Abstract
Accurate prediction provides a number of important benefits for research and decision-making. Occupational burnout is intertwined with individual, cultural, and social factors, the resolution of which requires methods that can deal with large amounts of data. The application of such methods capable of dealing with large datasets is a relatively novel research area in social science. For this purpose, this article presents insights into machine learning methods, mainly related to prediction tasks. A brief review of these techniques in burnout domain was applied. It is shown that the choice of a method depends on the presence of certain dependent variables. This paper also presents a comparison between novel and traditional approaches, which shows that the appropriateness of a technique depends on the aim of the research. The theoretical and practical implications of using machine learning methods in this context is also presented in the paper. It is found that a gap in the study of burnout exists which requires the attention of social work researchers. Through machine learning techniques, new theoretical models of burnout can be created. These algorithms can also provide new approaches to create data-driven interventions. Burnout monitoring systems supported by machine-learning algorithms can also be used in recruitment processes and to supervise employees. Applying machine learning methods in reducing burnout can also provide socio-economic benefits such as help to reduce employee turnover and improve general working conditions.