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Estudio de los criterios del estrés laboral utilizando mapas cognitivos

Abstract:

This research took place at the Ambulatory Care Center of the Ecuadorian Social Security Institute in the province of Guayas. The investigation was motivated by some irregularities observed in the behavior of the administrative and operative personnel, with respect to the service provided to the patients who arrived at the health care center that the Social Security of Ecuador provides to the entire population of the mentioned province and provinces adjacent as Manabí, Esmeraldas, Pichincha, Los Ríos and Cotopaxí. Specifically, attention is paid to the level of stress shown by such staff. For this, the main indicators that can reveal the existence of risk factors that promote work stress are established, such as: low self-esteem, low assertiveness, the inappropriate personality type of their workers, as well as a work environment that fosters stress. Similarly, causal relationships between them are evaluated, based on expert opinion. This is accomplished through the use of Neutrosophic Decision Maps, which generalize the Fuzzy Decision Maps, where the relationships among the criteria are represented by causal relationships that include neutroscopic numbers. An advantage of the method, which as far as the authors know is the first time it is applied in Cognitive Neutrosophic Maps, is the possibility of modeling the indeterminacy, produced by the lack of information, its incompleteness, the contradictory or inconsistent information through the dynamic analysis of these relationships. The obtained weights allowed for a total assessment of the situation, where it was detected that the work environment is the cause that most affects stress and the factors related to the personality of the workers do not significantly affect this problem. On the other hand, it was possible to carry out this study, because there were some undetermined causal relationships among the four variables, so it could not have been modelled with the Fuzzy Cognitive Maps.