Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Pharmacy Administration and Center for Pharmaceutical Marketing and Management, School of Pharmacy, University of Mississippi, University, MS, USA.
J Appl Gerontol. 2024 Apr;43(4):374-385. doi: 10.1177/07334648231210680. Epub 2023 Nov 21.
This paper groups persons who have transitioned into family caregiving using a latent class analysis and examines class differences on measures of well-being. Latent classes were identified for a sample of 251 participants who became family caregivers while participating in a longitudinal national study, and linear regression analyses compared average well-being change scores across classes. Fit indices supported a four-class solution dispersed along two conceptual dimensions: caregiving intensity and caregiving stain. The largest class (35.5%) was characterized as low intensity, low strain. The smallest class (12.7%) was characterized as high intensity, high strain, and these caregivers had significantly worse well-being change scores compared to the other caregiving classes. Categorizing caregivers by differing levels of care intensity and caregiving strain helps identify caregivers who are at most risk for poor psychosocial outcomes, determines which caregivers might benefit from specific caregiver support programs, and informs investigators on possible refinements to interventions.
本文使用潜在类别分析对过渡到家庭护理的人员进行分组，并研究了幸福感衡量标准上的类别差异。对 251 名参与者进行了潜在类别分析，这些参与者在参加一项纵向全国研究时成为了家庭护理人员，线性回归分析比较了各类别之间的平均幸福感变化得分。拟合指数支持了沿着两个概念维度分散的四个类别解决方案：护理强度和护理污名。最大的类别（35.5%）的特点是低强度、低污名。最小的类别（12.7%）的特点是高强度、高污名，与其他护理类别相比，这些护理人员的幸福感变化得分明显更差。通过不同程度的护理强度和护理污名对护理人员进行分类，可以帮助确定最有可能出现不良心理社会结果的护理人员，确定哪些护理人员可能受益于特定的护理人员支持计划，并为干预措施的可能改进提供信息。
