Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.
Objectives This study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across multiple domains, including personality, daily functioning, socioeconomic factors, health status, and mental well-being. Methods A random forest machine le...
| Publicado en: | Journals of Gerontology Series B: Psychological Sciences & Social Sciences Vol. 80; no. 4; pp. 1 - 11 |
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| Formato: | Artículo |
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Oxford University Press / USA
Apr2025
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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=184297330&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184297330 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10795014 JGB jtl: Journals of Gerontology Series B: Psychological Sciences & Social Sciences issn: 10795014 maglogo: N pubinfo: dt: Apr2025 vid: 80 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184297330 10.1093/geronb/gbae204 ppf: 1 ppct: 10 formats: tig: atl: Key Predictors of Generativity in Adulthood: A Machine Learning Analysis. aug: au: Joshanloo, Mohsen affil: Department of Psychology, Keimyung University, Daegu, South Korea su: Psychological aspects of aging Health status indicators Income Satisfaction Socioeconomic factors Social integration Personality Activities of daily living Well-being Intergenerational relations Achievement Random forest algorithms Research Machine learning Individual development Algorithms sug: subj: Psychological aspects of aging Health status indicators Income Satisfaction Socioeconomic factors Social integration Personality Activities of daily living Well-being Intergenerational relations Achievement Random forest algorithms Research Machine learning Individual development Algorithms keyword: MIDUS Random forests Successful aging MIDUS Random forests Successful aging ab: Objectives This study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across multiple domains, including personality, daily functioning, socioeconomic factors, health status, and mental well-being. Methods A random forest machine learning algorithm was used. Data were drawn from the Midlife in the United States (MIDUS) survey. Results Social potency, openness, social integration, personal growth, and achievement orientation were the strongest predictors of generativity. Notably, many demographic (e.g. income) and health-related variables (e.g. chronic health conditions) were found to be much less predictive. Discussion This study provides new data-driven insights into the nature of generativity. The findings suggest that generativity is more closely associated with eudaimonic and plasticity-related variables (e.g. personal growth and social potency) rather than hedonic and homeostasis-oriented ones (e.g. life satisfaction and emotional stability). This indicates that generativity is an inherently dynamic construct, driven by a desire for exploration, social contribution, and personal growth. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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