis and statistical analyses

A pool of data was analyzed using the DerSimonian-Laird method (Jackson et al., 2010), with the pooled prevalence and 95% confidence interval reported. The random-effects meta-analysis synthesizes data under the assumption that actual effects differ amongst studies (Shuster, 2014). Random-effects models assume that each study estimates a different true effect and that the distribution of this true effect is a normal distribution. Graphs of the data were displayed using Forest plots (Lewis & Clarke, 2001). They used these values to determine whether an impact is substantial, based on drapery plots (Rücker & Schwarzer, 2021) and p-curve analysis (van Aert et al., 2016). We used the I² statistic to assess study heterogeneity; a value of 75% to 100% was considered high (Higgins & Thompson, 2002). The H has also been described mathematically as a transformation that describes the proportion of total changes because of heterogeneity (Erschens et al., 2019). We also assessed the between-study heterogeneity using Cochran’s Q statistics (Huedo-Medina et al., 2006), tau2 (τ²; Higgins & Thompson, 2002), and tau (τ; Higgins & Thompson, 2002). As a sensitivity analysis, we carried out one study at a time by eliminating it one at a time (a Jackson-Knife analysis) in order to ensure that our results are not impacted by a single study (Mathur & VanderWeele, 2020; Patsopoulos et al., 2008). The inclusion of outliers may compromise the validity and robustness of the meta-analysis. This led to the discovery and elimination of outliers. A study is classified as an outlier if the study’s confidence interval does not match the pooled effect’s confidence interval (Debray et al., 2015).

As a visual tool, funnel plots were used to examine publication bias (Vandenbroucke, 1998). The publication bias was analyzed using Kendall’s rank-order correlations (Kendall & Gibbons, 1990) and Egger’s regression (Vandenbroucke, 1998). Duval and Tweedie (2000) developed a trim-and-fill approach to generate adjusted point estimates to address funnel plot asymmetry due to possible publication bias. The moderator analysis in a meta-analysis involves finding and accounting for systematic differences in the size of the effect or outcome being meta-analyzed (Higgins et al., 2019). The meta-regression analyses the effects of one or more study characteristics on observed effect sizes. Moderator analyses may also be conducted by subgrouping studies based on categorical variables (Higgins & Thompson, 2002).

We analyzed all data using R software for Statistical Computing (R Core Team, 2013). A selection flow diagram was created with the package ‘PRISMA2020’ (Haddaway et al., 2021). All meta-analysis was performed with the packages ‘meta’ (Schwarzer & Schwarzer, 2012) and ‘metafor’ (Viechtbauer, 2010). Using ‘robvis’, quality assessment plots were produced (McGuinness & Higgins, 2021). For the quality evaluation, a traffic light plot, which tabulates the judgment for each study in each area of the NOS, was used (McGuinness & Higgins, 2021). To show the proportion of information inside each judgment, a summary plot (weighted) was developed for all investigations (McGuinness & Higgins, 2021).

Results

Descriptive description of the studies included

During the period from November 30, 2021 through the inception of the databases, the search was conducted. Searches of electronic databases and other sources led to the identification of 2,875 records. After removing duplicate records and ineligible records, 386 records remained. The full text of 175 prospective articles was evaluated. There were 133 papers excluded, including: non-English full text, no prevalence data, and wrong population. Supplemental Figure S1 shows the search process using the PRISMA2020 flowchart.

