A prediction model of community commitment and self-efficacy, using machine-learning: age-related differences in attitudes towards preventing social isolation among community-dwelling older adults

Abstract

Purpose: We used machine learning to identify age-related differences in people's attitude concerning their community commitment and the community's self-efficacy for preventing social isolation among community-dwelling older adults. Methods: Anonymous self-administered questionnaire surveys were conducted in 2013 (N=528), 2016(N=888) and 2018(N=810) to randomly selected local residents in a municipality, aged ≥20years. The surveys included questions on personal attributes, a community commitment scale, and a self-efficacy scale for preventing social isolation among older adults. In the first phase, several machine-learning algorithms were applied to the responses from the <60-year and ≥60-year age groups for the 2013 and 2016 data to identify potential differences between the two age categories. In the second phase, the best-performing algorithm was applied to the 2018 data to predict the respondent's age group based on his/her responses. Results: The first phase analysis indicated, for the community commitment scale, a classification accuracy of 0.60 for the best-performing algorithm and a kappa-value of 0.18. The accuracy and kappa-value for the self-efficacy scale were 0.62 and 0.24, respectively. Using the 2018 test data, the prediction accuracies for the ≥60-year age group were 0.61 and 0.65 for the community commitment scale and self-efficacy scale, respectively, and those for the <60-year age group were 0.60 and 0.60, respectively. Conclusions: The results indicated awareness differences concerning community commitment and self-efficacy for preventing social isolation between residents aged <60 and ≥60 years. The responses of the residents aged ≥60 years showed slightly higher prediction accuracies, likely indicating more homogeneous responses.departmental bulletin pape