KMAN Publication Inc. (KMANPUB) Journal of Adolescent and Youth Psychological Studies (JAYPS) 2981-2526 7 Serial Number 43 2026 03 10 The Impact of Childhood Trauma on Adolescent Resilience: An Explainable Machine Learning Analysis of Protective Factors in Low-Socioeconomic Contexts The Impact of Childhood Trauma on Adolescent Resilience: An Explainable Machine Learning Analysis of Protective Factors in Low-Socioeconomic Contexts 1 10 10.61838/kman.jayps.2863 EN Journal Article 2025 11 28 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> This study aims to utilize an explainable machine learning framework to identify and rank the critical protective factors that buffer against the deleterious effects of specific childhood trauma subtypes on adolescent resilience within low-socioeconomic Iraqi contexts.</p> <p><strong>Methods and Materials:</strong> A cross-sectional design was employed with a sample of adolescents (mean age years; female) from low-socioeconomic districts in Iraq. Data were collected using culturally adapted and validated instruments, including the Childhood Trauma Questionnaire-Short Form (CTQ-SF), the Connor-Davidson Resilience Scale (CD-RISC), and the Resilience Scale for Adolescents (READ). To analyze the complex, non-linear relationships between variables, an eXtreme Gradient Boosting (XGBoost) machine learning model was developed. The model’s interpretability was enhanced using SHapley Additive exPlanations (SHAP) to calculate the global predictive importance and interaction effects of specific risk and protective factors.</p> <p><strong>Findings:</strong> The XGBoost model demonstrated robust predictive performance, explaining a significant portion of the variance in adolescent resilience ( , ). SHAP value analysis revealed that Family Cohesion emerged as the paramount protective factor (SHAP ), while Emotional Abuse (SHAP ) and Emotional Neglect (SHAP ) were identified as the most detrimental risk factors, surpassing physical and sexual abuse in predictive weight. Furthermore, a critical non-linear threshold effect was discovered: the protective utility of peer support increased significantly only when emotional neglect scores were below a specific clinical threshold (CTQ-SF ).</p> <p><strong>Conclusion:</strong> Explainable machine learning provides granular insights into trauma recovery, highlighting that emotional maltreatment profoundly damages adolescent adaptation. Interventions in low-resource settings must prioritize systemic family-based therapies to foster cohesion and adopt a phased clinical approach that addresses foundational emotional neglect before introducing peer-based support systems.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/jayps/article/download/2863/9344