A total of 42 studies involving 26,824 students were included in this systematic review and meta-analysis (Aghajani Liasi et al., 2021; Al-Jehani et al., 2020; Alkhamees et al., 2020; Almalki et al., 2017; Altannir et al., 2019; Asghar et al., 2019; Barbosa et al., 2018; Bolatov et al., 2021; Cecil et al., 2014; Chang et al., 2012; Chin et al., 2016; Danilewitz et al., 2018; Dyrbye et al., 2007, 2008, Dyrbye, Massie, et al., 2010, 2011, 2014; Erschens et al., 2018; Fares, Al Tabosh et al., 2016; Fitzpatrick et al., 2019; Galán et al., 2011; Gatell et al., 2017; Ilic et al., 2021; Jordan et al., 2020; Khosravi, 2021; Lee et al., 2020; Morgan et al., 2020; Muzafar et al., 2015; Nteveros et al., 2020; Obregon et al., 2020; Pharasi & Patra, 2020; Popa-Velea et al., 2017; Rudinskaitė et al., 2020; Shadid et al., 2020; Shokrpour et al., 2020; Shrestha et al., 2021; Tucker et al., 2017; van Venrooij et al., 2017; Vidhukumar & Hamza, 2020; Voltmer et al., 2010; Zhang et al., 2021; Zis et al., 2021). The studies came from twenty-two countries: USA n = 9, (21 %); KSA n = 5, (12 %); Iran n = 3, (7 %); Canada n = 3, (7 %); China n = 2, (5 %); Cyprus n = 2, (5 %); India n = 2, (5 %); Pakistan n = 2, (5 %); Brazil n = 1, (2 %); Germany n = 1, (2 %); Germany n = 1, (2 %); Ireland n = 1, (2 %); Kazakhstan n = 1, (2 %); Lebanon n = 1, (2 %); Malaysia n = 1, (2 %); Multi n = 1, (2 %); Nepal n = 1, (2 %); Netherlands n = 1, (2 %); Romania n = 1, (2 %); Serbia n = 1, (2 %); Spain n = 1, (2 %); and UK n = 1, (2 %).

The mean sample size was 635 (95% CI: 386–837) participants. The median was 356 and the minimum and maximum were 52 and 4,402, respectively. Participants were mainly females 60% (95% CI: 57%–64%). The mean age was 22 years (95% CI: 19–26 years), and the median was 22 years.

NOS quality score was 6.5 ± 0.50. Supplemental Figure S2 presents a detailed analysis of quality assessment for each study in the meta-analysis. Majority of the studies met the criteria for excellent quality, while 95% met the criteria for moderate quality. Risk bias was most prevalent in the selection dimension, especially in sample size and representativeness, as shown in Supplemental Figure S3. In Table 1, all the studies considered are listed in summary form.

Table 1.

Selected study and sample characteristics of studies investigating prevalence of burnout in medical students.

