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Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.
Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.
Background International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. Aims To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. Method Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. Results Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0–82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. Conclusions The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
Emotional intelligence significantly influences various aspects of teachers’ professional and personal lives, notably affecting preschoolers’ social skills and emotional development during formative years. This study utilizes a network analysis approach to explore the complex relationships among four components of emotional intelligence: emotional labor, emotional regulation, professional well-being, and professional identity. Participants included 2069 frontline Chinese teachers (34 males, 2035 females; M = 28.64, SD = 5.98; M years of teaching = 6.88, SD = 5.29) with no leadership roles, categorized into three stages of their careers based on years of teaching experience: novice (0–3 years; n = 612), advanced beginners (4–6 years; n = 537), and competent (7–40 years; n = 920). Findings revealed that joy of teaching, role value, and professional value were identified as the most critical elements within the emotional state network of early childhood education teachers. The strongest connections in teachers’ emotional networks were found between school connectedness and joy of teaching (r = 0.474), surface acting behavior and natural acting behavior (r = 0.419), and professional value and professional behavior (r = 0.372). Furthermore, teachers across different career stages exhibited similar characteristics and intrinsic connections among emotional state components. These findings deepen our understanding of the emotional state networks of ECE teachers, highlighting shared features and interconnected mechanisms, and suggest that enhancing teachers’ emotional intelligence through targeted professional development can improve both teacher well-being and preschoolers’ social–emotional outcomes. Policies that foster strong school connectedness and reduce emotional labor are key to promoting sustained joy in teaching, particularly for novice and advanced beginner teachers.
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