Exploration of stress, burnout and technostress levels in teachers. Prediction of their resilience levels using an artificial neuronal network (ANN)

Abstract

This study explores stress, burnout syndrome, resilience, and technostress in 168 teachers in Region of Murcia. The general objective was to predict the teacher's resilience levels, as well as analyse the relationship between the variables under study and see the influence of age and gender. The results achieved showed statistically significant relationships in the correlational analysis between stress, technostress, emotional exhaustion, and depersonalisation. Analyses on resilience showed a significant and negative relationship with factors the factors above, but a positive and statistically significant relationship with personal accomplishment. Also, we found age effects on technostress and stress. Furthermore, an artificial neural network (ANN) was created, obtaining a model with a capacity to predict resilience levels in an 86.7% of cases. Personal accomplishment is the most relevant factor to predict resilience levels in teachers, although stress, age and gender are also important.