SN	Study	Study characteristics	Sample characteristics	Quality score
1	Aghajani Liasi et al. (2021)	Cross-Sectional Study design; Convenient sample = 120; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 54; Age = NR years.	6
2	Al-Jehani et al. (2020)	Cross-Sectional Study design; Convenient sample = 593; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = CBI.	% Female = 63.1; Age = NR years.	6
3	Alkhamees et al. (2020)	Cross-Sectional Study design; Convenient sample = 305; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = BRS, CBI, PSS.	% Female = 51.7; Age = 23.98 years.	6
4	Almalki et al. (2017)	Cross-Sectional Study design; Convenient sample = 249; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = LBQ.	% Female = 60.3; Age = 19.52 years.	6
5	Altannir et al. (2019)	Cross-Sectional Study design; Convenient sample = 276; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = ; Age = NR years.	6
6	Chin et al. (2016)	Cross-Sectional Study design; Convenient sample = 452; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 32.5; Age = 21.8 years.	6
7	Asghar et al. (2019)	Cross-Sectional Study design; Convenient sample = 600; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 65.8; Age = NR years.	6
8	Barbosa et al. (2018)	Cross-Sectional Study design; Convenient sample = 399; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = BCSQ.	% Female = 61; Age = NR years.	6
9	Bolatov et al. (2021)	Cross-Sectional Study design; Convenient sample = 619; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 66.5; Age = 21.8 years.	6
10	Cecil et al. (2014)	Cross-Sectional Study design; Convenient sample = 356; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 80.2; Age = 24.4 years.	6
11	Chang et al. (2012)	Cross-Sectional Study design; Convenient sample = 336; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 63.1; Age = 23.19 years.	6
12	Danilewitz et al. (2018)	Cohort Study design; Convenient sample = 52; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 52.8; Age = NR years.	6
13	Dyrbye et al. (2008)	Cross-Sectional Study design; Convenient sample = 2,566; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 57.7; Age = NR years.	6
14	Dyrbye, Power, et al. (2010)	Cross-Sectional Study design; Convenient sample = 4,402; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = CBI.	% Female = 75; Age = 19.14 years.	6
15	Dyrbye et al. (2011)	Cross-Sectional Study design; Convenient sample = 2,246; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = MBI.	% Female = 54.9; Age = 25 years.	6
16	Dyrbye et al. (2007)	Cross-Sectional Study design; Convenient sample = 2,248; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = CBI.	% Female = 53.9; Age = NR years.	6
17	Dyrbye et al. (2014)	Cross-Sectional Study design; Convenient sample = 1,689; Reported prevalence rates = Burnout, EE, PA, DP; Research measure = ProQOL.	% Female = 71; Age = 25 years.	6
18	Erschens et al. (2018)	Cross-Sectional Study design; Convenient sample = 597; Reported prevalence rates = Burnout, EE, DP; Research measure = MBI.	% Female = 55.8; Age = 20.3 years.	6
19	Fares, Al Tabosh et al. (2016)	Cross-Sectional Study design; Convenient sample = 165; Reported prevalence rates = Burnout; Research measure = OLBI-S.	% Female = 35.4; Age = 22 years.	7
20	Fitzpatrick et al. (2019)	Cross-Sectional Study design; Convenient sample = 269; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = ; Age = NR years.	7
21	Galán et al. (2011)	Cross-Sectional Study design; Convenient sample = 270; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 69.5; Age = 22.6 years.	7
22	Gatell et al. (2017)	Cross-Sectional Study design; Convenient sample = 388; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 56.6; Age = 24.9 years.	7
23	Zhang et al. (2021)	Cross-Sectional Study design; Convenient sample = 1,062; Reported prevalence rates = Burnout; Research measure = CBI.	% Female = 62; Age = NR years.	7
24	Jordan et al. (2020)	Cross-Sectional Study design; Convenient sample = 172; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 55.8; Age = 20.5 years.	7
25	Khosravi (2021)	Cross-Sectional Study design; Convenient sample = 400; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 37.1; Age = NR years.	7
26	Lee et al. (2020)	Cross-Sectional Study design; Convenient sample = 731; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 82.4; Age = 20.5 years.	7
27	Ilic et al. (2021)	Cross-Sectional Study design; Convenient sample = 760; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 57.7; Age = NR years.	7
28	Morgan et al. (2020)	Cross-Sectional Study design; Convenient sample = 129; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 66.2; Age = 21.8 years.	7
29	Muzafar et al. (2015)	Cross-Sectional Study design; Convenient sample = 777; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 46; Age = 20.6 years.	7
30	Nteveros et al. (2020)	Cross-Sectional Study design; Convenient sample = 182; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 69.2; Age = 23.8 years.	7
31	Obregon et al. (2020)	Cross-Sectional Study design; Convenient sample = 273; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 62.3; Age = 25.6 years.	7
32	Pharasi and Patra (2020)	Cross-Sectional Study design; Convenient sample = 196; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 55.6; Age = 21 years.	7
33	Popa-Velea et al. (2017)	Cross-Sectional Study design; Convenient sample = 299; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 50.1; Age = NR years.	7
34	Rudinskaitė et al. (2020)	Cross-Sectional Study design; Convenient sample = 261; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 68.5; Age = 19.23 years.	7
35	Shadid et al. (2020)	Cross-Sectional Study design; Convenient sample = 356; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 75.5; Age = 21.2 years.	7
36	Shokrpour et al. (2020)	Cross-Sectional Study design; Convenient sample = 195; Reported prevalence rates = Burnout; Research measure = CBI.	% Female = 58.8; Age = NR years.	7
37	Shrestha et al. (2021)	Cross-Sectional Study design; Convenient sample = 229; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 93.5; Age = NR years.	7
38	Tucker et al. (2017)	Cross-Sectional Study design; Convenient sample = 59; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 48.4; Age = NR years.	7
39	van Venrooij et al. (2017)	Cross-Sectional Study design; Convenient sample = 433; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 70; Age = NR years.	7
40	Vidhukumar and Hamza, 2020)	Cross-Sectional Study design; Convenient sample = 375; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 66.6; Age = NR years.	7
41	Voltmer et al. (2010)	Cross-Sectional Study design; Convenient sample = 584; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 48.6; Age = NR years.	7
42	Zis et al. (2021)	Cross-Sectional Study design; Convenient sample = 154; Reported prevalence rates = Burnout; Research measure = MBI.	% Female = 48.2; Age = NR years.	7

