Risk factors associated with burnout syndrome in teachers in the province of Carchi. (Innovation & Development in Engineering and Applied Science, 2025)
Summary. Burnout syndrome is widely known as a work-related problem that afflicts teachers worldwide. This problem related to risk factors that generate stress arises due to the work environment and various situations related to dealing with people who demand a high level of dedication and involvement. The present study focused on identifying risk factors associated with burnout syndrome in teachers in the province of Carchi. The main objective was to validate the adaptation of the Maslach Burnout Inventory (MBI) to the specific educational context, and to propose a model of analysis of results based on artificial intelligence. When validating the instrument, it was identified that it was not completely adapted to the local context, so the instrument was modified, having to remove 5 questions that were identified as not relevant by the Confirmatory Factor Analysis, and demographic information of the participants considered of interest to the participating institutions was incorporated. Thus, for the extraction of results from the test, a new results processing model was designed based on multivariate techniques, contemplating 37 input variables. For this, multiple alternatives were considered, including binary classifiers, shallow neural networks and five deep learning models, finding that deep learning, especially a five-layer model developed with TensorFlow and Keras, offered the most accurate predictions of emotional exhaustion, a key dimension of burnout syndrome. This model achieved an accuracy of 86% and an MSE of 0.1193604, demonstrating its reliability for automatic detection of the syndrome without the need for professional diagnosis. The research validates the adaptation of the instrument to the target context and highlights the importance of establishing new models of analysis when using instruments modified from the MBI, ensuring their relevance and applicability in specific contexts.
Keywords: Burnout Syndrome; Risk Factors; Artificial Neuronal Rojo