痴呆
医疗保健
护理部
心理学
家庭健康
老年学
医学
政治学
病理
法学
疾病
作者
Julia Burgdorf,Jennifer Reckrey,David Russell
出处
期刊:Gerontologist
[Oxford University Press]
日期:2022-11-01
卷期号:63 (5): 874-886
被引量:16
标识
DOI:10.1093/geront/gnac165
摘要
Abstract Background and Objectives Identifying and meeting the needs of family and unpaid caregivers (hereafter, “caregivers”) during home health (HH) can improve outcomes for patients with Alzheimer’s Disease and Related Dementias (ADRD). However, little is known regarding ADRD caregivers’ perspectives on communication and support from the HH care team. The study objectives were to identify ADRD caregivers’ common support needs during HH and preferences for addressing these needs, to inform future development of an assessment and support intervention. Research Design and Methods We conducted semistructured key informant interviews with caregivers who had recently assisted a HH patient with ADRD (n = 27). Interview transcripts were analyzed using directed content analysis. Results Caregivers identified four major support needs: assistance navigating insurance and service coverage, training on nursing tasks, referral to respite care, and information regarding ADRD disease progression. Caregivers described major barriers to communicating these needs, including never being directly asked about their needs and information discontinuity within the HH care team. Incorporating caregiver recommendations, we propose a new model of assessment and support in which the HH care team (a) proactively asks about caregiver needs, (b) presents available supportive resources, (c) solicits information regarding the patient’s needs and routine, and (d) stores and shares this information within the medical record. Discussion and Implications Findings reveal critical gaps in current patterns of support for ADRD caregivers during HH and suggest directions for an assessment and support intervention that explicitly queries caregivers on their capacity and needs, with content tailored to the HH setting.
科研通智能强力驱动
Strongly Powered by AbleSci AI