Burnout among dental students: a systematic review and meta-analysis

Abstract

Objective

Burnout among dental students has been associated with detrimental effects on their mental health and educational outcomes. This systematic review aims to evaluate the prevalence of burnout in this group.

Methods

A comprehensive search was performed in PubMed, Web of Science, Scopus, and Academic Search Complete (EBSCO). The search was restricted to studies published from January 2000 to August 2025. Only studies employing the Maslach Burnout Inventory (MBI) were included. A random-effects model was applied, with prevalence data pooled using the Freeman-Tukey double arcsine transformation. Heterogeneity was assessed using Q and I² statistics.

Results

A total of 3,446 records were retrieved, with 27 studies meeting the inclusion criteria and included in the final analysis. The overall prevalence of burnout in dental students was 26.5% (95% CIs: 0.169–0.360, p < 0.1, I² = 98.2%). For studies employing the MBI-Human Services Survey (MBI-HSS), the pooled mean scores for emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) were 24.693 (95% CIs: 21.723–27.663, p < 0.1, I² = 97.7%), 7.135 (95% CIs: 5.969–8.301, p < 0.1, I² = 96.4%), and 31.949 (95% CIs: 30.661–33.238, p < 0.1, I² = 93.1%), respectively. For studies using the MBI-Student Survey (MBI-SS), the pooled mean scores for emotional exhaustion (EE), cynicism (CY), and professional efficacy (PE) were 16.210 (95% CIs: 13.481–18.939, p < 0.1, I² = 99.5%), 9.600 (95% CIs: 5.662–13.539, p < 0.1, I² = 99.8%), and 16.740 (95% CIs: 10.671–22.809, p < 0.1, I² = 94.1%), respectively.

Conclusion

The results of this meta-analysis found that 26.5% of dental students experienced burnout. This finding reflects substantial psychological distress and highlights the need for preventive measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12909-026-08891-8.

Introduction

Burnout was first described by Freudenberger in 1974 [1] and is a psychological syndrome that arises from long-term exposure to ongoing interpersonal stressors in occupational settings [2]. Building on extensive empirical work, Maslach and colleagues established the classical three-dimensional model of burnout and developed the Maslach Burnout Inventory (MBI), which conceptualizes burnout as comprising Emotional Exhaustion (EE), Depersonalization (DP), and reduced Personal Accomplishment (PA) [3]. EE refers to feelings of emotional depletion and exhaustion resulting from one’s work. DP refers to feelings of being detached, impersonal, and unfeeling toward service recipients. PA refers to feelings of ineffectiveness, incompetence, or lack of achievement in one’s work [4, 5].

To extend this model to academic settings, Schaufeli et al. adapted the MBI and defined student burnout as consisting of three parallel dimensions: Emotional Exhaustion (EE), Cynicism (CY) and reduced Professional Efficacy (PE). EE refers to feelings of being emotionally and physically drained by one’s study. CY refers to feelings of indifference or a distant attitude towards one’s study. PE refers to feelings of competence and successful achievement in one’s study [6]. The pattern of high EE, high DP/CY, and low PA/PE is considered the hallmark of burnout.

Over the past few decades, the risk of developing burnout has been increasingly observed not only among physicians and dentists [7–9], but also among students in human and dental medicine [10, 11]. Among medical students, burnout is particularly prevalent due to multiple stressors such as intensive coursework, demanding clinical rotations, and high professional expectations [12–14]. In contrast, dentistry is a highly practical discipline that involves not only a demanding curriculum but also considerable technical complexity. Moreover, dental patients often expect not only effective treatment, but also excellent aesthetic outcomes, which inevitably increases the communication demands, technical difficulty and the psychological burden on dental students [15, 16]. In addition, clinical dental training typically involves prolonged static postures, extended chairside work, and repetitive micromovements, contributing to musculoskeletal strain and physical fatigue. These unique academic, physical, emotional, and technical demands place dental students at particularly high risk for burnout compared with students in other healthcare programs. For dental students, burnout not only negatively affects personal physical health and academic development [17–20], but may also undermine empathy and professional commitment, thereby compromising future quality of care [21, 22].

Despite increasing attention to burnout as a critical issue in medical professional education, existing evidence regarding its prevalence and characteristics among dental student populations remains fragmented and inconsistent. Individual studies have reported widely varying estimates, likely reflecting differences in study populations, assessment instruments, educational contexts, and methodological approaches. Moreover, although some reviews have examined occupational burnout in dental professional groups, a focused and methodologically rigorous synthesis is still lacking [23, 24].Therefore, a systematic synthesis of the available evidence is essential to clarify the overall burden of burnout, explore potential sources of heterogeneity, and inform evidence‑based educational policies and targeted interventions. The present study aims to systematically review and meta‑analyze the prevalence of burnout and its core dimensions among dental students, while examining factors that may contribute to between‑study variability, thereby providing an evidence-based foundation to guide educational practice and support student well-being in dental training programs.

Methods

The protocol of this review was registered in PROSPERO (CRD420251181975). The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Supplementary Material 1) [25, 26]. Two authors (C-H.M. and L-M.C.) independently carried out all stages of the process, and consensus was achieved for every decision. Any disagreement was settled through discussion with a third investigator (L-L.B.).

