Análise longitudinal da rede de sintomas depressivos em pós-graduandos stricto sensu durante o processo formativo

Abstract

Depressive symptoms are a highly prevalent problem among pgraduate students, among other aspects, mainly due to the different stressors related to this level of training. Evidence has shown that students who have been in graduate studies for a longer period of time are more vulnerable to the harmful effects of academic stressors on their mental health. However, most currently available studies have assessed the prevalence and severity of depressive symptoms among postgraduates using traditional theoretical approaches, mainly latent variable models. New approaches, such as network analysis of psychopathological symptoms, have proven to be a promising alternative for understanding mental disorders to the detriment of conventional and hegemonic approaches. This perspective provides a more in-depth characterization of the relationships between symptomatic components, making it possible to understand the dynamics of interactions between symptoms and, based on these, how mental disorders are structured. Although recent, this approach has been widely used to understand depressive disorders in different populations, however, studies using this approach in university students, especially those enrolled in stricto sensu postgraduate studies, are still incipient. Objective: To analyze longitudinally the structure of the network of depressive symptoms in stricto sensu postgraduate students during their training journeys through network theory. Method: Prospective, single-center longitudinal study that followed a cohort of postgraduate students from a public institution during the first 12 months of the training process. Data on sociodemographic characteristics and depressive symptoms were collected using the Patient Health Questionnaire-9. Data were collected at the beginning of training (March and April/2021) and 12 months after the first collection (March and April/2022). Network analysis is proposed to characterize the structure of the depressive symptoms network at these two moments and comparison techniques to verify differences between these structures. Results: Depressed mood and concentration problems were the most central symptoms in the two assessments carried out. These two symptoms, together with anhedonia, were those that showed the greatest predictability both in the baseline study and 12 months later. Regarding network structure and global strength, no longitudinal differences were evident between the networks. Regarding symptom-by-symptom interactions, anhedonia and depressed mood were the strongest associations found, followed by the relationship between concentration problems and psychomotor problems. Conclusions: The postgraduate students in this sample presented dense networks and high severity of depressive symptoms in the two assessments carried out. The passage of the COVID-19 pandemic, in the baseline assessment, may have contributed to the severity of the depressive symptoms found, as well as influencing the configuration of the network upon entry into postgraduate studies and after a year of study, the inherent complexities of adapting to this formative level may have contributed to the maintenance of network configurations, symptom severity and their relationships.