Research Publications
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Publication Open Access Effects of Childhood Physical Abuse and Social Support on Young Adult Self- Esteem(School of Psychology. Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Nayanathara, V; Selvaratnam, N. DChildhood physical abuse has been empirically linked to reduced self-esteem, reflecting its lasting impact on psychological well-being. Although social support is often considered a protective factor, research on its role in shaping self-esteem among those who have experienced childhood abuse remains inconclusive, especially among Asian populations. This study aimed to investigate the impact of childhood physicalabuse and social support on self-esteem among young adults residing in rural and suburban areas of Sri Lanka. The sample consisted of 351 Sinhala-speaking individuals (53.56% female and 46.44% male), aged between 18 and 29 years, drawn from 11 rural and suburban districts in Sri Lanka using cluster sampling.Descriptive and inferential statistics, including two-way ANOVA and a post-hoc analysis, were conducted in SPSS and Jamovi to examine main and interaction effects of childhood physical abuse and social support on self-esteem. There were significant main effects of both childhood physical abuse and social support on self-esteem. However, the interaction effect between physical abuse and social support was not significant.Post-hoc analyses indicated that moderate levels of social support mitigated some of the adverse effects of severe childhood physical abuse on self-esteem. The absence of a significant interaction suggests independent effects, warranting further investigation into related psychological outcomes for relevant authorities to make informed decisions governing child protection.Publication Embargo 'xīnl' The Social Media App to Replenish Mental Health with the Aid of an Egocentric Network(Institute of Electrical and Electronics Engineers, 2022-11-03) Kasthurirathna, D; Kalansooriya, S; Kaluarachchi, A; Weerawickrama, C; Nanayakkara, D; Adeepa, DThe impact of social groups on one's emotional health is a crucial issue that must be addressed correctly. Emotions and social groups play significant roles in human mental and physical activities. It is difficult to detect and maintain track of changing emotional states. The main goal of this study is to build a social media app called Xinli, that proposes an aggregated method to predict emotions using a multimodal approach and to predict personalized activities based on the user's mental state, and to further track the improvement of emotional state with the impact of recommended activities and social support groups. The results suggest that the aggregated modalities method is more accurate in recognizing emotions, and activity prediction using reinforcement learning is a clean way to personalize activities based on the emotional state from user to user, which is the novelty of the proposed study.