Eligibility criteria

The inclusion criteria were established according to the PICOS framework: Participants: Undergraduate and postgraduate students (including residents, master’s, and doctoral students) currently enrolled in formal dental or oral medicine education programs. These programs encompass all major disciplines within dentistry, including, but not limited to endodontics, periodontology, prosthodontics, orthodontics, oral and maxillofacial surgery, pediatric dentistry, and oral pathology. Comparison: Owing to the single-arm design of this analysis, comparative evaluation was not applicable. Outcomes: Studies that used the MBI-HSS or MBI-SS and reported burnout prevalence and/or mean subscale scores (± standard deviation [SD]) were included. Study: Observational studies. In addition, only studies published from 2000 onward were included to improve comparability across studies.

The exclusion criteria for the study were (1) Studies for which the full English text was unavailable; (2) reviews, editorials, conference abstracts, duplicate publications, or case reports; (3) studies including both dental and non-dental students (e.g., medical, nursing, or mixed health professional students) that did not report data for dental students separately; (4) studies using a modified version of the MBI that resulted in different item structures, or studies that did not specify which MBI version was used; and (5) studies using the same data as other included studies.

Search strategy

We conducted a search for studies published from January 1, 2000, to August 18, 2025, across four international databases: PubMed, Web of Science, Scopus, and Academic Search Complete (EBSCO). To enhance comprehensiveness, a supplementary manual search was performed via Google Scholar. The detailed search strategy is shown in Supplementary Material 2. Only studies published after 2000 were included because medical education systems, curricular structures, and clinical training environments have undergone substantial changes over the past three decades. To improve comparability across studies and reduce structural heterogeneity, earlier publications were therefore excluded.

Literature screening

All records retrieved from database and manual searches were imported into EndNote 20 (Clarivate Analytics, Philadelphia, PA, USA). Duplicate entries were systematically eliminated. Two authors then independently screened titles and abstracts within EndNote 20 to identify potentially relevant studies. If the title or abstract lacked sufficient detail to allow a clear assessment, the record was kept for full-text examination. The same two authors conducted a comprehensive full-text examination of the preliminarily screened studies to determine those included.

Data extraction

Data from the eligible studies were systematically extracted and organized into a predesigned table. The variables extracted included: (1) first author; (2) year; (3) study design; (4) country; (5) instruments for burnout assessment; (6) number of students; (7) prevalence estimates of burnout; (8) mean subscale scores (± SD); and (9) burnout associated factors.

During data extraction, this review additionally recorded whether each included study conducted multivariable analyses. When both univariable and multivariable results were reported, the multivariable findings were prioritized for extraction in accordance with methodological standards for systematic reviews.

Quality assessment

The Joanna Briggs Institute (JBI) analytical cross sectional studies appraisal tool comprises eight items aimed at evaluating internal validity and risk of bias in analytical cross sectional studies [27]. Included studies were assessed according to these items, with possible ratings of “yes,” “no,” “unclear,” or “not applicable.” Overall risk of bias was categorized as low (all “yes”), high (at least one “no”), or unclear (one or more “unclear”) [28].

Statistical analysis

For all included studies using the MBI, response scores were treated as a 7-point Likert scale. If a study used a different scoring range, scores were linearly converted to the 0–6 scale to ensure comparability across studies (conversion formula provided in Supplementary Material 3).

All statistical analyses were conducted in STATA 18.0 (StataCorp LLC, USA), employing the metan and metaprop packages. A random-effects model was used due to anticipated heterogeneity across studies. The Freeman–Tukey double arcsine transformation was applied to stabilize variances when pooling burnout prevalence estimates, and 95% confidence intervals (CIs) were calculated accordingly.

For studies utilizing the MBI that reported mean scores with 95% CIs for its subscales, standard errors were derived from the confidence intervals and pooled mean scores were calculated using a random-effects model.

Heterogeneity among studies was evaluated using Cochran’s Q test and the I² statistic. Sensitivity analyses were performed to evaluate the robustness of the findings. Subgroup and regression analyses were conducted only when a sufficient number of studies were available to allow meaningful comparison groups.

Result

Study selection

A total of 3,454 records were initially identified through preliminary searches. After removing 1,172 duplicate records, 2,282 unique records were screened by title and abstract. Ultimately, 86 records proceeded to full-text screening, of which 27 studies were included in the analysis, and 59 were excluded for the following reasons: burnout not assessed using the MBI-HSS or MBI-SS (n = 23); outcome data not applicable or insufficient for analysis (n = 17); full text not available in English (n = 11); use of a modified version of the MBI-HSS/MBI-SS or unspecified MBI version without provision of questionnaire items (n = 7); and duplication of data with other included studies (n = 1). The detailed selection process is shown in Fig. 1.

Fig. 1.

Workflow of literature screening and study selection

Study characteristics

A total of 27 studies were included in the meta-analysis, comprising 11,433 dental students (range: 30–5,647) [29–55]. Studies were conducted in Asia (n = 14, 51.9%), Europe (n = 6, 22.2%), South America (n = 4, 14.8%), and North America (n = 3, 11.1%). All 27 studies were cross-sectional studies. Among the included studies, 21 focused on dental undergraduate students, while 6 targeted postgraduate students. Regarding the assessment tools of burnout, 16 studies employed the MBI-HSS to assess burnout among dental students, 10 studies utilized the MBI-SS, and 1 study applied both instruments to evaluate students from different academic years separately. Table 1 provides a summary of the study characteristics.

