Influência das tecnologias de informação e comunicação sobre a síndrome de Burnout em docentes de enfermagem

Introduction: higher education in Brazil and in the world has gone through several transformations having to fit the new demands that arise in the market. In nursing, teachers have been challenged to deal with new ways of teaching, based on the use of information and communication technologies, demanding more time and dedication for the teacher. Most of the time teachers are faced with overcrowded classrooms, low rates of pay, interpersonal conflicts, facts that contribute to the worker's illness by burnout syndrome. This study supports the view that the occurrence of burnout syndrome is beyond already defined characteristics, with the need to delve into new working relationships with the use of information and communication technologies. Objective: to investigate the influence of information and communication technologies on Burnout Syndrome in nursing teachers. Methodology: it is an exploratory, descriptive, transversal and explanatory study, with a quantitative approach. It was used: questionnaire of sociodemographic and professional data, Questionnaire for the Assessment of Quemarse Syndrome by el Trabajo, Questionnaire of Predictive Factors of Burnout Syndrome, and Questionnaire for the use of information technologies in teaching work. The decision models used were: Wheight of Evidence or Weight of Evidence, Latent Class Analysis, Cluster Analysis, Decision Tree and Crombach's Alpha. This study was submitted to the Ethics and Research Committee of CCS / UFPB, having obtained the approved opinion, with CAAE nº 43914115.4.0000.5188. The study took place at a public university and two private colleges, with 112 nurse professors from the undergraduate nursing course. Latent Class Analysis and Cluster Analysis identified a model composed of three classes: low risk, medium risk and high risk. Results: through Latent Class Analysis, 33 (29.5%) teachers present Burnout Syndrome. Through the Weight of Evidence Model, it was found that indolence is the dimension with the highest prediction for the identification of ...