Journal of Korean Academy of Nursing

Original Articles

A Prediction Model for Unmet Needs of Elders with Dementia and Caregiving Experiences of Family Caregivers: Sora Choi, Myonghwa Park; J Korean Acad Nurs 2016;46(5):663-674. Published online October 31, 2016; DOI: https://doi.org/10.4040/jkan.2016.46.5.663

Purpose

The purposes of this study were to develop and test a prediction model for caregiving experiences including caregiving satisfaction and burden in dementia family caregivers.

Methods

The stress process model and a two factor model were used as the conceptual frameworks. Secondary data analysis was done with 320 family caregivers who were selected from the Seoul Dementia Management Survey (2014) data set. In the hypothesis model, the exogenous variable was patient symptomatology which included cognitive impairment, behavioral problems, dependency in activity of daily living and in instrumental activity of daily living. Endogenous variables were caregiver's perception of dementia patient's unmet needs, caregiving satisfaction and caregiving burden. Data were analysed using SPSS/WINdows and AMOS program.

Results

Caregiving burden was explained by patient symptomatology and caregiving satisfaction indicating significant direct effects and significant indirect effect from unmet needs. The proposed model explained 37.8% of the variance. Caregiving satisfaction was explained by patient symptomatology and unmet needs. Mediating effect of unmet needs was significant in the relationship between patient symptomatology and caregiving satisfaction.

Conclusion

Results indicate that interventions focusing on relieving caregiving burden and enhancing caregiver satisfaction should be provided to caregivers with high levels of dementia patients' unmet needs and low level of caregiving satisfaction.

Citations

Citations to this article as recorded by

Experiences of Family Caregivers Utilizing Care Support of Dementia Center
Chun-Gill Kim, Myung Soon Kwon, Young Hee Lee
Korean Journal of Adult Nursing.2018; 30(3): 314. CrossRef

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Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis: Myonghwa Park, Sora Choi, A Mi Shin, Chul Hoi Koo; J Korean Acad Nurs 2013;43(1):1-10. Published online February 28, 2013; DOI: https://doi.org/10.4040/jkan.2013.43.1.1

Abstract PDF

Purpose

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method.

Methods

A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs.

Results

The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease.

Conclusion

The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Citations

Citations to this article as recorded by

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