Table 1.

Study characteristics

First author	Year	Country	Study design	Sample size	Education level	Burnout assessment tool
L Yang	2022	China	Cross-sectional	558	Postgraduate	MBI-HSS
PM Rivera	2023	Chile	Cross-sectional	121	Undergraduate	MBI-HSS
AE Ramírez-López	2024	Peru	Cross-sectional	154	Undergraduate	MBI-SS
GH Kwak	2024	Korea	Cross-sectional	156	Undergraduate	MBI-HSS
EJ Kwak	2021	Korea	Cross-sectional	112	Undergraduate	MBI-HSS
F Galán	2014	Spain	Cross-sectional	208	Undergraduate	MBI-HSS, MBI-SS
H Eren	2016	Turkey	Cross-sectional	458	Undergraduate	MBI-SS
S Bhattacharyya	2023	India	Cross-sectional	400	Undergraduate	MBI-SS
JL Jiménez-Ortiz	2019	Mexico	Cross-sectional	73	Undergraduate	MBI-SS
A Frajerman	2022	France	Cross-sectional	95	Undergraduate	MBI-SS
AC Mafla	2015	Colombia	Cross-sectional	5647	Undergraduate	MBI-SS
P Prinz	2012	Germany	Cross-sectional	73	Undergraduate	MBI-HSS
BB Partido	2020	US	Cross-sectional	57	Undergraduate	MBI-HSS
Z Mohammad	2021	Saudi Arabia	Cross-sectional	264	Undergraduate	MBI-HSS
DA Groneberg	2015	Germany	Cross-sectional	239	Undergraduate	MBI-HSS
C Korkmaz	2023	Turkey	Cross-sectional	211	Undergraduate	MBI-HSS
A Joseph	2023	US	Cross-sectional	631	Postgraduate	MBI-HSS
K Divaris	2012	Swiss	Cross-sectional	36	Postgraduate	MBI-HSS
LA Chauca Bajaña	2023	Ecuador	Cross-sectional	300	Undergraduate	MBI-HSS
DH Badran	2010	Jordan	Cross-sectional	307	Undergraduate	MBI-HSS
K Divaris	2012	Greece	Cross-sectional	99	Postgraduate	MBI-HSS
A Shetty	2015	India	Cross-sectional	82	Postgraduate	MBI-HSS
S Ghafoor	2018	Pakistan	Cross-sectional	30	Postgraduate	MBI-HSS
A Nakhostin-Ansari	2022	Iran	Cross-sectional	201	Undergraduate	MBI-SS
KC Pentapati	2018	India	Cross-sectional	159	Undergraduate	MBI-SS
M Balkis	2015	Turkey	Cross-sectional	329	Undergraduate	MBI-SS
I AlShahrani	2022	Saudi Arabia	Cross-sectional	433	Undergraduate	MBI-SS

Overall prevalence of burnout

Pooled data were derived from 12 studies reporting burnout, including a total of 8,141 students [31, 34, 36–39, 41, 44–46, 50, 51]. Among these studies, four employed the MBI-HSS to assess burnout [31, 38, 44, 50], seven employed the MBI-SS [34, 36, 37, 39, 45, 46, 51], and one study employed both instruments to evaluate students from different academic years (MBI-SS for second-year students and MBI-HSS for fourth- and fifth-year students) [41].

Across the 12 studies, the diagnostic criteria for burnout varied considerably. Most studies defined burnout based on the three subscale dimensions. Nine studies provided explicit cutoff values for the three subscales, although the specific cutoff values differed across studies [31, 37, 38, 41, 44–46, 50, 51]. One study classified high versus low burnout using one SD above or below the mean [34], another applied upper/lower tertiles as thresholds [36], and one study did not report any diagnostic criteria and only provided a descriptive definition [39].

Variation was also observed in the dimensional criteria used to define burnout. Among the nine studies that reported explicit cutoffs, four adopted a three-dimensional criterion [31, 44–46], three adopted a one-dimensional criterion [38, 41, 50], and two reported both two-dimensional and three-dimensional criteria when presenting prevalence estimates [37, 51]. For studies presenting both two- and three-dimensional criteria, the three-dimensional criterion was selected as the primary indicator for the meta-analysis. Given these sources of heterogeneity, the included studies reporting prevalence were categorized into two groups. Type 1 (Defined-Criteria Group) comprised studies that reported explicit subscale cutoff values and employed a three-dimensional diagnostic criterion [31, 37, 44–46, 51]. Type 2 (Mixed/Undefined Criteria Group) included the remaining studies, such as those that did not provide cutoff values or applied one-dimensional criteria [34, 36, 38, 39, 41, 50].

The pooled prevalence of burnout among dental students was 26.5% (95% CIs: 0.169–0.360, p < 0.1, I² = 98.2%; Fig. 2A). The sensitivity analysis confirmed the robustness of the meta-analysis findings (Fig. 2B).

Fig. 2.

