Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.

This study investigates predictors of generativity—defined as concern for guiding the next generation—in older adults using data from the Midlife in the United States (MIDUS) survey and a random forest machine learning approach. Analyzing over 60 variables across personality, social, health, and socioeconomic domains, the strongest predictors identified were social potency (assertiveness and leadership), openness to experience, social integration, personal growth, and achievement orientation, while many demographic and health-related factors showed limited predictive power. The findings suggest that generativity is more closely linked to eudaimonic well-being (purpose, personal growth) and personality plasticity (exploration and adaptability) than to hedonic well-being or stability traits, framing generativity as a dynamic, growth-oriented process. These results have implications for interventions aimed at fostering generativity through enhancing social skills, personal development, and a sense of purpose, though the cross-sectional design limits causal conclusions.