倦怠
心理学
收敛有效性
比例(比率)
社会心理学
情绪衰竭
度量(数据仓库)
玩世不恭
判别效度
标准效度
应用心理学
职业倦怠
心理测量学
结构效度
临床心理学
计算机科学
数据挖掘
内部一致性
法学
物理
政治
量子力学
政治学
作者
Cindy P. Muir,Charles Calderwood,O. Dorian Boncoeur
摘要
The evidence is overwhelming and ubiquitous; job burnout is a prevalent occupational syndrome with substantial costs. Although prevention and treatment are vital, both necessitate identifying job burnout itself, yet existing measures are long and sometimes proprietary. Because lengthy surveys are generally seen as too time-consuming, especially in contexts where rapid identification of job burnout is paramount and may be associated with increased measurement error for people experiencing burnout, there is a strong need for a quick and regular assessment of job burnout. Not surprisingly, many scholars have resorted to shortening existing scales. However, those efforts have seldom attended to the corresponding validation concerns of this approach. Our work aims to develop and validate a visual burnout scale using matches that can be deployed rapidly and consistently, as visual scales provide a way for people to more easily articulate their feelings. Our novel analytic approach entailed Bayesian comparisons of the effect sizes generated with our measure to published meta-analytic effect size estimates, evaluations of the convergence of our measure with existing job burnout scales, and comparisons of the overlap between our measure and existing scales as they relate to burnout antecedents and outcomes. Across multiple preregistered studies surveying over 1,200 participants in various industries, our results demonstrate that our visual scale shows strong convergent validity, criterion-related validity, and test-retest reliability. Our measure also compares favorably with the three most widely used burnout measures in organizational scholarship (the Maslach Burnout Inventory, Shirom-Melamed Job Burnout Measure, and Oldenburg Job Burnout Inventory) and, in some cases, demonstrated incremental validity beyond existing measures. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
科研通智能强力驱动
Strongly Powered by AbleSci AI