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Behavioral Sciences ; 12(5):134, 2022.
Article in English | ProQuest Central | ID: covidwho-1871881


From the analysis of the scientific literature relating to the health of oncological patients, the need to consider the global dimension of health of individuals emerges, which subsumes the bodily dimension and involves all the actors who offer their contribution to it in different ways. In this direction, the state of the art of the health construct offered by healthcare professionals highlights a lack of scientific contributions to the specific subject although these professionals are fundamental figures in oncological diagnosis setups. Considering, therefore, the healthcare roles as an integral part of the interactive framework where the oncological patient is placed, this paper offers the results of an Italian study relating to the health of healthcare professionals who take charge of patients with a neoplasia diagnosis. In particular, through an analysis of the discursive productions of 61 participants (healthcare workers, oncological patients and citizens) by the M.A.D.I.T. methodology (Methodology for the Analysis of Computerized Textual Data), this study aimed at observing the discursive reality of health offered by healthcare workers. The collected data highlight a low degree of health expressed by the healthcare professionals, who are strongly typified by rhetoric such as “the one who is destined to suffer psychologically”. These narrations limit the possibilities of development of different narrations in depicting these professionals: critical repercussions in the interaction with the oncological patients emerged, as well as in their global health degree. In conclusion, the results show the need for deep investigation into the impact that the health degree of health professionals can have on the patients they take charge of.

Front Psychol ; 12: 559842, 2021.
Article in English | MEDLINE | ID: covidwho-1365575


This contribution places itself within the emergency context of the COVID-19 spread. Until medical research identifies a cure acting at an organic level, it is necessary to manage what the emergency generates among the members of the Community in interactive terms in a scientific and methodologically well-founded way. This is in order to promote, among the members of the Community, the pursuit of the common aim of reducing the spread of infection, with a view to community health 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 shifting away from the use of deterministic-causal references to the use of the uncertainty of interaction as an epistemological foundation principle. Managing the interactive (and non-organic) fallout of the emergency in the Community is made possible by the formalisation of the interactive modalities (the Discursive Repertories) offered by Dialogical Science. To place oneself within this scientific panorama enables interaction measurements: so, the interaction measurement indexes offers a range of generative possibilities of realities built by the speeches of the Community members. Moreover, the Social Cohesion measurement index, in the area of Dialogical Science, makes available to public policies the shared measure of how and by how much the Community is moving towards the common purpose of reducing the contagion spread, rather than moving towards other personal and not shared goals (for instance, having a walk in spite of the lockdown). In this index, the interaction between the Discursive Repertories and the "cohesion weight" associated with them offers a Cohesion output: the data allow to manage 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 and efficient availability of the measure itself, optimising time and resources.