치매노인 부양자의 부양부담과 생활만족도 예측모형
A Prediction Model for Caregiver Burden and Life Satisfaction - Caring for a Patient with Dementia -
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초록
Purpose: The purpose of this research was to identify a causal structural model of burden of care and quality of life for caregivers of patients with dementia. The model would provide a theoretical framework for reduction of caregiver burden and increased quality of life. Methods: This study was designed to construct a model that predicts care burden and life satisfaction of caregivers of elders with dementia. The study was conducted using a convenience sampling method to select 180 family caregivers for people with dementia from 8 daycare centers in Incheon, Korea. Data were collected using questionnaires, and analyzed using descriptive statistics and correlation analysis with the SAS 10.0 program. The Linear Structural Modeling (LISREL) 8.13 program was used to find the best fit model. Results: The variables predicting caregiver life satisfaction were depression, fatigue,perceived health status and willingness to care. These variables explained 65% of the variance in life satisfaction. The variables predicting caregiver burden were depression, fatigue and functional status of elders. These variables explained 69% of the variance in caregiver burden in this model. Conclusion: The above results indicate the need to develop intervention programs which will reduce the care burden and promote life satisfaction in caregivers.
Abstract
Purpose: The purpose of this research was to identify a causal structural model of burden of care and quality of life for caregivers of patients with dementia. The model would provide a theoretical framework for reduction of caregiver burden and increased quality of life. Methods: This study was designed to construct a model that predicts care burden and life satisfaction of caregivers of elders with dementia. The study was conducted using a convenience sampling method to select 180 family caregivers for people with dementia from 8 daycare centers in Incheon, Korea. Data were collected using questionnaires, and analyzed using descriptive statistics and correlation analysis with the SAS 10.0 program. The Linear Structural Modeling (LISREL) 8.13 program was used to find the best fit model. Results: The variables predicting caregiver life satisfaction were depression, fatigue,perceived health status and willingness to care. These variables explained 65% of the variance in life satisfaction. The variables predicting caregiver burden were depression, fatigue and functional status of elders. These variables explained 69% of the variance in caregiver burden in this model. Conclusion: The above results indicate the need to develop intervention programs which will reduce the care burden and promote life satisfaction in caregivers.
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