Network Analysis of Sleep, Mental Health, and Stress in Korean Dentists

Introduction and aims Dentists are vulnerable to occupational stress, poor sleep, and related mental health problems. However, prior studies have treated sleep quality as a single aggregate score, obscuring which specific sleep dimensions are most closely connected with mental health and occupational stress. This study applied psychological network analysis to examine the interrelationships among individual sleep quality components, mental health indicators, and occupational stress in Korean dentists. Methods A cross-sectional online survey of 229 Korean dentists was conducted in 2016. Six Pittsburgh Sleep Quality Index (PSQI) components, the Center for Epidemiologic Studies Depression Scale (CES-D), State-Trait Anxiety Inventory (STAI), Brief Encounter Psychosocial Instrument-Korean (BEPSI-K), Physician Job Stress (PJS), and Dentist Job Stress (DJS) scales were analysed. A Gaussian Graphical Model was estimated using EBICglasso (gamma = 0.5) following nonparanormal transformation. Centrality indices, bridge centrality, and bootstrap stability were computed. Results The network revealed 31 non-zero edges among 11 nodes. The strongest edges were depression–anxiety (partial r = 0.60), physician–dentist job stress (0.42), and subjective sleep quality–daytime dysfunction (0.35). Depression showed the highest strength centrality (z = 1.65). Bridge centrality analysis identified daytime dysfunction (bridge strength = 0.385) and sleep latency (0.224) as the primary sleep-domain nodes connecting to mental health and stress domains. The network demonstrated excellent stability (CS-coefficient = 0.70). Conclusion Daytime dysfunction and sleep latency emerged as prominent bridge nodes linking sleep quality with mental health and occupational stress in dentists. These findings highlight specific sleep-related domains that may warrant attention in future screening and intervention studies. Clinical relevance Rather than treating poor sleep as a single construct, dental workforce well-being programmes could target daytime dysfunction and sleep latency as specific domains for screening and support. Early identification of depressive symptoms, which occupied a central position in the network, may help prevent broader deterioration of occupational functioning.