通过倦怠评估工具 (BAT) 重新审视九个国家样本中的全球倦怠评分

L. D. de Beer, W. Schaufeli, H. De Witte, J. Hakanen, Janne Kaltiainen, Jürgen Glaser, Christian Seubert, Akihito Shimazu, Janine Bosak, Jakub Procházka, Aleš Kajzar, M. Christensen
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引用次数: 0

摘要

摘要:近年来,有关职业倦怠评估工具(BAT)有效性的研究在文献中越来越受欢迎。当 BAT 被建立为二阶/高阶模型时,已成功证明其在代表性样本中具有等效性。然而,这种建模方法也并非没有受到批评,双因素方法被认为是另一种建模方法。因此,有必要在众多代表性样本中对 BAT 的建构相关多维性进行研究,以重新评估全球倦怠因子(n = 9,041)。我们采用了双因素探索性结构方程模型(bifactor exploratory structural equation modeling)来确定总体倦怠因素和特定组成因素(bifactor-ESEM)的相关性。根据标准化载荷和麦克唐纳ω系数,结果表明双因素-ESEM 模型有一个强大的整体倦怠因素,在整体因素之外还有相关的特定因素。该模型还显示出不同国家和性别间的测量不变性。我们还展示了一张图,比较了各国的总体倦怠平均得分。总之,本研究的结果再次证实,BAT 评估的倦怠可以通过不同条件下的等效总体倦怠得分来建模。
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Revisiting a Global Burnout Score With the Burnout Assessment Tool (BAT) Across Nine Country Samples
Abstract: Studies published on the validity of the Burnout Assessment Tool (BAT), a novel burnout instrument, have gained traction in the literature over recent years. The BAT has been successfully shown to be equivalent across representative samples when modeled as a second-order/higher-order model. However, this specification is not free of criticism and the bifactor approach has been presented as the alternative model specification. Therefore, a study investigating the construct-relevant multidimensionality of the BAT across many representative samples is warranted to reassess a global burnout factor ( n = 9,041). We implemented bifactor exploratory structural equation modeling to ascertain the relevance of a global burnout factor and specific component factors (bifactor-ESEM). According to the standardized loadings and McDonald’s ω coefficients, the results showed that the bifactor-ESEM model had a strong global burnout factor with relevant specific factors beyond the global factor. The model also showed measurement invariance across countries and genders. We also present a figure that compares the global burnout mean scores of the countries. All in all, the results of this study reaffirmed that BAT-assessed burnout can be modeled with an equivalent global burnout score across conditions.
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