Note. BCSQ = Breso’s Academic Burnout Questionnaire; BRS = Brief Resiliency Scale; CBI = The Copenhagen Burnout Inventory; LBQ = Learning Burnout Questionnaire; MBI = Maslach Burnout Inventory; NA = not available; NR = not reported; OLBI-S = Oldenburg Burnout Inventory adapted for students; ProQOL = professional quality of life; PSS = Perceived Stress Scale.

Burnout and its components in medical students: a meta-analysis

Global assessment of burnout in medical students

Using all available studies, a random-effects meta-analysis evaluated the prevalence of burnout in medical students (K = 42, N = 26,824), and generated a pooled prevalence rate of 37.23% [32.66%; 42.05%], heterogeneity Q = 2,267.15(41), p < .0001, τ² = .42, τ = .65, I² = 98.2%; H = 7.5. Table 2 provides details of the meta-analysis of prevalence of burnout and its components in medical students. Supplemental Figure S4 depicts the forest plot of the meta-analysis of burnout in medical students using all assessment measures. A (leave-one-out) sensitivity analysis found that no study had a greater than 1% impact on the global prevalence estimate of burnout in medical students.

Table 2.

A meta-analysis of prevalence of burnout and its components in medical students.

Analysis	K	N	Random effects model		Heterogeneity						Moderators		Publication bias
Analysis	K	N	Pooled results (%) [95% CI]	Forest plot	I^2a (%)	τ	τ^2b	H ^c	Q	Cochran’s Q p-Value^d	Age	Sex	Egger’s test^e	Rank test^f
Prevalence of burnout	42	26,824	37.23 [32.66–42.05]	Supplemental Figure S4	98.2	0.6422	0.4124	7.44	2,267.15 (41)	p < .0001	0.01	0.01	0.008	0.40
Prevalence of emotional exhaustion	18	10,046	38.08 [30.67–46.10]	Supplemental Figure S8	97.2	0.6816	0.4645	6.65	751.58 (17)	p < .0001	0.1	0.12	0.90	0.42
Prevalence of personal accomplishment	17	5,644	36.85 [26.63–48.40]	Supplemental Figure S9	98.1	0.9645	0.9303	7.19	828.14 (16)	p < .0001	0.25	0.27	0.88	0.88
Prevalence of depersonalization	18	10,046	35.07 [26.74–44.41]	Supplemental Figure S10	98.3	0.8107	0.6573	7.63	988.50 (17)	p < .0001	0.001	0.1	0.35	0.57

Note. K = number of included studies; N = number of included samples.

I² statistic referred to the percentage of variation across samples due to heterogeneity rather than chance.

τ² describe the extent of variation among the effects observed in different samples (between-sample variance).

H describes confidence intervals of heterogeneity.

Significant differences between samples in meta-analysis.

Detects publication bias in meta-analyses.

Represents the correlation between effect sizes and sample variation.

Visual inspection to funnel plot in Supplemental Figure S5 and radial plot Supplemental Figure S6 showed no significant publication bias, Begg’s rank correlation test (p = .40) confirmed the absence of publication bias. Thus, the trim-and-fill technique was unnecessary to estimate and compensate for the quantity and findings of missing studies. Detailed influence analysis on the effect size and the heterogeneity markers is depicted in Baujat plot in Supplemental Figure S7. Meta-regression analysis revealed that both age (older age) and sex (female) moderated the global prevalence rate of burnout in medical students (p = .01 and .01, respectively).

Global assessment of the components of burnout in medical students

Global assessment of EE in medical students

Supplemental Figure S8 show pooled assessment of the EE component of burnout in medical students. Using all available studies, a random-effects meta-analysis evaluated the prevalence of EE in medical students (K = 18, N = 10,046), and generated a pooled prevalence rate of 38.08% [30.67%; 46.10%], heterogeneity Q = 751.58(17), p < .0001, τ² = .46, τ = .69, I = 97.2%; H = 7.5. Table 2 provide details of the meta-analysis of prevalence of burnout and its components in medical students. A (leave-one-out) sensitivity analysis found that no study had a greater than 1% impact on the global prevalence estimate of EE in medical students.

