Applied Sciences (Apr 2022)
Fuzzy Approach to Computational Classification of Burnout—Preliminary Findings
Abstract
There is a common belief that medical professions generate more work-related stress and earlier job burnout. We tested two groups: study group 1: medical (physical therapists, n = 30), and study group 2: non-medical (informaticians, n = 30). The purpose of this study was to find new, more reliable models for calculating work-related stress and burnout in the two aforementioned different professional groups. In the paper, we focused on a new model of algorithm based on AI methods that extends the interpretability of the scale of results obtained using the MBI test. The outcomes of the Maslach Burnout Inventory (MBI) were analysed in both study groups. These became the starting point for the development of three different fuzzy models, from which, after comparison, the one best suited to the study groups and the way they were evaluated was selected. Among the patients participating in the study, the following results were obtained: MBI values expressed as median values were significantly higher in group 2 than in group 1. The computational analysis showed that the contribution of the different parts of the MBI test to the final score was unequal in both groups. AI allowed for optimal selection of the model parameters for the study group, from which an algorithm was created to optimise the selection of tools or their parameters. A computational tool can do this faster, more accurately, and more efficiently, becoming an important supporting tool. In the medical context, the main benefit of the results presented in this paper is the definition of an evaluation model that transforms the MBI test scores into a universal percentage scale while preserving the properties of the guidelines underlying the MBI. An additional advantage of the proposed solution is the readability and flexibility resulting from the linguistic rules underlying the model.
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