000			01726nam a22001937a 4500
005			20250117164632.0
008			250117b |||||||| |||| 00| 0 eng d
040			_cLDD
100			_aPagan-Garbin, Ines
245			_aExploration of stress, burnout and technostress levels in teachers. Prediction of their resilience levels using an artificial neuronal network (ANN)
260			_aElsevier, 2024
300			_bp. 1-11
490			_aAn International Journal Of Research And Studies _vVol. 148
500			_aThis 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.
650			_aArtificial Intelligence
700			_aMendez, Inmaculada
856			_yclick here to access online _uhttps://www.sciencedirect.com/science/article/pii/S0742051X2400249X
942			_2ddc _cARTICLES
999			_c194417 _d194417