IRT represents a class of generalized linear mixed effect models for relating observed item responses to latent constructs. We evaluated the demographic measurement equivalence of the MBI-HSS subscales in a series of multi-group item response theory- (IRT-) based differential item functioning (DIF) analyses (Additional file 1: Appendix 1). Demographic variables included age group (< 35, 35–44, 45–54, 55–64, and ≥ 65 years), gender (male and female), and specialty. Burnout symptoms are indicated by high scores on the EE and DP subscales and low scores on the PA subscale. Higher scores on each subscale indicate more of each construct. All MBI-HSS items have a 7-point Likert-type, frequency response scale (0 = never, 1 = a few times a year or less, 2 = once a month or less, 3 = a few times a month, 4 = once a week, 5 = a few times a week, 6 = every day). The MBI-HSS is an outcome assessment of job burnout containing three subscales: emotional exhaustion (EE) (9 items), depersonalization (DP) (5 items), and personal accomplishment (PA) (8 items). From this dataset, we excluded physicians who were not practicing in the US or were retired. Further sampling design details are reported in Shanafelt et al. Data were collected in 2014 from physicians of all specialties sampled via email from the American Medical Association Physician Master File. This study used secondary, cross-sectional survey data from a national study on the prevalence of physician burnout conducted by Shanafelt et al. However, no studies, to our knowledge, have evaluated the demographic measurement equivalence of the MBI-HSS in US physicians. Establishing the measurement equivalence of an instrument is a key aspect of construct validity and, consequently, is required for the unbiased comparison of physician burnout across demographic groups. For example, female physicians may have higher observed burnout scores than male physicians because they are more willing than male physicians to report their symptoms, despite both groups having the same latent burnout levels. However, when a measure lacks equivalence across respondents who differ demographically, subscale score differences may actually reflect systematic differences in the way the demographic groups interpret items or in their willingness to endorse items, as opposed to true differences in the groups’ latent (unobserved) burnout symptom levels. Our findings support the use of the MBI as a valid tool to assess age-, gender-, and specialty-related disparities in US physician burnout.Ī measure is equivalent when it functions the same way across groups of respondents who might differ in gender, age, or other personal characteristics that may influence their responses to a self-reported measure. ConclusionsĪge-, gender-, and specialty-related disparities in US physician burnout are not explained by differences in the MBI’s functioning across these demographic groups. Differences in physicians’ individual-level subscale scores and burnout symptom prevalence estimates across DIF- adjusted and unadjusted IRT models were also small (in all cases, mean absolute differences in individual subscale scores were < 0.04 z-score units prevalence estimates differed by < 0.70%). However, in all cases, average differences in expected subscale-level scores due to DIF were < 0.10 SD on each subscale. We detected statistically significant age-, gender-, and specialty- DIF in all but one MBI item. We assessed DIF’s practical significance by comparing differences in individuals’ subscale scores and burnout prevalence estimates from models unadjusted and adjusted for DIF. We detected DIF using two IRT-based methods and assessed its impact by estimating the overall average difference in groups’ subscale scores attributable to DIF. We assessed the measurement equivalence of the MBI across age, gender, and specialty groups in multi-group item response theory- (IRT-) based differential item functioning (DIF) analyses using secondary, cross-sectional survey data from US physicians ( n = 6577). We evaluated whether disparities in US physician burnout are explained by differences in the MBI’s functioning across physician age, gender, and specialty groups. Disparities in US physician burnout rates across age, gender, and specialty groups as measured by the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI) are well documented.
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