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Year : 2022  |  Volume : 8  |  Issue : 1  |  Page : 14

A new approach to Maslach Burnout Inventory: Measuring burnout syndrome in health-care staff with fuzzy conjoint analysis

1 Department of Computer Programming, Vocational School, Ostim Technical University, OSTIM, Yenimahalle, Ankara, Turkey
2 Department of Business Administration, Faculty of Economics Administrative and Social Sciences, Antalya Bilim University, Antalya, Turkey

Correspondence Address:
Guney Gursel
Department of Computer programming, Vocational School, Ostim Technical University, OSTIM, Yenimahalle, Ankara
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/digm.digm_2_22

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Background and Purpose: Burnout syndrome (BOS), the popular phenomenon of our pandemic era, is examined in three dimensions: emotional exhaustion, depersonalization and cynicism, and personal inefficiency. One of the known and accepted ways of measuring BOS is Maslach Burnout Inventory (MBI), in which these three dimensions are measured by 22 items, using 5- or 7-point Likert scales. The aim of this study is to eliminate the loss of precision in BOS measurement and handle the subjectivity and uncertainty, as a result, to get rid of the bias caused by the classical way. Methods: To do this, fuzzy conjoint analysis (FCA) is used together with MBI. In the classical way, the calculations are made by assigning crisp values to the answers, which causes scientific bias and loss of precision because Likert scale type answers have subjectivity and uncertainty. Results: When the scores obtained with FCA are examined, all the scores and some BOS levels differ. When the position of the values according to the borders of the BOS levels is taken into account, it can be said that these tiny differences caused by the loss of precision make this difference. Conclusion: Findings show that the resulting scores changed significantly when calculations are made with FCA. Especially, when these scores are interpreted as intervals or grades, as in MBI, even tiny differences may result in significant scientific bias.

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