Yasuhiro MINAMI (南 泰浩)

ABSTRACT

Background

Nurturing environments have a critical influence on children's language development. It is unclear to what extent nurturing environments in institutions influence children's language development.

Methods

The present study investigated the early lexical development in Japanese children raised in institutional care (IC) (N = 86; 10–33 months; 37 boys) and compared their lexical skills to a large sample of age peers being raised in biological family care (BFC) (N = 1897; 937 boys) using vocabulary checklists.

Results

Our results present three main findings: (1) Japanese IC children did not exhibit a delay in productive vocabulary compared with BFC children, although their comprehensive vocabulary was delayed; (2) IC children who experienced maltreatment showed poorer vocabulary scores than non‐maltreated IC children; (3) both the duration at the institution and the number of books read to them significantly predicted children's vocabulary scores.

Conclusion

Our study suggests that the Japanese institutions included in the present study did not show a negative impact, at least on productive vocabulary, and may competently foster children's language development. We discussed the relationship between institutional environments and children's language development.

Behavioral and psychological symptoms of dementia (BPSD) that develop in patients with dementia not only impose a heavy burden on caregivers, but also affect the quality of life of the patients themselves. If BPSD can be predicted in advance and symptoms can be dealt with, the burden on caregivers can be reduced. In a preliminary experiment, we predicted BPSD using machine learning based on environmental and vital sensor data collected from multiple nursing homes. However, the Average Precision of the PR curve is still low. In this study, we analyzed data to improve the accuracy of BPSD prediction.The data analysis confirms that certain symptoms have a 24-hour cycle. The results show the possibility of predicting the onset of BPSD using machine learning methods.