@misc{10481/70320,
year = {2021},
url = {http://hdl.handle.net/10481/70320},
abstract = {Emotional exhaustion, cynicism, and work inefficiency are three dimensions that define
burnout syndrome among teachers. On another note, resilience can be understood as the ability to
adapt to the environment and overcome adverse situations. In addition, COVID-19 has provided
a threatening environment that has led to the implementation of resilience strategies to struggle
with burnout and cope with the virus. The aim of this study was to analyze the relationship
between resilience, burnout dimensions, and variables associated with COVID-19 through the design
of an artificial neural network architecture. For this purpose, the Maslach Burnout InventoryGeneral Survey (MBI-GS), the Brief Resilience Coping Scale (BRCS), and a questionnaire on stress
towards COVID-19 were administered to 419 teachers from secondary schools in southeastern
Spain (292 females; 69.7%). The results showed that 30.8% suffered from burnout (high emotional
exhaustion, high cynicism, and low professional efficacy) and that 38.7% had a high level of resilience,
with an inverse relationship between both constructs. Likewise, we modelled an ANN able to predict
burnout syndrome among 97.4% of teachers based on its dimensions, resilience, sociodemographic
variables, and the stress generated by COVID-19. Our conclusions shed some light on the efficacy
of relying on artificial intelligence in the educational field to predict the psychological situation of
teachers and take early action.},
publisher = {MDPI},
keywords = {Artificial intelligence},
keywords = {Artificial neural networks},
keywords = {Burnout},
keywords = {COVID-19},
keywords = {Resilience},
keywords = {Teachers},
title = {Burnout, Resilience, and COVID-19 among Teachers: Predictive Capacity of an Artificial Neural Network},
doi = {10.3390/app11178206},
author = {Martínez Ramón, Juan Pedro and Morales Rodríguez, Francisco Manuel and Pérez López, Sergio},
}