Modified PRDG Model for Caregiver Segmentation Using Zarit Burden Interview Instrument
DOI:
https://doi.org/10.33736/jcsi.4317.2022Keywords:
Caregivers, S PRDG Model, ZBI Instruments, K-Means Clustering, Elbow Method, Silhouette IndexAbstract
The increasing demand for Indonesian workers in Taiwan has an impact on caregiver problems which can be triggered by the burden of caring for the elderly. Therefore, the aim of this study is to identify the characteristics of caregivers who are resilient to burdens based on Indonesian female caregivers who work in Taiwan data to be a guide for selecting prospective caregivers. The process includes analyzing the personal characteristics that have the most influence on the burden using multiple regression and then clustering caregiver data using K-Means with the Elbow Method and Silhouette Index. Then, segmentation in each cluster based on a comparison of the average values. The results of clustering accuracy on dimensions (PRDG) and modified dimensions (S+PRDG) were compared and the smallest error cluster was in case 4 in the S+PRDG dimension with the Elbow Method of 3.6%. Based on segmentation on that dimension, cluster 2 is a resilient caregiver cluster. Then the results of the multiple regression analysis (Number of Children, Education and Work Location) were studied further for each caregiver in cluster 2 and the conclusions are, their average number of children is 1, final education is in junior high school and their work location is in the capital of Taiwan.
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