Network Analysis of Healthcare Worker Burnout: Organizational Factors Show Highest Centrality.
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...
| Publicado en: | Inquiry (00469580) Vol. 63; pp. 1 - 10 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
6/30/2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=194993659&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 194993659 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 6/30/2026 vid: 63 pid: 344 pub: Sage Publications Inc. artinfo: ui: 194993659 10.1177/00469580261433856 ppf: 1 ppct: 9 formats: tig: atl: Network Analysis of Healthcare Worker Burnout: Organizational Factors Show Highest Centrality. aug: au: Flores-Cohaila, Javier A. Miranda-Chávez, Brayan Copaja-Corzo, Cesar affil: Grupo NEMECS: Neurociencias, Metabolismo, Efectividad Clínica y Sanitaria, Universidad Científica del Sur, Carrera de Medicina Humana, Lima, Perú Hospital Victor Larco Herrera, Magdalena del Mar, Lima, Peru Grupo de Estudios e Investigación en Educación Médica y Bioética de la FACSA, EDUCAB – UPT, Universidad Privada de Tacna, Perú Hospital Nacional Guillermo Almenara Irigoyen, EsSalud, Lima, Perú Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Universidad San Ignacio de Loyola, Lima, Perú su: Corporate culture Nurses Cross-sectional method Psychological burnout Data analysis Research funding Mental illness Questionnaires Job satisfaction Research Statistics Physicians Data analysis software Industrial hygiene Peru sug: subj: Peru Corporate culture Nurses Cross-sectional method Psychological burnout Data analysis Research funding Mental illness Questionnaires Job satisfaction Research Statistics Physicians Data analysis software Industrial hygiene keyword: burnout healthcare workers mental health network analysis occupational health ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
|---|