Global assessment of DP in medical students

Supplemental Figure S9 show pooled assessment of the DP component of burnout in medical students. Using all available studies, a random-effects meta-analysis evaluated the prevalence of burnout in medical students (K = 17, N = 5,644), and generated a pooled prevalence rate of 35.07% [26.74%; 44.41%], heterogeneity Q = 988.50(17), p < .0001, τ² = .93, τ = .96, I² = 98.1%; H = 7.2. Table 2 provide details of the meta-analysis of prevalence of burnout and its components in medical students. A (leave-one-out) sensitivity analysis found that no study had a greater than 1% impact on the global prevalence estimate of DP in medical students.

Global assessment of PA in medical students

Supplemental Figure S10 show pooled assessment of the EE component of burnout in medical students. Using all available studies, a random-effects meta-analysis evaluated the prevalence of burnout in medical students (K = 18, N = 10,046), and generated a pooled prevalence rate of 37.23% [32.66%; 42.05%], heterogeneity Q = 2,267.15(41), p < .0001, τ² = .42, τ = .65, I² = 98.2%; H = 7.5. Table 2 provide details of the meta-analysis of prevalence of burnout and its components in medical students. A (leave-one-out) sensitivity analysis found that no study had a greater than 1% impact on the global prevalence estimate of PA in medical students.

Burnout in medical students by country

Four countries had three or more studies, allowing to perform a subgroup meta-analysis. Detailed prevalence of burnout in medical students by country is presented in Table 3. Results showed that overall pooled prevalence rate of burnout in medical students varied significantly (Q = 2,267.15(41), p < .0001. Supplemental Figure S11 show pooled assessment of burnout in medical students according to country. The USA had a total of nine studies (K = 9, N = 14,320), and generated a pooled prevalence rate of 49.99% [45.12%; 54.86%], I² = 96.5%; H = 7.5. Saudi Arabia had a total of five studies (K = 5, N = 1,779), and generated a pooled prevalence rate of 30.37% [14.35%; 53.15%], I² = 98.6%; H = 7.5. Finally, Iran and Canada had each three studies to estimate burnout in medical students. The overall estimated prevalence rate of burnout in medical students in Iran (K = 3, N = 715) was 44.06% [16.22%; 76.21%], I² = 93.7%; H = 7.5. The overall estimated prevalence rate of burnout in medical students in Canada (K = 3, N = 240) was 27.56% [18.83%; 38.42%], I² = 63.6%; H = 7.5.

Table 3.

A meta-analysis of prevalence of burnout by country in medical students.

Analysis	K	N	Random effects model		Heterogeneity
Analysis	K	N	Pooled results (%) [95% CI]	Forest plot	I^2a (%)	τ	τ^2b	H ^c	Q	Cochran’s Q p-Value^d
USA	9	14,320	49.99 [45.12–54.86]	Supplemental Figure S11	96.5	0.2792	0.0780	7.44	2,267.15 (41)	p < .0001
KSA	5	1,779	30.37 [14.35–53.15]		98.6	1.0791	1.1644	7.44
Iran	3	715	44.06 [16.22–76.21]		93.7	1.0868	1.1811	7.44
Canada	3	240	27.56 [18.83–38.42]		63.6	0.3479	0.1210	7.44

Note. K = number of included studies; N = number of included samples.

I² statistic referred to the percentage of variation across samples due to heterogeneity rather than chance.

τ² describe the extent of variation among the effects observed in different samples (between-sample variance).

H describes confidence intervals of heterogeneity.

Significant differences between samples in meta-analysis.

Burnout in medical students by measure

Six different tools were used to measure the prevalence of burnout in medical students. MBI and CBI were the most frequently used tools. Detailed prevalence of burnout in medical students by measure is presented in Table 4. Supplemental Figure S12 show pooled assessment of burnout in medical students according to country. The highest pooled prevalence rate of burnout in medical students 41.86% [36.39%; 47.54%] was obtained using the MBI measure (K = 32, N = 2,169). The CBI revealed a lower prevalence rate of 34.27% [23.06%; 47.56%] (K = 5, N = 3,733).

