Back

Frequency and Risk Factors of Resident Burnout Before and During the COVID19 Pandemic

Turk, S.; Kalyoncu, A.; Karabilgin Ozturkcu, O. S.; Elbi, H.; Orman, M.; Yaman, A. C.; Toptas, M.; Bekki, Y. C.; Ozlutas, M.; Esenturk, M. B.; Bapuraj, J.; Ozbek, S. S.

2022-09-04 medical education
10.1101/2022.09.04.22279366 medRxiv
Show abstract

ObjectiveTo explore the prevalence and contributing factors of resident burnout in a University Hospital before and during the COVID 19 pandemic. MethodsThirty Faculty of Medicine departments were included in the survey, where 400 university hospital residents filled out the Maslach Burnout Inventory (MBI) in January 2018 and April 2020. Related scores of emotional exhaustion (EE), decreased accomplishment (DA) and depersonalization (DP) were calculated and compared between the different groups. Correlation between scores and possible contributing factors, including demographics, work-life circumstances, exposure to workplace violence, were investigated. Contributing factors were compared between the time points. ResultsThe EE and DA scores were significantly higher in junior residents than in senior residents. Both scores were higher among residents who had experienced abuse or violence. The emergency medicine residents had significantly higher DP scores, while the EE scores of radiology residents were lower than others. Thirty percent of all residents smoked cigarettes. This percentage was even higher among the residents of the departments of surgery and emergency medicine (45-50%). A significant correlation was demonstrated between the scores of MBI and smoking, while analysis with other demographics did not yield any relation. According to the study results, basic science residents had significantly increased scores in all MBI subgroups during the pandemic. ConclusionResident physician burnout was found to be related to the work environment, smoking cigarettes and exposure to violence at workplace.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.