| Sumario: | 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.
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