Meta-analysis of burnout prevalence among dental students and the corresponding sensitivity analysis

To explore the sources of heterogeneity, subgroup analyses were conducted according to the predefined study classification, data collection period (pre-pandemic vs. pandemic/post-pandemic period), country income level (high-income economies vs. non-high-income economies), and assessment instrument. No statistically significant subgroup differences were observed (Supplementary Material 4).

Levels of the burnout subscales

Twelve studies that assessed burnout using the MBI-HSS reported burnout based on the mean and SD of the EE, DP, and PA subscale scores [29, 32, 33, 35, 40, 42, 43, 47, 49, 52–54]. In the EE, the random-effects estimate of the mean score was 24.693 (95% CIs: 21.723–27.663, p < 0.1, I² = 97.7%; Fig. 3A). The DP was 7.135 (95% CIs: 5.969–8.301, p < 0.1, I² = 96.4%; Fig. 3C) and, the PA was 31.949 (95% CIs: 30.661–33.238, p < 0.1, I² = 93.1%; Fig. 3E). The sensitivity analysis confirmed the robustness of the meta-analysis findings (Fig. 3B and D, and 3F).

Fig. 3.

A&B The mean score of EE and the corresponding sensitivity analysis results. C&D The mean score of DP and the corresponding sensitivity analysis results. E&F The mean score of PA and the corresponding sensitivity analysis results

Eight studies that assessed burnout using the MBI-SS reported burnout based on the mean and SD of the EE, CY, and PE subscale scores [30, 34, 36, 37, 45, 48, 51, 55]. In the EE, the random-effects estimate of the mean score was 16.210 (95% CIs: 13.481–18.939, p < 0.1, I² = 99.5%; Fig. 4A). The CY was 9.600 (95% CIs: 5.662–13.539, p < 0.1, I² = 99.8%; Fig. 4C) and, the PE was 16.740 (95% CIs: 10.671–22.809, p < 0.1, I² = 94.1%; Fig. 4E). The sensitivity analysis confirmed the robustness of the meta-analysis findings (Fig. 4B and D, and 4F).

Fig. 4.

A&B The mean score of EE and the corresponding sensitivity analysis results. C&D The mean score of CY and the corresponding sensitivity analysis results. E&F The mean score of PE and the corresponding sensitivity analysis results

Associated factors of burnout

Twenty-two studies investigated factors associated with burnout among dental students (Table 2) [29–55]. Of these, seven studies conducted multivariable regression analyses to identify correlates of burnout [36, 37, 43, 49–51, 54].

Table 2.

