Burnout in Graduate Medical Education: Uncovering Resident Burnout Profiles Using Cluster Analysis.

Autor:	Yaghmour NA; Accreditation Council for Graduate Medical Education.; School of Health Professions Education, Maastricht University, Maastricht, The Netherlands., Savage NM; Old Dominion University, Norfolk, VA., Rockey PH; Southern Illinois University School of Medicine, Springfield, IL., Santen SA; Virginia Commonwealth School of Medicine, Richmond, VA.; University of Cincinnati College of Medicine, Cincinnati, OH., DeCarlo KE; University of Illinois at Chicago Health System, Chicago, IL., Hickam G; Virginia Commonwealth University Health System, Richmond, VA., Schwartzberg JG; Accreditation Council for Graduate Medical Education., Baldwin DC Jr; Accreditation Council for Graduate Medical Education.; University of Nevada, Reno Medical School, Reno, NV.; Northwestern University Feinberg School of Medicine, Chicago, IL., Perera RA; Virginia Commonwealth University, Richmond, VA.
Jazyk:	angličtina
Zdroj:	HCA healthcare journal of medicine [HCA Healthc J Med] 2024 Jun 01; Vol. 5 (3), pp. 237-250. Date of Electronic Publication: 2024 Jun 01 (Print Publication: 2024).
DOI:	10.36518/2689-0216.1784
Abstrakt:	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. Competing Interests: Conflicts of Interest: The authors declare no conflicts of interest. (© 2024 HCA Physician Services, Inc. d/b/a Emerald Medical Education.)
Databáze:	MEDLINE
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