KMAN Publication Inc. (KMANPUB) Journal of Adolescent and Youth Psychological Studies (JAYPS) 2981-2526 7 Serial Number 45 2026 05 01 Identifying Profiles of Adolescent Flourishing Through Unsupervised Machine Learning and Variables Including Self-Compassion, Meaning in Life, Digital Wellbeing, Gratitude, and Peer Support Identifying Profiles of Adolescent Flourishing Through Unsupervised Machine Learning and Variables Including Self-Compassion, Meaning in Life, Digital Wellbeing, Gratitude, and Peer Support 1 13 10.61838/kman.jayps.5437 EN Journal Article 2025 09 12 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The present study aimed to identify distinct profiles of adolescent flourishing using unsupervised machine learning techniques based on self-compassion, meaning in life, digital wellbeing, gratitude, and peer support among Canadian adolescents.</p> <p><strong>Methods and Materials: </strong>This study employed a cross-sectional correlational design with an unsupervised machine learning approach. The statistical population consisted of Canadian high school students enrolled in public secondary schools during the 2025–2026 academic year. Using multistage cluster sampling, 742 adolescents were recruited, and after data screening, 711 participants were retained for final analysis. Data were collected using standardized instruments including the Flourishing Scale, Self-Compassion Scale–Short Form, Meaning in Life Questionnaire, Digital Wellbeing Scale for Adolescents, Gratitude Questionnaire–Six Item Form, and the peer support subscale of the Multidimensional Scale of Perceived Social Support. Data analysis was conducted using Python and SPSS-28. Hierarchical clustering and K-means clustering were applied to identify latent flourishing profiles. Cluster validity was evaluated using silhouette coefficients, Davies–Bouldin index, and Calinski–Harabasz index. Multivariate analysis of variance and Bonferroni post hoc tests were used to compare identified profiles across study variables.</p> <p><strong>Findings: </strong>The findings indicated that the three-cluster solution demonstrated the strongest statistical performance and conceptual interpretability compared to alternative clustering solutions. Three distinct profiles were identified, including the High Flourishing Adaptive Profile, Moderate Flourishing Vulnerable Profile, and Low Flourishing At-Risk Profile. Significant multivariate differences were observed among the profiles across flourishing, self-compassion, meaning in life, digital wellbeing, gratitude, and peer support (p &lt; .001). Adolescents within the High Flourishing Adaptive Profile reported significantly higher scores across all psychological and relational variables compared to the other profiles, whereas the Low Flourishing At-Risk Profile demonstrated consistently lower functioning across all domains. Meaning in life and flourishing produced the largest effect sizes in distinguishing profile membership. Visualization analyses using t-distributed stochastic neighbor embedding additionally confirmed clear separation and structural distinctiveness among the identified flourishing profiles.</p> <p><strong>Conclusion: </strong>The findings suggest that adolescent flourishing is a multidimensional and heterogeneous construct shaped by emotional, existential, relational, and digital wellbeing factors. Self-compassion, meaning in life, gratitude, peer support, and healthy digital engagement appear to function as major protective resources associated with adaptive flourishing. The application of unsupervised machine learning techniques provided a nuanced person-centered understanding of adolescent wellbeing and highlighted the existence of distinct flourishing trajectories among youth populations. These findings support the development of strengths-based and personalized interventions aimed at enhancing flourishing and psychological resilience during adolescence.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/jayps/article/download/5437/9867