Network Analysis of Healthcare Worker Burnout: Organizational Factors Show Highest Centrality.
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| Title: | Network Analysis of Healthcare Worker Burnout: Organizational Factors Show Highest Centrality. |
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| Authors: | Flores-Cohaila, Javier A.1,2 (AUTHOR), Miranda-Chávez, Brayan3,4 (AUTHOR), Copaja-Corzo, Cesar5 (AUTHOR) Csarcopaja@gmail.com |
| Source: | Inquiry (00469580). 6/30/2026, Vol. 63, p1-9. 9p. |
| Subject Terms: | *Corporate culture, *Psychological burnout, *Data analysis, *Job satisfaction, *Research, Nurses, Cross-sectional method, Research funding, Mental illness, Questionnaires, Statistics, Physicians, Data analysis software, Industrial hygiene |
| Geographic Terms: | Peru |
| Abstract: | Healthcare worker burnout is a complex phenomenon that traditional linear models fail to fully explain. This study uses network analysis to map the associative interactions between organizational factors, mental health symptoms, and burnout dimensions in a national sample of Peruvian physicians and nurses. Cross-sectional network analysis using data from the 2016 National Healthcare Worker Survey, comprising 4951 healthcare professionals (2125 physicians, 2826 nurses). Twenty-two variables spanning burnout dimensions (MBI-GS), mental health symptoms, work satisfaction, and organizational factors were analyzed using Gaussian Graphical Models with bootstrap validation (1000 iterations). Expected Influence, Betweenness, Closeness, and Strength centrality indices were calculated. Network invariance testing compared structural differences between professions. The network comprised 22 nodes with 82 non-zero edges (density = 0.355). Health services management satisfaction showed the highest expected influence (EI = 2.14), followed by monthly income (EI = 1.49). Emotional exhaustion showed substantial negative influence (EI = −0.46). Network invariance testing revealed statistically significant structural differences between professions (M = 0.2289, P =.0099), though overall similarity was moderate to high (ρ = 0.685). Nurses showed higher expected influence for job stability (EI = 0.619 vs 0.375), while physicians showed higher expected influence for marital status (EI = 0.659 vs 0.416). Bootstrap stability coefficients exceeded recommended thresholds (CS = 0.67-0.75). Burnout components showed network patterns consistent with complex adaptive systems, with organizational factors (management satisfaction, income) displaying higher expected influence than individual mental health symptoms. Network structures differed statistically between professions, though with moderate-to-high overall similarity. We propose a preliminary exploratory framework (SPIRAL model) identifying 6 network-based patterns that require prospective longitudinal validation before clinical application. [ABSTRACT FROM AUTHOR] |
| Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | Healthcare worker burnout is a complex phenomenon that traditional linear models fail to fully explain. This study uses network analysis to map the associative interactions between organizational factors, mental health symptoms, and burnout dimensions in a national sample of Peruvian physicians and nurses. Cross-sectional network analysis using data from the 2016 National Healthcare Worker Survey, comprising 4951 healthcare professionals (2125 physicians, 2826 nurses). Twenty-two variables spanning burnout dimensions (MBI-GS), mental health symptoms, work satisfaction, and organizational factors were analyzed using Gaussian Graphical Models with bootstrap validation (1000 iterations). Expected Influence, Betweenness, Closeness, and Strength centrality indices were calculated. Network invariance testing compared structural differences between professions. The network comprised 22 nodes with 82 non-zero edges (density = 0.355). Health services management satisfaction showed the highest expected influence (EI = 2.14), followed by monthly income (EI = 1.49). Emotional exhaustion showed substantial negative influence (EI = −0.46). Network invariance testing revealed statistically significant structural differences between professions (M = 0.2289, P =.0099), though overall similarity was moderate to high (ρ = 0.685). Nurses showed higher expected influence for job stability (EI = 0.619 vs 0.375), while physicians showed higher expected influence for marital status (EI = 0.659 vs 0.416). Bootstrap stability coefficients exceeded recommended thresholds (CS = 0.67-0.75). Burnout components showed network patterns consistent with complex adaptive systems, with organizational factors (management satisfaction, income) displaying higher expected influence than individual mental health symptoms. Network structures differed statistically between professions, though with moderate-to-high overall similarity. We propose a preliminary exploratory framework (SPIRAL model) identifying 6 network-based patterns that require prospective longitudinal validation before clinical application. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00469580 |
| DOI: | 10.1177/00469580261433856 |
