This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

While the association between migration and deteriorated refugee mental health is well-documented, existing research overwhelmingly centers on adult populations, leaving a discernible gap in our understanding of the factors influencing mental health for forcibly displaced children. This focus is particularly noteworthy considering the estimated 43.3 million children who are forcibly displaced globally. Little is known regarding the association between family processes, parental and child wellbeing for this population. This study addresses these gaps by examining the relationship between parental mental health and child mental health among refugees experiencing transmigration. We conducted in-person structured survey interviews with 120 parent-adolescent dyads living in the Trichy refugee camp in Tamil Nadu, India. Descriptive, multivariate analysis (hierarchical regression), and Machine Learning Algorithm (XGBOOST) were conducted to determine the best predictors and their importance for child depressive symptoms. The results confirm parental mental health and child behavioral and emotional factors are significant predictors of child depressive symptoms. While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. The study’s findings amplify the critical need for mental health resources for both parents and children, as well as parenting interventions inside refugee camps.

Sri Lankan Tamil refugees have endured over four decades of protracted displacement in southern India. This paper synthesizes findings from four studies conducted between 2005 and 2018 among refugees residing in the Gummidipoondi and Trichy refugee camps in the state of Tamil Nadu, India. Framed by a Community Based Participatory Research, in collaboration with the Organization for Eelam Refugee Rehabilitation (OfERR) and Community Advisory Board, these studies aimed to investigate the challenges related to migration stressors, mental health, family dynamics, and resource utilization of the Sri Lankan refugees living in India. The association of Psychological Distress and Migration Stress (PDMS) study examined migration stressors influencing psychological distress among refugees. The intergenerational Conflict and Community Readiness (ICCR study) assessed community readiness for repatriation and intergenerational conflict employing semi-structured qualitative interviews. The Family Dynamics (FD) mixed-method study investigated longitudinal associations between migration stressors, resource utilization, family dynamics, and health outcomes among Sri Lankan Tamil refugees. The Parenting Processes and Intervention Development (PPID) Study incorporated perspectives of community health workers through qualitative exploratory research. Integrating these findings, the research developed (1) Conceptual Model of Refugee Family Dynamics and (2) Framework for Refugee Health Intervention. These models provide a culturally sensitive framework for future interventions to improve family well-being among refugee populations.

This qualitative study explores perspectives of community health workers (CHWs) on family functioning among Sri Lankan Tamil refugees in India. Focus groups with 18 CHWs explored the impact of transmigration stress on family dynamics and the mitigating role of CHWs. Thematic analysis revealed three themes: parenting/family practices, services for addressing family conflicts and challenges, and solutions to enhance family well-being. Findings highlight the importance of culturally responsive interventions, increased access to counselling services and building trust. This study contributes to limited research on psychosocial experiences of refugees during transmigration and emphasises targeted support to promote family functioning and overall well-being.