Caregiver Segmentation Using The Integration of The Modified Burden Dimensions and Fuzzy C-Means

• Published in: Procedia computer science Vol. 219; pp. 1200 - 1207
• Main Authors: Rahmayanti, Nabillah, Vinarti, Retno, Djunaidy, Arif, Tjin, Anna, Liu, Jeng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: Caregiver segmentation; Fuzzy C-Means; Indonesian female caregiver; S+PRDG model; Fuzzy C-Means; Indonesian female caregiver; Caregiver segmentation; S+PRDG model
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2023.01.402

The increasing demand for Indonesian female caregivers to Taiwan, raises issues related to caregiver burden. In this study, a segmentation analysis of 299 questionnaire data that consisted of Zarit Burden Interview (ZBI) instrument and four dimensions: personal strain, role strain, dependency, and guilt (PRDG) was conducted to find selection pattern strategies of prospective caregivers based on their characteristics. The results of confirmatory factor analysis (CFA) indicated the need to add “social life” as a new dimension (S+PRDG) representing the caregivers’ social problems, while the results of multiple regression analysis indicated three most influential characteristics of a caregiver: number of children, education level, and work location. The segmentation analysis was carried out using Fuzzy C-Means on the modified PRDG model which is (S+PRDG) and resulted two best segments that have a fuzzy silhouette index value of 0.61. From the analysis of these two segments, the resilient caregivers were those in the second segment with the characteristics such as having children, having level of education up to junior high school, and working in the capital city of Taiwan.