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个体和项目特征可能会影响照顾者参与定制支持干预后的结果的变异性。

Individual and program Characteristics May Drive Variability in Outcomes After Caregivers Participate in a Tailored Support Intervention.

机构信息

Durham VA Health Care System, Durham, NC, USA.

Department of Population Health Sciences, Duke University, Durham, NC, USA.

出版信息

J Appl Gerontol. 2022 Aug;41(8):1960-1970. doi: 10.1177/07334648221091564. Epub 2022 May 16.

Abstract

Critically needed programs designed to support family caregivers have shown inconsistent reductions in stress and burden. To explore drivers of improvement in caregiver outcomes after participation in a support intervention we analyzed data from a one-on-one, tailored problem-solving intervention targeting caregiver wellbeing (2015-2019, = 503). We explored data patterns across 21 individual, household, and program-level variables using elastic net regression to identify drivers of improvements, and their relative importance. Baseline subjective burden, baseline depressive symptom scores, baseline caregiver problem solving, African American race, and site and coach fixed effects were the most consistent drivers of changes across the explored caregiver outcomes. Caregiver and program characteristics may be promising avenues to target to decrease distress and burden during intervention design. Interventions focusing on highly distressed caregivers may lead to greater improvements. More research is needed to identify how site or interventionists characteristics drive positive intervention effects.

摘要

迫切需要设计支持家庭照顾者的项目,以减轻他们的压力和负担。为了探讨参与支持干预后照顾者结果改善的驱动因素,我们分析了一项针对照顾者福祉的一对一、量身定制的解决问题干预的数据(2015-2019,n=503)。我们使用弹性网络回归分析了 21 个个体、家庭和项目水平变量的数据模式,以确定改善的驱动因素及其相对重要性。基线主观负担、基线抑郁症状评分、基线照顾者解决问题能力、非裔美国人种族以及地点和教练固定效应是探索的照顾者结果中变化的最一致驱动因素。照顾者和项目特征可能是干预设计中减少压力和负担的有希望的目标。关注高度痛苦的照顾者的干预可能会带来更大的改善。需要进一步的研究来确定地点或干预者特征如何推动积极的干预效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea1/9364230/372199332c0c/10.1177_07334648221091564-fig2.jpg

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