Dual-factor model of mental health: a latent profile and network analysis approach among Vietnamese people

Purpose Mental health research has increasingly adopted dual-factor models that distinguish between psychopathology and well-being. In Southeast Asia, particularly Vietnam, limited empirical attention has been given to the interplay among anxiety, depression and well-being in populations. This study aims to identify distinct mental health profiles among Vietnamese adults and examine how the structural relationships between psychopathological symptoms and well-being indicators differ across these profiles using network analysis. Design/methodology/approach A cross-sectional study was conducted with 1,105 Vietnamese adults (aged 18–64). Latent class analysis was employed to classify participants into mental health subgroups based on psychopathology and well-being. Subsequently, network analysis was used to visualize and compare the relationships between nodes within each latent class. Findings Three latent classes emerged: a “troubled” group (high psychopathology, low well-being), a “vulnerable” group (moderate psychopathology, low well-being) and a “complete mental health” group (low psychopathology, high well-being). Network analysis showed that the troubled group had a dense, exclusively positive structure dominated by tightly interconnected anxiety and depression, and well-being nodes were also central but weakly connected across domains, indicating coexisting distress and diminished positive functioning without inhibitory regulation. The vulnerable group was characterized by high centrality of psychopathological symptoms and fragmented well-being. In contrast, the complete mental health group was characterized by the highest centrality concentrated in well-being nodes. Originality/value This study provides empirical support for the dual-factor model of mental health and highlights class-specific network patterns that underscore the importance of both well-being and psychopathology in determining mental health.