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. 2020 Dec 18;17(24):9495.
doi: 10.3390/ijerph17249495.

Burnout Assessment Tool (BAT)-Development, Validity, and Reliability

Wilmar B Schaufeli  1   2 , Steffie Desart  1 , Hans De Witte  1   3
Affiliations

Burnout Assessment Tool (BAT)-Development, Validity, and Reliability

Wilmar B Schaufeli et al. Int J Environ Res Public Health. .

Abstract

This paper introduces a new definition for burnout and investigates the psychometric properties of the Burnout Assessment Tool (BAT). In a prior qualitative study, 49 practitioners were interviewed about their conceptualization of burnout (part 1). Using a dialectical approach, four core dimensions-exhaustion, mental distance, and impaired emotional and cognitive impairment-and three secondary dimensions-depressed mood, psychological distress, and psychosomatic complaints-emerged, which constitute the basis of the BAT. In the second study, the psychometric characteristics of the BAT were investigated in a representative sample of 1500 Flemish employees, focusing on factorial validity, reliability, and construct validity, respectively. Results demonstrate the assumed four-factor structure for the core dimensions, which is best represented by one general burnout factor. Contrary to expectations, instead of a three-factor structure, a two-factor structure was found for the secondary dimensions. Furthermore, the BAT and its subscales show adequate reliability. Convergent validity and discriminant validity with other burnout measures-including the MBI and OLBI-was demonstrated, as well as discriminant validity with other well-being constructs, such as work engagement and workaholism.

Keywords: Burnout Assessment Tool (BAT); burnout; conceptualization; scale development; validation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Multi-trait, multi-method framework for the Burnout Assessment Tool—Core (BAT-C), Maslach Burnout Inventory-General Survey (MBI-GS), and Oldenburg Burnout Inventory (OLBI) (correlated traits–correlated methods (CT-CM) model) with average pad coefficients.
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