Perceived Organizational and Social Support as Probable Mitigators of Burnout Among Medical Trainees and Providers

BACKGROUND: National trends show that employees and trainees in the medical field are susceptible to burnout. To our knowledge, no studies have been published on burnout moderators, such as perceived support and lifestyle behaviors. This study is part of a larger, longitudinal investigation examining the relationships among burnout, levels of perceived stress, levels of perceived support (social and organizational), and several lifestyle behaviors for faculty, staff, residents, fellows, and students at the OU-TU School of Community Medicine (OUSCM). METHODS: Investigators sent an email survey to every member of the OUSCM in April 2019. It included validated measures such as the Maslach Burnout Inventory (MBI), University of Delaware Survey of Perceived Organizational Support, and the Multidimensional Scale of Perceived Social Support, alongside questions about lifestyle behaviors. MBI subscores of exhaustion and cynicism were stratified in the analysis. SPSS software was used to conduct Pearson correlations among these variables. RESULTS: 318 responses were collected (35% response rate), with respondents’ demographic data representing the white (67.7%), women (78.1%), and staff (57.2%) members of the population. Among the whole sample, levels of perceived organizational support had a moderate negative correlation relative to burnout subscores of exhaustion (r=-.556, p<0.001) and cynicism (r=-.558, p<0.001). Likewise, levels of perceived social support had a weak negative correlation to exhaustion (r= -.169, p = 0.008) and cynicism (r= -.233, p<0.001). Among the disaggregated subgroups of students and faculty, moderate negative correlations were found between perceived social support and burnout subscores. Social support had a moderate negative correlation only with cynicism among students (r= -.453, p=0.006), while social support had moderate negative correlations with both exhaustion (r=-.514 p<0.001) and cynicism (-.555, p<0.001) among faculty. There were no significant relationships found between these two variables among staff members and resident physician subgroups. CONCLUSION: Because of a low response rate, our sample’s demographics may not be representative of our populations and may potentially limit generalization based on these results. However, the significant correlations found in whole sample analysis between perceived organizational and social support relative to burnout suggest that these variables may lessen the effects of burnout in our population. Moreover, subgroup analysis suggests that social support is a more important potential mitigator of burnout only in students and faculty, when compared to staff and residents. Furthermore, this finding supports that burnout levels within distinct subgroups of our population may be mitigated by different variables

Cluster Analysis as More Precise Measure of Burnout Among Healthcare Providers

BACKGROUND: The study of burnout among physicians and medical trainees has become a focus of many professional societies, academic institutions, and hospital systems in recent years, given the high prevalence of burnout in these populations and its implications for poor patient outcomes. However, physician burnout, widely assessed via abbreviated versions of the Maslach Burnout Inventory (MBI), has been largely considered a monolithic, syndromic condition, neglecting multidimensional aspects of the psychometric measure. This study seeks to identify the presence of distinct burnout “clusters” among academic medical professionals and trainees based on respondents’ MBI subscores of exhaustion, cynicism, and professional inefficacy, according to the analytic framework of the MBI’s developers. METHODS: This secondary data analysis was conducted using a large dataset from the 2019 OUSCM’s well-being survey, which included the MBI among other social construct measures. Per a new analytic approach recommended by creators of the MBI, we conducted additional cluster analysis on the dataset to better characterize our population. TwoStep cluster analysis via SPSS was utilized to analyze mean scores of the 3 MBI subscales and to understand similarities, differences, and clusters that existed within the dataset. RESULTS: A total of 272 burnout subscores were included in TwoStep Cluster analysis. Sample demographics included: mean age 39.4, 78.0% female, 75.1% white, 57.2% staff. Preliminary results of the cluster analysis indicated 4 distinct clusters, at fair cluster quality, with all 272 individuals included. Four distinct clusters were identified: 1) respondents with high subscores in both cynicism and exhaustion, 105 (38.6%); 2) respondents with high scores of exhaustion only, 62 (22.8%); 3) those with high scores of inefficacy only, 58 (21.3%); and 4) those with low scores in all areas, 47 (17.3%). DISCUSSION: The emergent four-cluster pattern is consistent with preliminary cluster analysis on burnout subscores among mental health professionals, as elicited by the psychologists who developed the MBI. This method identifies individuals who share similar patterns of burnout subscores, previously considered outliers. Identifying specific dimensions of burnout within a population provides greater understanding of how individuals experience burnout and how their environments contribute to burnout. Our sample restricted to the OUSCM limits assessment of burnout clusters among medical professionals and trainees at large. Extending cluster analysis to samples from multiple academic medical institutions would validate the identification of burnout clusters and provide evidence for the development of more precise interventions to mitigate burnout among medical providers and trainees. Media Link: https://youtu.be/-SnGGFsZFQ