Introduction There are few published empirical data on the effects of COVID‐19 on mental health, and until now, there is no large international study. Material and methods During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. Statistical analysis Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. Results Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. Conclusions The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.

The growing popularity of cognitive-behavioral therapy (CBT) has helped reshape the mental health scene in the city of Buenos Aires, historically the stronghold of psychoanalysis. In the early 1980s, CBT was infrequently used and sometimes overtly resisted in the field of mental health. Almost 3 decades later, the impact of CBT has increased dramatically in Argentina, not only in independent practice but also in the health system and in everyday life. This article aims to describe the process by which Argentine psychotherapists first adopted this new theoretical framework.

Background Due to the COVID-19 pandemic, Argentina has been under mandatory quarantine. We have aimed to investigate the state of mental health of the Argentine population and the behaviours adopted to cope with mental distress during quarantine. Method An online survey was conducted using a probabilistic sampling technique and stratified according to the geographic regions of the country. The survey covered days 7-11 (n = 2,631) and days 50-55 (n = 2,068) after compulsory quarantine. The psychological impact was measured using the 27-item Symptom CheckList (SCL-27), which provides a Global Severity Index (GSI). An ad hoc questionnaire registered problematic, healthy and other behaviours. Two network models were estimated using a Mixed Graphical Model. Data from the two periods were compared and analysed. Outcomes Higher GSI scores and greater risk of experiencing mental disorder were found in Period 2 as compared with Period 1. The lowest GSI scores were associated with physical activity in both periods, and meditation and yoga in Period 1. Drug users reported the highest GSI scores in both periods. The Network Comparison Test confirmed a significant change in symptomatology structure over the two quarantine periods. Conclusion This study showed that psychological symptoms and the risk of experiencing mental disorder increased significantly from Period 1 to Period 2. Network analysis suggested that the quarantine might have brought about changes in the relationships between symptoms. Overall results revealed the relevance of mental health and the need to take mental health actions upon imposing quarantine during the current COVID-19 pandemic.

No abstract