Journal of General Internal Medicine

, Volume 27, Issue 11, pp 1445–1452 | Cite as

Concurrent Validity of Single-Item Measures of Emotional Exhaustion and Depersonalization in Burnout Assessment

  • Colin P. WestEmail author
  • Liselotte N. Dyrbye
  • Daniel V. Satele
  • Jeff A. Sloan
  • Tait D. Shanafelt
Original Research



Burnout is a common problem among physicians and physicians-in-training. The Maslach Burnout Inventory (MBI) is the gold standard for burnout assessment, but the length of this well-validated 22-item instrument can limit its feasibility for survey research.


To evaluate the concurrent validity of two questions relative to the full MBI for measuring the association of burnout with published outcomes.


The single questions “I feel burned out from my work” and “I have become more callous toward people since I took this job,” representing the emotional exhaustion and depersonalization domains of burnout, respectively, were evaluated in published studies of medical students, internal medicine residents, and practicing surgeons. We compared predictive models for the association of each question, versus the full MBI, using longitudinal data on burnout and suicidality from 2006 and 2007 for 858 medical students at five United States medical schools, cross-sectional data on burnout and serious thoughts of dropping out of medical school from 2007 for 2222 medical students at seven United States medical schools, and cross-sectional data on burnout and unprofessional attitudes and behaviors from 2009 for 2566 medical students at seven United States medical schools. We also assessed results for longitudinal data on burnout and perceived major medical errors from 2003 to 2009 for 321 Mayo Clinic Rochester internal medicine residents and cross-sectional data on burnout and both perceived major medical errors and suicidality from 2008 for 7,905 respondents to a national survey of members of the American College of Surgeons.


Point estimates of effect for models based on the single-item measures were uniformly consistent with those reported for models based on the full MBI. The single-item measures of emotional exhaustion and depersonalization exhibited strong associations with each published outcome (all p ≤0.008). No conclusion regarding the relationship between burnout and any outcome variable was altered by the use of the single-item measures rather than the full MBI.


Relative to the full MBI, single-item measures of emotional exhaustion and depersonalization exhibit strong and consistent associations with key outcomes in medical students, internal medicine residents, and practicing surgeons.


burnout well-being measurement graduate medical education medical practice 



This work was supported by the Mayo Clinic Department of Medicine Program on Physician Well-Being The authors wish to acknowledge the participation of the American College of Surgeons and the site leaders for each medical school, Steven J. Durning, MD (Uniformed Services University of the Health Sciences), Anne M. Eacker, MD (University of Washington School of Medicine), William Harper, MD (University of Chicago Pritzker School of Medicine), F. Stanford Massie, MD (University of Alabama School of Medicine), Christine Y. Moutier, MD (University of California, San Diego), David V Power, MD (University of Minnesota Medical School), and Matthew R. Thomas, MD (Mayo Clinic).

Conflict of Interest

The authors declare that they do not have a conflict of interest.


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Copyright information

© Society of General Internal Medicine 2012

Authors and Affiliations

  • Colin P. West
    • 1
    • 2
    • 5
    Email author
  • Liselotte N. Dyrbye
    • 3
  • Daniel V. Satele
    • 2
  • Jeff A. Sloan
    • 2
  • Tait D. Shanafelt
    • 4
  1. 1.Division of General Internal MedicineMayo ClinicRochesterUSA
  2. 2.Division of Biomedical Statistics and InformaticsMayo ClinicRochesterUSA
  3. 3.Division of Primary Care Internal MedicineMayo ClinicRochesterUSA
  4. 4.Division of HematologyMayo ClinicRochesterUSA
  5. 5.Divisions of General Internal Medicine and Biomedical Statistics and Informatics, Department of MedicineMayo Clinic College of MedicineRochesterUSA

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