The Interactive Management of the SARS-CoV-2 Virus: The Social Cohesion Index, a Methodological-Operational Proposal - Turchi, Gian Piero; Dalla Riva, Marta Silvia; Ciloni, Caterina; Moro, Christian; Orrù, Luisa (Frontiers in Psychology, 2021)
This contribution is part of the emergency spread of COVID-19. Until medical research identifies a cure acting at an organic level, it is necessary to manage in a scientific and methodologically well-founded way what the emergency generates among the members of the Community in interactive terms. This is in order to promote the pursuit of the common aim of reducing the spread of infection, with a view to the Health of the Community as a whole. In addition, being at the level of interactions enables us to move towards a change of these interactions in response to the COVID-19 emergency, in order to manage what will happen in the future, in terms of changes in the interactive arrangements after the emergency itself. This becomes possible by using the uncertainty of interaction as an epistemological foundation principle. Managing the interactive fallout of the emergency in the Community is made possible by the formalization of the interactive modalities (Discursive Repertoires) offered by Dialogical Science. To place oneself within this scientific panorama allows to have interaction measurement: thus, interaction measurement indexes allow to offer the quantum of generative possibilities of realities built by the speeches of the Community members. Moreover, through the Social Cohesion measurement index it makes available to public policies the shared measure of how much and how the Community is moving towards the common purpose of reducing the spread of contagion. In this index, the interaction between the Discursive Repertoires and the "cohesion weight" offers a Cohesion output: the data allows managing operationally what happens in the Community in a shared way and in anticipation, without leaving the interactions between its members to chance. In this way, they can be directed towards the common purpose through appropriate interventions relevant to the interactive set-up described in the data. The Cohesion measure makes it possible to operate effectively and efficiently, thanks to the possibility of monitoring the progress of the interventions implemented and evaluating their effectiveness. In addition, the use of predictive Machine Learning models, applied to interactive cohesion data, allows for immediate availability of the measure itself, optimizing time and resources.