adaptive and maladaptive perfectionism, and professional burnout among medical laboratory scientists

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ID: 184512
2018
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Abstract
Background The goal of this paper is to verify the correlations between adaptive and maladaptive perfectionism and the selected demographic and job characteristics vs. professional burnout among medical laboratory scientists in Poland. Material and Methods The study group consisted of 166 laboratory scientists. The Polish Adaptive and Maladaptive Perfectionism Questionnaire (Szczucka) was used for testing perfectionism. The Oldenburg Burnout Inventory was used for examining burnout syndrome. Results Adaptive perfectionism was positively and maladaptive perfectionism was negatively correlated with both aspects of professional burnout: the disengagement from work and exhaustion. What is more, maladaptive perfectionism was correlated negatively with age and work experience. People in relationships have a higher level of disengagement and a higher level of exhaustion than single ones. The results of hierarchical regression analyses have revealed, after having controlled selected demographic and job factors, that a significant predictor of disengagement is the high level of adaptive perfectionism and low level of maladaptive perfectionism. In addition, a significant predictor of high level of exhaustion is the low level of maladaptive perfectionism. Conclusions Professional burnout among medical laboratory scientists is of a specific nature. The “healthier” perfectionism they reveal, the higher level of burnout they present. In this profession, lower risk of burnout is represented by those who are characterized by the lack of confidence in the quality of their actions and a negative reaction to their own imperfections associated with imposed social obligation to be perfect. The individuals pursuing their internal high standards experience burnout faster. Med Pr 2018;69(3):253–260
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Authors ;Marlena Robakowska;Anna Tyrańska-Fobke;Maciej Walkiewicz;Małgorzata Tartas
Journal journal of bioinformatics and computational biology
Year 2018
DOI
10.13075/mp.5893.00644
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Keywords

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