Factors associated with burnout and the subscales

Author, year	Burnout assessment tool	Multivariable analysis	Burnout	EE	DP/CY	PA/PE
L Yang, 2022 [50]	MBI-HSS	YES	Depressive symptoms (OR = 5.38, p < 0.001); Career choice regret (OR = 1.82, p < 0.01)
S Ghafoor, 2018 [35]	MBI-HSS	NO		Marital status: single (“I feel frustrated with my studies”, p = 0.028), single (“I feel like I’m at the end of my rope”, p = 0.049)		Marital status: married (“I can easily understand how my recipients feel about things”, p = 0.012):
PM Rivera, 2023 [38]	MBI-HSS	NO		Gender: female > male (p = 0.026)	Gender: male > female (p = 0.0046)	Academic level: clinical (p = 0.0009)
P Prinz, 2012 [40]	MBI-HSS	NO	Gender: female (F = 2.558, p = 0.008); Degree course: dental (F = 4.653, p < 0.001); Coping strategies: dysfunctional coping (F = 5.279–17.159, p = 0.024 - p < 0.001)	Semester (F = 8.929, p < 0.001);	Semesters (F = 4.164, p = 0.018)
BB Partido, 2020 [49]	MBI-HSS	YES		Empathy (r = -0.295, p < 0.05); Empathy (B = -2.777, p = 0.039), Self-awareness (B = 2.473, p = 0.039)	Empathy (B = -1.189, p = 0.048), Self-awareness (B = 1.241, p = 0.048), Year of study (B = -5.146, p = 0.048)	Empathy (B = 1.130, p = 0.025)
Z Mohammad, 2021 [42]	MBI-HSS	NO	Age, gender, and marital status: no significant associations
AC Mafla, 2015 [36]	MBI-SS	YES	Year of study: 4th (vs. 1st, OR = 1.76, p = 0.02); Institution type: Private ( vs. Public, OR = 0.59, p = 0.04); Class size: 30–60 (vs. < 30, OR = 2.01, p = 0.02); Passed courses: Yes (vs. No, OR = 0.64, p < 0.001); First career choice: Yes (vs. No, OR = 0.78, p = 0.03); Workload stress (OR = 1.62, p < 0.001); Self-efficacy stress (OR = 1.43, p < 0.001)
DA Groneberg, 2015 [47]	MBI-HSS	NO		Gender: female > male (p < 0.05); Meeting friends (r = -0.16, p < 0.01); Exercising (r = -0.11, p < 0.05); Taking sedatives (r = 0.17, p < 0.01)	Gender: male > female (p < 0.05); Meeting friends (r = -0.11, p < 0.05); Exercising (r = -0.10, p < 0.05); Taking sedatives (r = 0.12, p < 0.05); Taking cannabis/marijuana (r = 0.10, p < 0.05)	Taking performance enhancing stimulants (r = 0.15, p < 0.01)
GH Kwak, 2024 [44]	MBI-HSS	NO		Year of study (4th > 3rd, p < 0.0001); COVID-19 stress (IES-R) (Abnormal > Normal, p = 0.0232)	Year of study (4th > 3rd, p < 0.0001); COVID-19 stress (IES-R) (Abnormal > Normal, p = 0.0043)
EJ Kwak, 2021 [31]	MBI-HSS	NO		Academic workload: increasing (p = 0.004); Depressive symptoms (PHQ-9) (r = 0.625, p < 0.001); Satisfaction with dental education: lower in high-EE group (p < 0.001); Need for counseling: higher in high-EE group (p < 0.001)	Academic workload: increasing (p = 0.012); Depressive symptoms (PHQ-9) (r = 0.463, p < 0.001); Satisfaction with dental education: lower in high-DP group (p = 0.021); Need for counseling: higher in high-DP group (p = 0.001)	Depressive symptoms (PHQ-9) (r = -0.208, p = 0.028); Satisfaction with dental education: lower in Low-PA group (p < 0.001)
KC Pentapati, 2018 [37]	MBI-SS	YES		Year of study: 3rd (vs. 2nd, RR = 1.28, p < 0.001), 4th (vs. 2nd, RR = 1.22, p = 0.022)	Year of study: 3rd (vs. 2nd, RR = 1.83, p < 0.001), 4th (vs. 2nd, RR = 1.88, p < 0.001); Gender: female (RR = 0.80, p = 0.036); Age (RR = 0.86, p = 0.001)	Year of study: 3rd (vs. 2nd, RR = 1.79, p < 0.001), 4th (vs. 2nd, RR = 1.87, p < 0.001); Gender: female (RR = 0.73, p < 0.001); Age (RR = 0.88, p < 0.001)
C Korkmaz, 2023 [32]	MBI-HSS	NO*	Perceived Stress (PSS-10) (B = 0.956, p < 0.001)	Gender: female > male (p = 0.031); Perceived Stress (PSS-10) (r = 0.418, p < 0.001); Sense of Coherence (SoC-13) (r = -0.243, p < 0.001); Perceived Social Support (MSPSS) (r = -0.195, p = 0.004)	Perceived Stress (PSS-10) (r = 0.230, p = 0.001); Perceived Social Support (MSPSS) (r = -0.139, p = 0.044)	Gender: female < male (p = 0.03); Sense of Coherence (SoC-13) (r = 0.233, p = 0.001); Perceived Social Support (MSPSS) (r = 0.339, p < 0.001);Willingness to choose dentistry (willingly < unwillingly, p = 0.016); Family type (extended < nuclear < blended, p = 0.037)
A Joseph, 2023 [43]	MBI-HSS	YES		Gender: female (β = 0.18, p < 0.001); Age: 30–39 < 20–29 (β = -0.23, p = 0.004); Year of study: 3rd > 1st (β = 0.28, p = 0.009), 4th > 1st (β = 0.40, p = 0.009); Region: overall effect (p = 0.015); Non-patient work hours: increasing (p < 0.001)	Gender: female (β = -0.26, p = 0.008); Age: 30–39 < 20–29 (β = -0.47, p = 0.002); Year of study: 2nd > 1st (β = 0.40, p < 0.001), 3rd > 1st (β = 1.06, p < 0.001), 4th > 1st (β = 1.31, p < 0.001); Non-patient work hours: increasing (p = 0.004)	Race/Ethnicity: African American/Black (Non-Hispanic) (β = -0.13, p = 0.005), Asian/Pacific Islander (β = -0.08, p = 0.005)
F Galán, 2014 [41]	MBI-HSS/MBI-SS	NO	Year: 4th > 5th (p = 0.003); Depression (p < 0.001)	Year of study: 2nd > 5th (p = 0.019); 4th > 5th (p < 0.001)		Year of study: 4th > 2nd (p = 0.003); 4th > 5th (p = 0.05)
H Eren, 2016 [34]	MBI-SS	NO		Age: decreasing (p = 0.002); Gender: female > male (p = 0.04); Accommodation: alone > residence or family (p = 0.001); Year of study: increasing (p = 0.02); Occupational Performance (r = -0.712, p < 0.05); Occupational Satisfaction (r = -0.685, p < 0.05)	Occupational Performance (r = -0.624, p < 0.05); Occupational Satisfaction (r = -0.592, p < 0.05)	Year of study: decreasing (p = 0.002); Occupational Performance (r = 0.842, p < 0.05); Occupational Satisfaction (r = 0.794, p < 0.05)
S Bhattacharyya, 2023 [51]	MBI-SS	YES		Extra-curricular activities (ECA) participation (RR = 1.38, p = 0.028) **	ECA participation (RR = 2.12, p = 0.001); “ECA helps skill enhancement” (RR = 1.40, 95%CI = 1.22–1.89) **	ECA participation (RR = 1.53, p < 0.05) **
LA Chauca Bajaña, 2023 [51]	MBI-HSS	NO		Marital status (p = 0.041); Semester (p = 0.011); Multiple symptoms incl. sleeping issues, tension, etc. (all p < 0.001)	Multiple symptoms incl. sleeping issues, tension, etc. (all p < 0.05)	Marital status (p < 0.001); Multiple symptoms incl. sleeping issues, tension, etc. (all p < 0.05)
DH Badran, 2010 [33]	MBI-HSS	NO		Gender: female > male (p < 0.05); Year of study and college: 4th UJ > 4th JUST (p < 0.001); Year of study: 4th UJ > 5th UJ (p < 0.001)	Year of study: 5th > 4th (p = 0.005)	Year of study and college: 5th UJ < 4th UJ (p < 0.001); 5th UJ < 5th JUST (p = 0.014)
M Balkis, 2015 [55]	MBI-SS	NO		Gender: female > male (p < 0.001); Year of study: 3rd > 1st, 2nd (p < 0.001); Academic workload (β* = 0.27, p < 0.001)	EE (β* = 0.55, p < 0.001); Academic workload (indirect effect)	Year of study: 1st > 3rd (p < 0.05); Accommodation (away > with family, p < 0.01); CY (β* = 0.55, p < 0.001)
I AlShahrani, 2022 [48]	MBI-SS	NO		Year of study: increasing (p = 0.0005); Medication use: yes (> no, p = 0.0001); Academic performance: poor (> good, p = 0.0001); Teacher performance: poor (> good, p = 0.0001); Course expectation: better (> worse, p = 0.0001); staying alone (> with family/friends, p = 0.0339); Thought of quitting the course: yes (> no, p = 0.0001)	Year of study: increasing (p = 0.0001); First-choice dentistry: no (> yes, p = 0.002); Medication use: yes (> no, p = 0.0001); Academic performance: poor (> good, p = 0.0001); Teacher performance: poor (> good, p = 0.0001); Course expectation: worse (> better, p = 0.0001); Infrastructure: poor (> better, p = 0.0161); Thought of quitting the course: yes (> no, p = 0.0001)	Year of study: decreasing (p = 0.0259); First-choice dentistry: no (> yes, p = 0.007); Teacher performance: poor (> good, p = 0.0001); Academic performance: poor (> good, p = 0.0001); Course expectation: worse (> better, p = 0.0001); Thought of quitting the course: yes (> no, p = 0.0001)
K Divaris, 2012 [54]	MBI-HSS	YES		Perceived academic stress (GDES-A) (β = 8.6, p < 0.05); Perceived clinical stress (GDES-C) (β = 6.7, p < 0.05); Independent practice (β = -4.4, p = 0.02); Age ≥ 30 (β = -4.9, p < 0.05)	Perceived clinical stress (GDES-C) (β = 1.8, p < 0.05); Independent practice (β = -1.8, p = 0.01)	Perceived clinical stress (GDES-C) (β = -4.8, p < 0.05); Independent practice (β = 3.4, p = 0.15)

