Previous research has documented the role of different categories of psychosocial factors (i.e., sociodemographic factors, personality, subjective life circumstances, activity, physical health, and childhood circumstances) in predicting subjective well-being and quality of life among older adults. No previous study has simultaneously modeled a large number of these psychosocial factors using a well-powered sample and machine learning algorithms to predict quality of life, happiness, and life satisfaction among older adults. The aim of this paper was to investigate the correlates of quality of life, happiness, and life satisfaction among European adults older than 50 years using machine learning techniques.
Correlates of quality of life, happiness and life satisfaction among European adults older than 50 years: A machine-learning approach
Prati, Gabriele
2022
Abstract
Previous research has documented the role of different categories of psychosocial factors (i.e., sociodemographic factors, personality, subjective life circumstances, activity, physical health, and childhood circumstances) in predicting subjective well-being and quality of life among older adults. No previous study has simultaneously modeled a large number of these psychosocial factors using a well-powered sample and machine learning algorithms to predict quality of life, happiness, and life satisfaction among older adults. The aim of this paper was to investigate the correlates of quality of life, happiness, and life satisfaction among European adults older than 50 years using machine learning techniques.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