Table 4.

A meta-analysis of prevalence of burnout by research measure in medical students.

Analysis	K	N	Random effects model		Heterogeneity
Analysis	K	N	Pooled results (%) [95% CI]	Forest plot	I^2a (%)	τ	τ^2b	H ^c	Q	Cochran’s Q p-Value^d
MBI	32	21,697	41.86 [36.39–47.54]	Supplemental Figure S12	98.3	0.6401	0.4097	7.44	2,267.15 (41)	p < .0001
CBI	5	3,733	34.27 [23.06–47.56]		97.5	0.6190	0.3831	7.44
Others	5	1,484	15.66 [8.46–27.16]		94.3	0.7620	0.5807	7.44

Note. K = the number of included studies; N = number of included samples.

I² statistic referred to the percentage of variation across samples due to heterogeneity rather than chance.

τ² describe the extent of variation among the effects observed in different samples (between-sample variance).

H describes confidence intervals of heterogeneity.

Significant differences between samples in meta-analysis.

Discussion

We aimed through this work to present an updated systematic review and meta-analysis focused on the prevalence and risk factors of burnout among undergraduate medical students. To the best of our knowledge, this is the first study that used a proportional analysis of the pooled burnout data, in order to provide sufficient clarity for our findings. Overall, several notable findings were observed in the present meta-analysis. First, we found a pooled prevalence of burnout among medical students of 37.23%. Second, we found that older and female medical students were affected by burnout to a greater extent than male students. Third, we found differences across countries, with higher prevalence of burnout in US and Iran than other countries.

Our main finding was that the prevalence of burnout among medical students was estimated to be 37.23%, with a pooled prevalence of emotional exhaustion, impaired personal accomplishment, and depersonalization of 38.08%, 36.85%, and 35.07%, respectively. These results are consistent with prior systematic review and meta-analytic findings. Ishak et al. (2013) reviewed literature from 1974 to 2011 and found a prevalence of burnout in medical students ranging between 45% and 71%. Similarly, Fares, Al Tabosh, et al. (2016) found that burnout among different samples of medical students ranged between 27% and 75%. More recently, and based on 24 studies and 17,431 medical students, a previous meta-analysis published in 2019 found a prevalence estimate of 44.2% for burnout, with a higher prevalence for emotional exhaustion (40.8%), followed by depersonalization (35.1%) and personal accomplishment (27.4%). Two major findings emerge from these data. The first one is that a high prevalence of burnout among medical students was consistently reported in existing literature, reflecting high levels of suffering in the preclinical years. The second one is that there is a wide variation in the prevalence rates and thus an enormous level of heterogeneity in studies of burnout among medical students, depending on several factors including instruments and cutoff-criteria used to assess burnout symptomatology (Erschens et al., 2019).

With respect to demographics, we found that an older age of students was significantly associated with higher burnout levels. Previous research has revealed mixed results regarding the effects of age on burnout among medical students. Some studies showed that younger students had higher levels of burnout (Carlotto & Goncalves, 2008; Carlotto et al., 2006), while others rather found no association between age and burnout (Costa et al., 2012). One explanation for our findings might be that medical students’ burnout was consistently found to be lower compared to the general population at the beginning of medical school (Brazeau et al., 2014), increasing with each year of study (Ashkar et al., 2010; Cecil et al., 2014; Chang et al., 2012; Fares, Al Tabosh et al., 2016), and thus to be more prevalent in later stages in the medical curriculum (Ishak et al., 2013).

