{"title":"Exploration of stress, burnout and technostress levels in teachers. Prediction of their resilience levels using an artificial neuronal network (ANN)","authors":"","doi":"10.1016/j.tate.2024.104717","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":48430,"journal":{"name":"Teaching and Teacher Education","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0742051X2400249X/pdfft?md5=319c51bd2a2be5fe407cdcdb98bdc26b&pid=1-s2.0-S0742051X2400249X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Teaching and Teacher Education","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0742051X2400249X","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Teaching and Teacher Education is an international journal concerned primarily with teachers, teaching, and/or teacher education situated in an international perspective and context. The journal focuses on early childhood through high school (secondary education), teacher preparation, along with higher education concerning teacher professional development and/or teacher education. Teaching and Teacher Education is a multidisciplinary journal committed to no single approach, discipline, methodology, or paradigm. The journal welcomes varied approaches (qualitative, quantitative, and mixed methods) to empirical research; also publishing high quality systematic reviews and meta-analyses. Manuscripts should enhance, build upon, and/or extend the boundaries of theory, research, and/or practice in teaching and teacher education. Teaching and Teacher Education does not publish unsolicited Book Reviews.