Individuals readily self-diagnose as experiencing burnout despite continuing debate among researchers and practitioners regarding how the syndrome should best be defined and measured. The objective of this study was to determine whether a new 34-item measure of burnout distinguished those who did and did not selfidentify as burnt out and in doing so ascertain the most distinguishing symptoms. Six hundred twenty-five participants recruited via Facebook completed the burnout measure online before reporting whether they were currently experiencing burnout. Receiver operating characteristic analyses indicated that the measure adequately discriminated between those who did (47.7%) and did not self-report burnout. Cutoff scores based on Youden’s indices had comparable classificatory accuracy as prediction rule ensembles derived through machine learning methods. Items capturing exhaustion, compromised cognition, lack of pleasure in work, and self-criticism were the most distinguishing items across the analyses, while items depicting empathy loss varied in their discriminatory capacity between analyses. Weighting symptom items according to their discriminatory capacity did not improve classificatory accuracy compared to when all items were weighted equally. Overall, the 34-item measure satisfactorily differentiated those with and without selfreported burnout, with symptoms of exhaustion, cognitive dysfunction, lack of pleasure in work, and selfcriticism being most indicative of the syndrome. Future research is needed to validate the measure and its cutoff values by comparing measure scores against those clinically judged as having burnout. [ABSTRACT FROM AUTHOR]