UJ University of Jordan, JUST Jordan University of Science and Technology

NO* A multivariable regression analysis was performed for non-burnout-related factors

β* Standardized path coefficient

** Unadjusted mean differences and regression results showed inconsistent directions, likely due to highly unbalanced group sizes (201 vs. 7) and model limitations

Most studies reported demographic factors (gender, age, year of study, accommodation, marital status, and region) associated with burnout among dental students. Across the included studies, single status, female gender, higher academic year, younger age, and living alone were associated with higher levels of EE [33–35, 37, 38, 43, 44, 47, 48, 55]. However, some studies found that lower-year students exhibited higher EE levels [33, 41]. Male gender, higher academic year, and younger age were associated with greater DP/CY [33, 38, 43, 44, 47, 48]. Marital status, female gender, academic year, older age, larger household size, living arrangement, and certain ethnic backgrounds were related to PA/PE [29, 32–35, 37, 41, 55].

Burnout was also found to be linked with various psychological factors. Depressive symptoms and perceived stress were consistently linked to increased burnout or its components [31, 32, 50, 54]. Students with higher empathy exhibited lower levels of EE and CY/DP, and higher levels of PA/PE, whereas those with greater self-awareness demonstrated higher EE and CY/DP [49]. Students who were more psychologically affected by COVID-19, those expressing a need for counseling, and those reporting thoughts of dropping out showed a tendency toward higher EE and CY/DP levels [31, 44, 48].

Educational factors were associated with burnout. Lower satisfaction with dental education, heavier academic workload, poorer academic performance, poorer teacher performance, and lower-than-expected or unmet course expectations were associated with higher EE and CY/DP [31, 43, 48, 55]. Additionally, having dentistry as a non-first-choice major and dissatisfaction with teaching facilities or materials were positively associated with DP [32].

Physical and behavioral factors were likewise related to burnout. Multiple physical discomfort symptoms were associated with burnout [29]. Poor professional performance and lower professional satisfaction were correlated with higher EE and CY/DP [34]. Conversely, meeting with friends, engaging in physical exercise, and independent practice were negatively associated with EE and CY/DP [45, 47]. Substance abuse was positively associated with EE and CY/DP [47, 48]. Notably, the use of performance enhancing stimulants was positively associated with PA, suggesting that different substances may exert distinct effects on burnout among students [47].

Risk of bias

Assessment of bias risk in the studies included was conducted using the JBI analytical cross sectional studies appraisal tool [27]. This tool comprises eight key questions that evaluate internal validity and the potential for bias in cross-sectional designs.

