{"title":"Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.","authors":"Mohsen Joshanloo","doi":"10.1093/geronb/gbae204","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>A random forest machine learning algorithm was used. Data were drawn from the Midlife in the United States (MIDUS) survey.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/geronb/gbae204","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The Journal of Gerontology: Psychological Sciences publishes articles on development in adulthood and old age that advance the psychological science of aging processes and outcomes. Articles have clear implications for theoretical or methodological innovation in the psychology of aging or contribute significantly to the empirical understanding of psychological processes and aging. Areas of interest include, but are not limited to, attitudes, clinical applications, cognition, education, emotion, health, human factors, interpersonal relations, neuropsychology, perception, personality, physiological psychology, social psychology, and sensation.