Cluster Analysis as More Precise Measure of Burnout Among Healthcare Providers
| dc.contributor.author | Stiefer, Auston | |
| dc.contributor.author | Kezbers, Krista | |
| dc.contributor.author | Austin, Tessa | |
| dc.contributor.author | Nguyen, Amy | |
| dc.contributor.author | McIntosh, Heather | |
| dc.contributor.author | Touchet, Bryan | |
| dc.date.accessioned | 2020-11-05T17:18:20Z | |
| dc.date.available | 2020-11-05T17:18:20Z | |
| dc.date.issued | 2020-10 | |
| dc.description.abstract | 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/-SnGGFsZFQQ | en_US |
| dc.identifier.uri | https://hdl.handle.net/11244/325657 | |
| dc.language | en_US | en_US |
| dc.title | Cluster Analysis as More Precise Measure of Burnout Among Healthcare Providers | en_US |
| dc.type | Presentation | en_US |
| ou.group | Other | en_US |
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