Simulation of Burnout Processes by a Multi-Order Adaptive Network Model

In this paper, an adaptive network model for the development of and recovery from burnout was designed and analysed. The current literature lacks adequate adaptive models to describe the processes involved in burnout. In this research, the informal conceptual models from Golembiewski and Leiter-Maslach model were combined with additional first-and second-order adaptive components and used to design a computational network model based on them. Four different scenarios were simulated and compared, where the importance of the therapy and the ability to learn from it was emphasised. The results show that if there was no therapy present, the emotional regulation was too poor to have an effect. However, at the moment therapy was applied, the emotional regulation would start to help against burnout. Another finding was that one long therapy session has a greater effect than several shorter sessions. Lastly, therapy only had a significant long-lasting effect when adaquate neuro-plasticity occurred.