Introducing effective parameters for predicting job burnout using a self-organizing method based on group method of data handling neural network

Tingting Fan and Ehsan Nazemi

PLOS ONE, 2023, vol. 18, issue 11, 1-12

Abstract: In addition to affecting people’s bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach’s alpha confirmed the polls’ dependability, and an expert panel confirmed the surveys’ authenticity. The GMDH neural network’s inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network’s output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0290267

DOI: 10.1371/journal.pone.0290267

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