Latent profiling of five-dimensional psychological resilience across generations: a deep clustering and behavioural divergence analysis in pre-conflict Iran
Taghi Shakouri Youvalari, Inci Zaim Gökbay
TL;DR
This study explores psychological resilience in pre-conflict Iran using AI to identify distinct resilience profiles across generations.
Contribution
The paper introduces a novel AI-based method for profiling multidimensional psychological resilience in a culturally specific context.
Findings
Four distinct psychological resilience archetypes were identified using deep clustering techniques.
Resilience profiles varied across generational and gender groups in pre-conflict Iran.
AI-based latent profiling reveals nonlinear configurations of resilience dimensions.
Abstract
Psychological resilience is increasingly conceptualized as a multidimensional construct encompassing identity, emotional, cognitive, behavioural, and social domains. Using data from 620 Iranian adults (aged 18–64 years; 52% female), collected through an online self-report survey, this study applied unsupervised machine-learning techniques combining a deep autoencoder for dimensionality reduction with a Gaussian Mixture Model (GMM) for latent clustering-to examine psychological resilience profiles in pre-conflict Iran. Thirty-seven standardized psychological subscales were aggregated into five theoretically grounded dimensions: Self-Identity and Meaning, Emotional Regulation, Cognitive Flexibility, Coping and Growth, and Social Support and Connectedness. Unsupervised analysis identified four latent archetypes-Fragile Striver, Reactive Idealist, Hidden Reactor, and Stable Withdrawer-that…
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Taxonomy
TopicsResilience and Mental Health · Migration, Health and Trauma · Posttraumatic Stress Disorder Research