The Role of a Social Support System in Alleviating Multidimensional Stress among Family Caregivers of Older Adults with Disability and Dementia: An Exploratory Study

Abstract

Against the backdrop of global population aging posing severe social challenges, the long-term care needs of older adults with disability and dementia are becoming increasingly prominent, straining healthcare and social welfare systems worldwide. Family caregivers, as the cornerstone of the care system, universally experience profound and multidimensional stress while fulfilling their responsibilities, often at significant personal cost ^[1]. This exploratory study employed a mixed-methods approach, combining standardized questionnaire surveys and semi-structured in-depth interviews with 30 caregivers. The aim was to preliminarily delineate their stress status across key domains and construct a comprehensive social support framework grounded in Bronfenbrenner’s ecological systems theory. The findings indicate that caregivers exhibit alarmingly high-stress levels across physiological (mean sleep quality self-assessment 4.25/5) ^[2], psychological (mean anxiety/depression tendency 4.10/5) ^[3], economic (mean care expenditure as a percentage of household income 58.3%) ^[4], and social (mean social interaction frequency 4.23/5) ^[5] dimensions. Qualitative data further contextualized these scores, revealing narratives of chronic exhaustion, anticipatory grief, financial precarity, and profound social isolation. Consequently, this study proposes a multi-tiered social support system encompassing micro (individual and family empowerment through skill training and psychological services) ^[6], meso (community support network construction via respite care and peer groups) ^[7], and macro (policy safeguards and cultural shifts) ^[8] levels. It aims to provide preliminary theoretical insights and practical pathways for systematically alleviating caregiver stress and building a resilient, sustainable care ecosystem. The study concludes by acknowledging its sampling limitations and outlining critical directions for future research, including large-scale validation, subgroup analysis, and the integration of digital health technologies ^[9].