Optimism as a protective factor against the psychological impact of COVID-19 pandemic through its effects on perceived stress and infection stress anticipation.

The 2019 coronavirus disease (COVID-19) and the recommended social isolation presented a challenge to people's mental health status. Optimism is a psychological factor that plays a key role in the evaluation of stressful situations. The purpose of this study was to investigate the mediating role of perceived stress and Covid-19-related stress anticipation in the relationship between optimism and post-traumatic stress symptoms. Our sample included 1015 participants ranging in age from 18 to 79 years, 80% of whom were Spaniards. At the beginning of the worldwide pandemic, participants were confined to their homes for at least seven days and completed an online survey measuring various sociodemographic and psychological variables. We found an indirect effect of optimism on intrusion and hyperarousal through perceived stress and stress anticipation. In addition, we observed an indirect effect of optimism on avoidance through perceived stress. Finally, the results showed a significant indirect effect of optimism on the total post-traumatic stress symptoms score through perceived stress and stress anticipation. Our results indicate that positive beliefs inherent to optimism are related to less psychological impact of the COVID-19 outbreak.

The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion.

More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams - such as reduced informal communication - with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable - but not limited to - being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.