A Bayesian network analysis of the association between quality of life and burnout among nurses in Iran

Burnout and reduced quality of life are prevalent and interrelated issues among nurses, especially in high-stress healthcare settings. This study applies Bayesian network analysis to examine the association between quality of life and burnout among nurses in Iran by identifying key sub-domains and their interrelationships within the burnout-quality of life framework. A cross-sectional study. This study was conducted in 2023 involving 300 nurses from hospitals affiliated with Babol and Kashan Universities of Medical Sciences, Iran. Data were collected using the WHOQOL-BREF and Maslach Burnout Inventory. Conditional dependencies among eight health and burnout domains were assessed using a Bayesian Gaussian Graphical Model (GGM). Only edges with Bayes Factor BF₁₀ > 10 were retained. Centrality metrics (strength, closeness, betweenness, expected influence) were calculated with bootstrapped 95% confidence intervals. Stability was assessed using case-dropping bootstrap (CS coefficients). The sample was predominantly female (81.7%) and married (77.0%), with a mean work experience of 11.09 ± 7.87 years. The network included 8 nodes and 42 retained edges (sparsity = 0.364). Psychological health emerged as the most central node (strength = 2.132; closeness = 2.150; betweenness = 2.190; expected influence = 2.038), showing strong conditional associations with physical health (weight = 0.221), environmental health (weight = 0.371), and general health satisfaction (weight = 0.314). Depersonalization was strongly interconnected with emotional exhaustion (weight = 0.288), social relationships (weight = 0.258), and physical health (weight = -0.211). The correlation stability coefficient for strength was 0.749, indicating excellent stability. This study emphasizes the practical importance of prioritizing nurses’ mental health in the design of workplace interventions aimed at improving quality of life and reducing burnout. The findings support the implementation of mental health–oriented organizational strategies, particularly interventions targeting depersonalization, to enhance overall quality of life and professional functioning. Bayesian network modeling is presented as a valuable framework for evidence-based and targeted decision-making in healthcare settings. Bayesian network analysis revealed the complex interplay between burnout and quality of life components among nurses. Psychological health emerged as the most central node linking subdomains of quality of life and burnout. Emotional exhaustion and depersonalization were strongly connected with physical, social, and psychological health.