College Adaptation and Perceived Social Support: a Cross-Sectional Correlational Study

• Grant number: 24/04614-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2024
• End date: October 31, 2025
• Field of knowledge: Humanities - Psychology - Human Development Psychology
• Principal Investigator: Fernanda Aguillera
• Grantee: Guilherme Villa Barichello
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

With the new public policies for access and retention, higher education has been democratizing, and a new profile of students is emerging in institutions. In this context, various challenges are growing in complexity, ranging from financial assistance to psychosocial retention, at a critical phase of life (emerging adulthood) where there is pressure to define a professional project. Student support services still offer incipient actions related to university adaptation, such as psychological support and pedagogical guidance. Reflections of this can be observed in the lack of adaptation, dropout rates, and university retention, which seems to be more pronounced among groups with low levels of perceived social support. In light of this, this study aims to verify the possible relationships between dimensions of perceived social support, mental health, and different aspects of adaptation to the university context, with a view to possible contributions towards student support and retention. For this purpose, a cross-sectional correlational method will be implemented through an online survey. The instruments will include a sociodemographic questionnaire, the Questionário de Adaptação ao Ensino Superior, the brazilian version of the Multidimensional Scale of Perceived Social Support, and the DASS-21. Four hundred students over 18 years old, enrolled in STEM (science, technology, engineering, and mathematics) courses at public universities in São Paulo, will participate. Data will be analyzed using various statistical techniques: descriptive, normality tests, t-Student and ANOVA (to verify differences between groups with high and low dropout rates), Pearson, Spearman, and point-biserial correlations, as well as linear regressions. It is hoped to contribute to the elucidation of factors that favor university adaptation, useful for proposals and improvements in student support programs.