In this project, detecting the burnout syndrome with the power of natural language processing allows to prepare for the future's digital methods in psychology.
- Lead department School of Engineering and Computer Science
- Institute Institute for Data Applications and Security (IDAS)
- Research unit IDAS / Data Science and Engineering (DSE)
- BFH centre BFH Centre for the Digital Society
- Funding organisation SNSF
- Duration (planned) 01.03.2021 - 28.02.2022
- Project management Prof. Dr. Mascha Kurpicz-Briki
- Head of project Prof. Dr. Mascha Kurpicz-Briki
- Keywords burnout, psychology, natural language processing, computer science, ai4socialgood
To identify burnout in clinical intervention, inventories are used. Inventories are psychological tests, where the person concerned fills out a questionnaire. The currently used metric, in both practice and most studies, measures burnout with self-test inventories, and is criticized in the literature. The state-of-the-art does not use free-text questions in the inventories or interviews, even though there have been promising approaches in the literature.
Course of action
In this project we propose to investigate on a new approach of burnout detection based on open questions, analyzed using the power of traditional and contextualized word embeddings.
The approach developed in this project will enable new methods to measure burnout, both in clinical intervention and in preventive measures.