Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation

Abstract

In 2002, the Colombian ministry of education released statute 1278, for teaching professionalization, superseding statute 2277 of 1977. Although statute 1278 was intended to increase the quality of the education service and teachers’ remuneration, there is evidence that the abundant evaluations and hindered promotion system introduced by statute 1278 resulted in an impairment of the quality of life of the teachers, and a higher incidence of burnout syndrome. We used two techniques for machine learning interpretability, SHapley Additive exPlanation summary plots and predictor importance, to interpret support vector machine and decision tree machine learning models, respectively, to better understand the differences on risk factors and symptoms of burnout syndrome in school teachers under statutes 2277 and 1278. We have surveyed 54 school teachers between August and October 2018, 17 under statute 2277, and 37 under statute 1278. Among the risk factors and symptoms of burnout syndrome considered in this study, we found that the satisfaction with earnt income was the most relevant risk factor, followed by the overtime work and the perceived severity of the sanctions on lower performance. The most relevant symptoms of burnout were fatigue at the end of the day, and frequent headaches. This methodology can be potentially used in other contexts and social groups, allowing institutional authorities and policy makers to allocate resources to specific issues affecting a particular group of workers.

Keywords

• burnout
• statute 2277
• statute 1278
• machine learning
• teaching