SYSTEMATIZATION OF BURNOUT MODELS IN THE CONTEXT OF CONTEMPORARY SCIENTIFIC PERSPECTIVES

Abstract

The scientific literature presents numerous models of burnout that differ in their theoretical foundations, interpretations of the nature and structure, of the phenomenon, contributing factors, and development mechanisms. However, the absence of a unified classification system complicates the comparison of different theoretical approaches and the identification of relationships among them. Therefore, the systematization and classification of existing burnout models based on shared conceptual and substantive characteristics reman important research tasks. This study aims to classify burnout models through an analysis of their theoretical propositions, structural components, and functional orientations. The study employed retrospective and comparative analysis, generalisation, and the classification of scientific approaches. Retrospective analysis made it possible to trace the evolution of scholarly perspectives on burnout and identify key directions in the development of existing models. Authors identified four main types of burnout models: factorial, processual, conceptual, and applied. Factorial models describe the structural components of burnout, processual models explain its developmental dynamics, conceptual models address multilevel contributing factors, and applied models focus on the development of diagnostic and preventive interventions. Unlike previous classification, the proposed framework considers not only the origins of burnout-related factors but also the structure, causes, and mechanisms of burnout development, as well as its diagnosis and prevention. The scientific novelty of the study lies in the proposed classification of burnout models, which provides a foundation for developing diagnostic tools and preventive programs. The practical significance of the findings derives from their potential application within the education system of the Republic of Kazakhstan. Future research should focus on developing contextually adapted diagnostic methods and burnout-prevention programmes incorporating contemporary digital technologies, including artificial intelligence.