Psychometric phenotypes of quality of life in community-dwelling older adults: A multivariate segmentation and Monte Carlo validation of the FRAG model.

Traditional biomedical paradigms frequently associate aging with an inevitable narrative of systemic decline, often neglecting the intrinsic capacity of older adults to optimize their quality of life (QoL) through psychological strengths. Grounded in Contextual Behavioral Science and the FRAG framework, this study aimed to identify heterogeneous QoL profiles in community-dwelling older adults and cross-validate their structural stability through computational simulation. A sample of Spanish community-dwelling older adults (N = 579, mean age = 69.37 years, 59.2% women) completed standardized instruments assessing QoL, gratitude, resilience, experiential acceptance, and psychological distress. A hierarchical cluster analysis using Ward's method was executed based on standardized psychometric scores. To evaluate parameter convergence and model stability against sampling noise, a single parametric Monte Carlo stochastic simulation (N = 1000) was parameterized using the empirical statistical moments and the Mersenne Twister algorithm. The cluster analysis identified a robust three-profile structure, distinguishing two vulnerability cohorts from a "functional excellence" phenotype. The Monte Carlo simulation confirmed excellent replication with parameter convergence metrics below 0.05. A subsequent univariate ANOVA revealed that continuous QoL scores differed significantly across the three empirical profiles, (2, 576) = F(2, 576) = 147.74, p < .001, η2p = .339. Experiential acceptance and gratitude emerged as the primary discriminant anchors of functional excellence, whereas baseline resilience exhibited negligible weight. These findings support the systematic integration of acceptance- and gratitude-based interventions within psychogeriatric community care to foster intrinsic capacity.