More than half of the included studies exhibited inadequate identification or insufficient management of confounding factors, leading to their classification as having a high risk of bias [29, 31–35, 38, 39, 41, 42, 44, 47, 48, 51, 53, 54]. Details of the risk of bias for each study are summarized in Supplementary Material 5.

Discussion

Interpretation

This systematic review and meta-analysis synthesized 27 studies that assessed burnout among dental students using the MBI. The pooled prevalence of burnout was 26.5%. This estimate is broadly comparable to those reported in previous single-country or single-center studies, although considerable variation remains in the specific values. Pomareda-Lago et al. reported that burnout prevalence among undergraduate dental students ranged from 7% to 41.3% [23]. Recent multicenter studies from various regions also indicate that burnout is widespread among dental students, with prevalence commonly falling within the moderate to high range [56–58]. Collectively, these findings suggest that burnout represents a significant and widespread concern within dental education globally.

From a broader perspective, burnout prevalence is high among university students in general and is particularly pronounced among those in medical and health-related programs [4, 59–62]. Compared with other student groups, dental students are required to engage in intensive technical training and clinical patient care early in their curriculum, which exposes them to substantial academic, technical, and communication-related stress within limited time frames [63]. Moro et al. reported a 13% burnout prevalence among dentists [64]. Together with the findings of the present study, this suggests that burnout may emerge early in training and remain prevalent across different stages of the dental profession. These observations underscore the need for early recognition and intervention in dental education.

Our study also summarized the three subscale scores of the MBI-HSS and MBI-SS. Overall, dental students showed moderate to high levels of EE and DP/CY, while their PA/PE scores were relatively low. Notably, the mean PA/PE scores did not fall into the very low range. This may suggest that, although students experience substantial EE and DP/CY in high-pressure environments, they have not entirely lost confidence in their abilities or future professional development. This pattern highlights the urgency of timely intervention while also indicating that meaningful improvements remain possible at both educational and institutional levels.

Our study further summarized burnout associated factors from 22 included studies, emphasizing multivariable evidence. These associations were synthesized narratively based on multivariable findings reported in individual studies, rather than derived from pooled quantitative estimates. Overall, burnout among dental students appears to result from the combined influence of demographic, psychological, educational, behavioral, and lifestyle factors.

Regarding demographic factors, being female, in higher academic years, or younger in age was frequently linked to higher EE or DP/CY [33–35, 37, 38, 43, 44, 47, 48, 55]. These findings align with research in broader university and medical student populations, where female and younger students generally report greater emotional strain and psychological distress. Meanwhile, students in senior years or those who are younger often experience the dual pressures of early clinical exposure and career decision-making, which may render them more susceptible to EE and CY [65–67]. However, a few studies reported higher EE in junior students, possibly due to challenges in adapting to the dental program or sudden increases in academic demands. This suggests that burnout risk may peak at different stages of dental training [33, 41]. Psychological factors represent key determinants of burnout. Empathy, depressive symptoms, and perceived pressure have all been associated with burnout severity [32, 41, 49, 50, 54]. Anishchuk et al. reported that cognitive empathy tends to be negatively associated with burnout and has a particularly strong protective effect against emotional exhaustion [22]. Zhang et al. found that among university students, perceived academic pressure not only directly predicts academic burnout but also indirectly increases burnout through two mediating pathways involving reduced perceived social support and diminished self-esteem [68]. Alhilali et al. further showed that higher levels of depression, anxiety, and pressure are significantly linked to all three dimensions of burnout, which is consistent with our identification of depressive symptoms and perceived pressure as robust risk factors [63].

Educational factors also play a critical role in the development of burnout among dental students. Consistent with previous research [69, 70], our study found that high academic workload and poor academic performance were associated with higher EE and CY/DP and lower PA/PE [31, 43, 48, 55]. In addition, students who did not select dentistry as their first-choice major showed elevated DP/CY scores, suggesting that inadequate professional motivation may reduce students’ resilience to pressure [32].

Consistent with previous research, various physical complaints and unhealthy lifestyle behaviors were significantly associated with burnout [71–73]. The use of substances such as sedatives was generally associated with higher EE and CY/DP, whereas some studies suggested that certain stimulants were linked to higher PA. This may reflect compensatory behaviors adopted by students to maintain short-term academic performance [47]. In contrast, regular exercise and social activities were associated with lower burnout levels, suggesting a protective effect of active and healthy lifestyles.

Possible sources of heterogeneity and limitations

High statistical heterogeneity was observed in both the pooled prevalence and the subscale scores. Although subgroup analyses comparing diagnostic criteria, MBI versions, country income levels, and pre-pandemic versus during/post-pandemic periods were conducted, none of the subgroup differences reached statistical significance. This suggests that the sources of heterogeneity are likely to be multifactorial and complex and not fully captured by the available subgroup variables.

Differences in diagnostic criteria may represent an important contributor. The included studies used various cut-off definitions, including clinical thresholds based on three-dimension or one-dimension criteria, and percentile-based methods. Evidence shows that even when using the same MBI version, different cut-off definitions can lead to several-fold differences in burnout prevalence [23]. Cultural and educational differences may also contribute to heterogeneity [74]. In addition, the impact of COVID-19 on the mental health of university and medical students must be considered. Several studies have reported marked increases in student burnout during the pandemic, with dental and medical students particularly vulnerable due to remote learning, disruption of clinical training, and uncertainty about future employment [44, 75, 76].

