A Rasch Analysis Validation of the Maslach Burnout Inventory–Student Survey with Preclinical Medical Students

Shi, Yang; Gugiu, P. Cristian; Crowe, Remle P.; Way, David P.

doi:10.1080/10401334.2018.1523010

Cited by 33 publications

(30 citation statements)

References 41 publications

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“…Signi cance between burnout versus non-burnout students was noted for all MBI-SS items, subscales, two-and three-dimensional models. Internal consistency measured using Cronbach's Alpha was equal or higher than previous burnout studies using the MBI-SS (15,27,28). Several MBI surveys are available to measure burnout in different populations.…”

Section: Mbi-ss Prediction Of Burnout In Medical Studentsmentioning

confidence: 63%

“…The survey (see appendix) was a mix of demographic, career-oriented, medical specialty, MBI-SS, and SMMS questions. The 16-item MBI-SS is a validated tool used to measure burnout in student populations (15). The MBI-SS (Mind Garden Inc., Menlo Park, CA) consists of three subscales to evaluate the different domains of burnout: emotional exhaustion (i.e.…”

Section: Methodsmentioning

confidence: 99%

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Assessment of burnout in medical students using the Maslach Burnout Inventory-Student Survey: A cross-sectional data analysis

Obregon

Luo

Shelton

et al. 2020

Preprint

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Background: Medical student burnout can cause emotional and physical exhaustion and detachment. The objectives of this study were to evaluate burnout using the Maslach Burnout Inventory-Student Survey (MBI-SS), identify factors that may predict burnout, and assess wellness initiatives effectiveness at reducing burnout. Methods: The MBI-SS was administered to all medical students (Classes 2019 to 2022) at the University of Illinois College of Medicine (UICOM) from February to May 2019. Factor analysis and internal consistency of the MBI-SS were assessed. Mean MSBI-SS subscale scores for burnout were calculated for cynicism (CY), emotional exhaustion (EE), and academic efficacy (AE). Multiple regression analysis was used to identify student factors that may predict burnout.Results: A total of 273 (21.6%) UICOM students completed the survey and 110 (40.3%) respondents reported self-perceived burnout. MBI-SS subscale scores were significantly higher for CY and EE, and significantly lower for AE in students who reported suffering from burnout versus students who did not report burnout. Mean ± SD subscale scores for CY, EE, and AE in burnout students were 14.44 ± 5.59, 23.23 ± 4.74, and 24.81 ± 5.35, respectively. In comparison, mean ± SD subscale scores for CY, EE, and AE in non-burnout students were 7.59 ± 5.16, 14.96 ± 5.71, and 28.74 ± 3.21, respectively. Regression analysis denoted significant associations between burnout and being out-of-phase in the curriculum, the effectiveness of wellness initiatives, and strength of motivation for medical school (SMMS) in both the two- and three-dimensional MBI-SS models. Gender was significantly associated with burnout in only the two-dimensional model.Conclusions: Self-reported burnout in medical students at UICOM was validated using the MBI-SS. Being out-of-phase in the curriculum, being female, rating wellness initiatives as less effective, and demonstrating lower motivation for continued medical school education may be used as predictors of medical student burnout. This investigation may act as a guide for measuring burnout in medical student populations and how the implementation of wellness initiatives may ameliorate burnout.

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Section: Mbi-ss Prediction Of Burnout In Medical Studentsmentioning

confidence: 63%

Section: Methodsmentioning

confidence: 99%

Assessment of burnout in medical students using the Maslach Burnout Inventory-Student Survey: A cross-sectional data analysis

Obregon

Luo

Shelton

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Although the Maslach Burnout Inventory (MBI) is generally accepted as a valid measure of burnout, our study utilized a single self-reported burnout score [26]. The single self-reported measure was used to encourage resident participation through ease of use and to quantify the variable for correlational analysis.…”

Section: Discussionmentioning

confidence: 99%

The Correlation of Burnout and Optimism among Medical Residents

Fowler¹,

Kiessling²,

Khan³

et al. 2020

Cureus

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Burnout is common among clinical specialties and has implications on the residents' wellbeing and mental health. Evidence suggests that optimism and burnout are correlated, but research has not focused on the applicability to medical residents. The objective of the study was to define burnout in residents and correlate it with optimism. Methods The authors conducted a correlational, prospective cross-sectional study using self-reported single item burnout (1-10) and Life Oriented Test-Revised (LOT-R) (0-24) survey instruments among residents of neurosurgery, neurology, internal medicine, family medicine and emergency medicine at a community-based hospital. Residents were asked to fill out the survey once in the 2018 academic year and once again in the 2019 academic year. Burnout and optimism scores were correlated and compared across subgroups for each year.

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“…The 22-question Maslach Burnout Inventory for healthcare professionals (MBI-HSS) contains three subscales, which assess emotional exhaustion (EE), depersonalization (DP), and a sense of low personal achievement (LPA). It is the most commonly used and validated tool to detect burnout in clinicians so it was considered as the gold standard [14,15]. The abbreviated version of the MBI-HSS (aMBI) contains 12 of the 22 questions from the MBI-HSS but utilizes different cut-offs to define burnout [16].…”

Section: Introductionmentioning

confidence: 99%

The Abbreviated Maslach Burnout Inventory Can Overestimate Burnout: A Study of Anesthesiology Residents

Lim

Ong

et al. 2019

JCM

103

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The Maslach Burnout Inventory for healthcare professionals (MBI-HSS) and its abbreviated version (aMBI), are the most common tools to detect burnout in clinicians. A wide range in burnout prevalence is reported in anesthesiology, so this study aimed to ascertain which of these two tools most accurately detected burnout in our anesthesiology residents. The MBI-HSS and aMBI were distributed amongst 86 residents across three hospitals, with a total of 58 residents completing the survey (67.4% response rate; 17 male and 41 female). Maslach-recommended cut-offs for the MBI-HSS and the aMBI with standard cut-offs were used to estimate burnout prevalence, and actual prevalence was established clinically by a thorough review of multiple data sources. Burnout proportions reported by the MBI-HSS and aMBI were found to be significantly different; 22.4% vs. 62.1% respectively (p < 0.0001). Compared to the actual prevalence of burnout in our cohort, the MBI-HSS detected burnout most accurately; area under receiver operating characteristic of 0.99 (95% confidence interval (CI): 0.92–1.0). Although there was a good correlation between the MBI-HSS and aMBI subscale scores, the positive predictive value of the aMBI was poor; 33.3% (95% CI:27.5–39.8%), therefore caution and clinical correlation are advised when using the aMBI tool because of the high rates of false-positives.

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A Rasch Analysis Validation of the Maslach Burnout Inventory–Student Survey with Preclinical Medical Students

Cited by 33 publications

References 41 publications

Assessment of burnout in medical students using the Maslach Burnout Inventory-Student Survey: A cross-sectional data analysis

Assessment of burnout in medical students using the Maslach Burnout Inventory-Student Survey: A cross-sectional data analysis

The Correlation of Burnout and Optimism among Medical Residents

The Abbreviated Maslach Burnout Inventory Can Overestimate Burnout: A Study of Anesthesiology Residents

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