ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Computational Psychiatry
This article is part of the Research TopicAdvancing Biostatistics and Informatics Applications in Mental Health ResearchView all 4 articles
Latent Profiling of Five-Dimensional Psychological Resilience Across Generations: A Deep Clustering and Behavioral Divergence Analysis in Pre-Conflict Iran
Provisionally accepted
- Istanbul University, Istanbul, Türkiye
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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 reflected nonlinear configurations of resilience capacities across generational and gender groups. However, because the research employed a cross-sectional and self-report design, findings illustrate associative rather than causal relationships, and representativeness is limited to online participants. These contextual and demographic influences suggest that resilience is embedded within Iran's evolving social environment. Despite these limitations, the study demonstrates the potential of AI-based latent profiling to clarify the multidimensional nature of resilience within culturally demanding contexts.
Keywords: Behavioral drift, Deep clustering, generational analysis, Internal tension, Iranian society, Machine Learning in Mental Health, psychological resilience, Resilience archetypes
Received: 20 Jul 2025; Accepted: 05 Dec 2025.
Copyright: © 2025 Shakouri Youvalari and Zaim Gökbay. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Inci Zaim Gökbay
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.