Abstract
BACKGROUND: Burnout is common among residents and negatively impacts patient care and professional development. Residents vary in terms of their experience of burnout. Our objective was to employ cluster analysis, a statistical method of separating participants into discrete groups based on response patterns, to uncover resident burnout profiles using the exhaustion and engagement sub-scales of the Oldenburg Burnout Inventory (OLBI) in a cross-sectional, multispecialty survey of United States medical residents. METHODS: The 2017 ACGME resident survey provided residents with an optional, anonymous addendum containing 3 engagement and 3 exhaustion items from the OBLI, a 2-item depression screen (PHQ-2), general queries about health and satisfaction, and whether respondents would still choose medicine as a career. Gaussian finite mixture models were fit to exhaustion and disengagement scores, with the resultant clusters compared across PHQ-2 depression screen results. Other variables were used to demonstrate evidence for the validity and utility of this approach. RESULTS: From 14 088 responses, 4 clusters were identified as statistically and theoretically distinct: Highly Engaged (25.8% of respondents), Engaged (55.2%), Disengaged (9.4%), and Highly Exhausted (9.5%). Only 2% of Highly Engaged respondents screened positive for depression, compared with 8% of Engaged respondents, 29% of Disengaged respondents, and 53% of Highly Exhausted respondents. Similar patterns emerged for the general query about health, satisfaction, and whether respondents would choose medicine as a career again. CONCLUSION: Clustering based on exhaustion and disengagement scores differentiated residents into 4 meaningful groups. Interventions that mitigate resident burnout should account for differences among clusters.