Another finding of this systematic review was that female medical students were proven to exhibit higher burnout scores than male students. The existing literature and longitudinal studies reported conflicting results regarding female vulnerability to burnout in medical school (Campbell et al., 2010; Dyrbye, Power, et al., 2010; Dyrbye, West, et al., 2021; Hu et al., 2019; Ripp et al., 2011). Recently, Dyrbye, West, et al. (2021) revealed that baseline emotional exhaustion levels were higher, and more likely to be worsened across follow-up interval among females compared to males, even after controlling for potential confounders. Other studies found no statistically significant effect of sex on the prevalence of burnout (Hu et al., 2019; Nteveros et al., 2020). Whereas, a contrary finding emerged from a longitudinal cohort study of 14 126 US medical students, revealing that after adjusting for demographics, baseline burnout scores, mistreatment, and perceptions of the learning environment, women medical students had lower emotional exhaustion and depersonalization scores than males (Dyrbye, Satele, et al., 2021).

We found that burnout among undergraduate medical students was more prevalent in US and Iran as compared to other countries. This was in line with a previous systematic review that found that the prevalence of burnout in medical students before residency was significantly affected by geographic localization, being highly represented in Oceania and the Middle East as compared to other countries (Frajerman et al., 2019). This variation of burnout levels across countries would be multifaceted, with several possible explanations proposed in the literature. Firstly, various lifestyle patterns (such as smoking, drinking alcohol, and sleep patterns) have been shown to be related to burnout in medical students (Cecil et al., 2014; Chunming et al., 2017; Lee et al., 2020). However, previous studies highlighted cross-country differences in lifestyles among medical students. For example, prior studies found that medical students from European countries (such as the United Kingdom, Newbury-Birch et al., 2000) or Ireland, Boland et al., 2006) reported more alcohol use than their Asian counterparts (such as Hong Kong; Lee et al., 2020). Moreover, the prevalence of self-medication for sleep was reported to be higher among medical students from the USA (Webb et al., 2013) as compared to Asian (Lee et al., 2020) and Middle Eastern medical students (Ghandour et al., 2012). Additionally, while family support and closeness were consistently associated with lower burnout levels (Bitran et al., 2019; Chunming et al., 2017; Gil-Calderon et al., 2021; Santen et al., 2010). These factors have been found to vary cross-culturally among students (e.g. more geographical distance and less contact with family in American students than European students; Hall et al., 2017). Finally, it was also suggested that medical education settings, differences in the curriculum itself with stress related to the competition, and various cross-cultural features represent important factors contributing to burnout in medical students (Nteveros et al., 2020). For instance, Fares, Saadeddin, et al. (2016) found that Lebanese preclinical medical students displayed much higher rates of burnout (75%) than those in other countries, and explained their finding by the fact that most AUB medical students work hard under high competition and stress conditions to apply for residency positions in the US. Finally, certain limitations of our meta-analysis have to be discussed. First, some relevant non-English articles might have been excluded according to our inclusion criteria. Second, all studies included in our analysis used self-reporting instruments (mainly MBI), and could be subject to response biases. However, today, these instruments are the most widely used in burnout research, and MBI in particular represents the gold standard instrument for burnout (Dyrbye et al., 2008). Third, the date of the studies included was not taken into account, which means that the possible impact of the COVID-19 pandemic on the prevalence of burnout remains unknown.

Conclusion

This meta-analysis revealed alarming, but not surprising, prevalence rates of burnout among medical students. Burnout in medical students has several negative effects on their mental health, with increased depression levels, sleep deprivation, suicidal ideation, and substance abuse. Burnout in this specific population might also have widespread repercussions on learning, lead to career regret and thoughts of dropping out, and impact future patient care as medical students enter the workforce. We suggest an urgent need for a consensus about definition and measurement properties of burnout to enable a better international comparison of prevalence rates of burnout in different settings.

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Footnotes

Author contribution

Conceptualization, HA, AA, SA, AS; Methodology, HA, AA, SA, AS and HJ; Software, HJ; Validation, FFR; Formal Analysis, HJ; Investigation, FFR and HJ; Resources, HJ; Data Curation, HA, AA, SA, AS FFR and HJ; Writing – Original Draft Preparation, all authors; Writing – Review & Editing, all authors; Visualization, HJ; Supervision, HJ; Project Administration, HJ; Funding Acquisition, Not applicable.

Ethical approval

This review gathered data from studies published and indexed in public databases, thus did not require ethical approval.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Feten Fekih-Romdhane

Haitham Jahrami

Supplemental material

Supplemental material for this article is available online.

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