Several key issues still require attention in this study. First, all included studies employed cross-sectional designs. The directional relationships between burnout and variables such as depression, perceived pressure, and academic performance cannot be established without prospective cohort data. Second, the assessment of burnout relied entirely on self-reported measures, which may be influenced by social desirability and recall bias. Third, as noted earlier, despite the use of random-effects models and sensitivity analyses, the substantial heterogeneity in the results remains difficult to ignore. Fourth, many studies had limitations in the identification and control of confounders. According to the JBI appraisal, a considerable proportion of the included studies presented a high risk of bias, which weakens the strength of evidence regarding associated factors. In addition, research from several regions (such as Africa, Eastern Europe, and Oceania) remains scarce, limiting the geographical representativeness of the current evidence. Finally, only English-language publications were included due to language constraints, which could have resulted in the exclusion of relevant studies.

Implications

The findings of this review indicate that burnout among dental students is not a single-dimensional issue but a complex phenomenon shaped by individual, educational, and institutional factors. Accordingly, intervention strategies should be implemented across the individual, curriculum, and organizational levels.

At the individual level, psychological screening, emotional regulation training, and mindfulness-based programs may help students improve their ability to recognize and cope with stress. Existing evidence suggests that mindfulness-based interventions may help reduce levels of stress, depression, and burnout [77, 78]. In addition, fostering cognitive empathy may help reduce emotional exhaustion while maintaining effective communication with patients [22]. At the curriculum level, dental schools may consider adjusting the scheduling of coursework and clinical training to avoid clustering them within short periods. At the institutional level, universities and teaching hospitals should approach student burnout from the perspective of organizational well-being and establish more robust psychological support systems and accessible pathways for help-seeking.

Future research should address several key areas. First, multicenter prospective cohort studies are required to follow dental students across academic years and to include objective indicators such as absenteeism, academic performance and help-seeking records to enhance ecological validity. Second, structural equation modeling and longitudinal cross-lagged models should be used to explore causal links between perceived pressure, emotion regulation, self-esteem, social support and burnout, which may help identify key intervention targets. Third, more comparative studies across countries and cultural contexts are needed to examine how educational systems and cultural values shape burnout patterns. Finally, high-quality randomized controlled trials are necessary to evaluate the effects of mindfulness training, empathy and communication courses, resilience programs and institutional reforms on reducing burnout among dental students.

Conclusion

This systematic review and meta-analysis offers a detailed summary of burnout in dental students across the globe. The pooled prevalence of 26.5%, together with consistently elevated EE and DP/CY scores and comparatively low PA/PE levels, suggests that burnout is already evident during dental training. Multiple demographic, psychological, educational, and behavioral factors have been associated with this burden, highlighting the multifactorial nature of burnout in this population. The substantial heterogeneity across studies also reflects variations in diagnostic criteria, educational contexts, and pandemic-related influences. Given the wide range of adverse consequences associated with burnout, addressing it at an early stage will be critical for supporting the well-being and professional development of future dental practitioners.

Abbreviations

MBI

Maslach Burnout Inventory

MBI-HSS

Maslach Burnout Inventory-Human Services Survey

MBI-SS

Maslach Burnout Inventory-Student Survey

EE

Emotional exhaustion

DP

Depersonalization

PA

Personal accomplishment

CY

Cynicism

PE

Professional efficacy

CIs

Confidence intervals

SD

Standard deviation

Authors’ contributions

C-H.M. and L-M.C.: Investigation, Methodology, Conceptualization, Writing - Original Draft, Writing - Review & Editing. H-Y.L., K.Z. and Y-F.Y.: Investigation, Methodology, Conceptualization, Writing – Review & Editing. Z-Z.L. and G-R.W.: Investigation, Methodology, Writing – Review & Editing. Y.X.: Writing - Review & Editing, Conceptualization. Z-Q. Z.: Writing - Review & Editing. B.L.: Supervision, Writing - Review & Editing. S-S.L.: Supervision, Writing - Review & Editing. M.H.: Supervision, Writing - Review & Editing. L-L.B.: Conceptualization, Supervision, Project Administration, Funding Acquisition, Writing - Review & Editing. All authors have reviewed and approved the final version of this manuscript for publication. Each author agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

This work was supported by the Medical Young Talents Program of Hubei Province; and Wuhan Young Medical Talents Training Project to L.-L. Bu; the Degree and Graduate Education Reform Research Project of Wuhan University (2024); the Professional Degree Graduate Education Comprehensive Reform Project of Wuhan University (2025) to M H; the Educational Research Project of Medical Department of Wuhan University (2024YB47) to SS L

The funders had no influence on the design or conduct of the study and was not involved in data collection, data analysis, data interpretation, or in the writing of the manuscript. Funding is used to support potential article processing charge for the publication.

Data availability

All data supporting the conclusions of this study have been presented in the main text or Supplement.

Declarations

Ethics approval and consent to participate

No ethical approval was required for this study.

Competing interests

The authors declare no competing interests.