We All Need Somebody to Lean on: Social Support as a Protective Factor for Individuals with Childhood Adversity

Within the past two decades, research on adverse childhood experiences (ACEs) spurred by the seminal Felitti (1998) study has gained significant momentum. Research has shown that childhood adversity impacts development across the lifespan, and it has been linked to heightened risk for both physical and mental health difficulties. Depression symptoms is one such outcome that has been associated with ACE exposure. In examining the pathways through which ACEs impact later development, the literature indicates emotion regulation may be a potential mediator between ACEs and depression outcomes. While understanding etiology of depression and risk factors that contribute to symptomology is important, it is also important to investigate factors which may buffer against depression and build resilience. Social support may be a protective environmental factor that could mitigate heightened risk within populations of individuals with ACE exposure. The primary aim of this study is to investigate the role of social support as a protective factor against depression in those who have experienced ACEs nested within the model where emotion regulation acts as a mediator between ACE exposure and later depressive symptoms. In this study, undergraduate participants (N = 766) at a southeastern university completed self-report questionnaires which evaluated ACEs, emotion regulation difficulties, perceived social support, and depressive symptoms. In the current study, it is hypothesized that difficulties in emotion regulation will mediate the link between ACEs and later depressive symptoms (model 1), social support will act as a protective factor against depression in the pathway between difficulties in emotion regulation and depression (model 2), and social support will have a greater buffering effect in individuals who have greater severity of ACE exposure (model 3). Mediation (model 1) and moderated mediation (model 2) analyses will be conducted using Hayes PROCESS macro. For all PROCESS models, bootstrapping frequencies will be set at 5,000 and used to generate a 95% confidence interval. The PROCESS bootstrapping methods entail a statistical process of extracting, resampling, and replacement of cases within a dataset. Additional follow-up moderated moderation analyses (model 3) will be conducted using Hayes PROCESS macro if the moderated mediation model is found to be significant