Luna David, Figuerola-Escoto Rosa Paola, Sienra-Monge Juan José Luis, Hernández-Roque Alejandra, Soria-Magaña Arturo, Hernández-Corral Sandra, Toledano-Toledano Filiberto
Unidad de Investigación Multidisciplinaria en Salud, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Calzada México-Xochimilco 289, Arenal de Guadalupe, Tlalpan, Mexico City 14389, Mexico.
Centro Interdisciplinario de Ciencias de la Salud Unidad Santo Tomás, Instituto Politécnico Nacional, Av. de Los Maestros s/n, Santo Tomás, Miguel Hidalgo, Mexico City 11340, Mexico.
Healthcare (Basel). 2023 Nov 26;11(23):3042. doi: 10.3390/healthcare11233042.
The aim of this study was to use latent profile analysis to identify specific profiles of burnout syndrome in combination with work engagement and to identify whether job satisfaction, psychological well-being, and other sociodemographic and work variables affect the probability of presenting a profile of burnout syndrome and low work enthusiasm. A total of 355 healthcare professionals completed the Spanish Burnout Inventory, the Utrecht Work Engagement Scale, the Job Satisfaction Scale, and the Psychological Well-Being Scale for Adults. Latent profile analysis identified four profiles: (1) burnout with high indolence (BwHIn); (2) burnout with low indolence (BwLIn); (3) high engagement, low burnout (HeLb); and (4) in the process of burning out (IPB). Multivariate logistic regression showed that a second job in a government healthcare institution; a shift other than the morning shift; being divorced, separated or widowed; and workload are predictors of burnout profiles with respect to the HeLb profile. These data are useful for designing intervention strategies according to the needs and characteristics of each type of burnout profile.
本研究的目的是使用潜在剖面分析来识别倦怠综合征与工作投入相结合的特定剖面,并确定工作满意度、心理健康以及其他社会人口学和工作变量是否会影响呈现倦怠综合征和低工作热情剖面的概率。共有355名医疗保健专业人员完成了西班牙倦怠量表、乌得勒支工作投入量表、工作满意度量表和成人心理健康量表。潜在剖面分析确定了四种剖面:(1)高度怠惰型倦怠(BwHIn);(2)低度怠惰型倦怠(BwLIn);(3)高投入、低倦怠(HeLb);以及(4)倦怠进行中(IPB)。多变量逻辑回归显示,在政府医疗机构从事第二份工作;非早班的轮班;离婚、分居或丧偶;以及工作量是相对于HeLb剖面的倦怠剖面的预测因素。这些数据有助于根据每种倦怠剖面的需求和特征设计干预策略。