Investigating Factors of Active Aging Among Chinese Older Adults: A Machine Learning Approach.
Background and Objectives With the extension of healthy life expectancy, promoting active aging has become a policy response to rapid population aging in China. Yet, it has been inconclusive about the relative importance of the determinants of active aging. By applying a machine learning approach, t...
| Publicado en: | Gerontologist Vol. 62; no. 3; pp. 332 - 342 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=156085750&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 156085750 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00169013 GET jtl: Gerontologist issn: 00169013 maglogo: N pubinfo: dt: Apr2022 vid: 62 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 156085750 10.1093/geront/gnab058 ppf: 332 ppct: 10 formats: tig: atl: Investigating Factors of Active Aging Among Chinese Older Adults: A Machine Learning Approach. aug: au: Yu, Jiao Huang, Wenxuan Kahana, Eva affil: Department of Sociology, Case Western Reserve University , Cleveland, Ohio , USA su: China Social participation Home environment Active aging Caregivers Work Rural conditions Health status indicators Metropolitan areas Residential patterns Middle age Old age Machine learning Random forest algorithms Job involvement Descriptive statistics Statistical models sug: subj: Social participation Home environment Active aging Caregivers Work Rural conditions Health status indicators Metropolitan areas Residential patterns Middle age Old age China Machine learning Random forest algorithms Job involvement Descriptive statistics Statistical models keyword: Chinese context LASSO regression Random Forest Chinese context LASSO regression Random Forest ab: Background and Objectives With the extension of healthy life expectancy, promoting active aging has become a policy response to rapid population aging in China. Yet, it has been inconclusive about the relative importance of the determinants of active aging. By applying a machine learning approach, this study aims to identify the most important determinants of active aging in 3 domains, i.e. paid/unpaid work, caregiving, and social activities, among Chinese older adults. Research Design and Methods Data were drawn from the first wave of the China Health and Retirement Longitudinal Study, which surveys a nationally representative sample of adults aged 60 years and older (N = 7,503). We estimated Random Forest and the least absolute shrinkage and selection operator regression models (LASSO) to determine the most important factors related to active aging. Results Health has a generic effect on all outcomes of active aging. Our findings also identified the domain-specific determinants of active aging. Urban/rural residency is among the most important factors determining the likelihood of engaging in paid/unpaid work. Living in a multigenerational household is especially important in predicting caregiving activities. Neighborhood infrastructure and facilities have the strongest influence on older adults' participation in social activities. Discussion and Implications The application of feature selection models provides a fruitful first step in identifying the most important determinants of active aging among Chinese older adults. These results provide evidence-based recommendations for policies and practices promoting active aging. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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