A Study Exploring Relationship between Risk Factors and Resilience among Youth

Abstract

ABSTRACT Background: “Youth” is best understood as a period of transition from the dependence of childhood to adulthood’s independence. Of the 1.2 billion people live in India, 54% are <24 years of age. These young people are the futures of tomorrow. However, their lives today are ridden with anxieties, depressions, identity crisis, relationship break downs, conflicts, stress, and sensuality. Youth live in a society serene of a number of social spheres that influence their belief, attitudes, and behavior where the effect of risk behavior among youth can be seen in them being reckless. Youth lifestyles are of concern. Determining the risk behaviors in this age group is important for understanding how hazardous behavior shape their adult outcomes and identify resilience that will help them make better decisions and enhance the existing protective factors. Aim: This study aims to assess the relationship between risk behaviors and resilience among youths in the city of Coimbatore. Setting and Design: A cross-sectional study was conducted on youth from schools and colleges, selected by random sampling, in Coimbatore city of Tamil Nadu. Methodology: One thousand seven hundred and ten youth studying in various schools and colleges (F = 799 and M = 911) aged between 18 and 22 years were selected randomly for this study. Youth Risk Behavior Scale and Resilience Scale were administered to elicit the information from the selected youth. Statistical Analysis Used: The frequency and percentage were calculated to assess the level of risk behavior and resilience among youth. Canonical correlation analysis was applied to identify the significant relationship of risk with the resilient factors among youth. The data were subjected to the statistical analysis through SPSS 19. Results: The results highlighted that smoking, tobacco, alcohol, and self-medication risks were higher and all the aspects of resilience except for adaptability were found to be lower among youth. The “canonical correlation analysis” resulted in four canonical functions and where collectively the full model was statistically significant f (40, 7399) = 7.137, P = 0.000. These results emphasized the differential impact of risk on resilience among youth and in their functioning. Conclusions: The study emphasizes the differential impact of risk and resilience on the different aspects of youth functioning and supports the need for specificity in resilience research. Resilience can be practiced and youth may be educated to be resilient. Attention to risk factors at both individual and social levels is required to provide the fullest understanding of healthy youth and improve their well-being with enhanced resilience.