Depression Risk Prediction Based on IV-Probit and Transformer for Community Management

In modern society, community management has gained more attention on children, such as carrying out the three-child policy. On the contrary, the elderly seems be ignored, so this paper researches the correlation between the quantity of children and the well-being of the elderly. Employing data from the China Health and Retirement Longitudinal Study (CHARLS), this research employs an IV-Probit model to elucidate the correlation between the number of offspring and the aged mental well-being, specifically depression symptoms. The result suggests that (1) Chinese people have fertility preference for mix-sex; (2) and parents with more children had a remarkable negative impact on depression rate when endogeneity was not taken into account, but the offspring quantity had a non-significant protective effect on parental mental health when estimated using instrumental variables. In addition, with the aging of society, the mental health problems of the elderly have attracted much attention. This article uses the Transformer model to more accurately capture the diverse factors of depression risk in the elderly and make effective predictions through the advantages of powerful long-range dependency modeling, self-attention mechanism, and multi-head self-attention.