고령자의 사회적 건강 변화궤적에 대한 잠재계층분류와 예측요인 : 성장혼합모형(Growth Mixture Model)을 중심으로

Abstract

Objective: The purpose of this study is to identify the latent classes for a change in trajectory of the level of social health of older adults who have gone through the COVID-19 pandemic and analyze the characteristics of each class, thereby identifying predictive factors affecting the social health of older adults. Methods: To analyze the types of changes in the trajectory of the level of social health, Korean Welfare Panel Study (KOWEPS) data (2019~2021) was used. The subjects of the study were 2845 older adults who responded at all three time points. The Growth Mixed Model (GMM), which is a subject-centered approach, was applied to analyze the latent classes. In addition, χ2 and ANOVA were conducted to identify the characteristics of each latent type, and a multinominal logistic regression was applied to analyze the influencing factors between classes. Results: As the results of applying GMM indicate, the latent classes for the changes in trajectory of social health were classified into four groups: a low level decrease-increase group, a medium level maintenance-increase group, a high level decrease group, and a high level maintenance group. There was a difference in leisure satisfaction and demographic between classes. In terms of the influencing variables, it was analyzed that the greater the number of women, the more religious the individuals, and the higher their leisure and overall life satisfaction, the higher their probability of belonging to a high level maintenance group. Conclusion: By applying GMM, this study identified the latent class of the changes in trajectory of the social health level of older adults, and it was found that there were significant differences in leisure satisfaction according to the various types. In addition, the influencing factors that distinguish social health were also analyzed.