Poster Introductions II--Mentor Self-Efficacy as a Hypothesized Mediator between Environmental Factors and the Quality of Big Brothers Big Sisters Community Match Relationships in Canada

Background: Empirical evidence suggests that environmental factors (parent support, caseworker support, mentor training) and mentor self-efficacy are positively associated with match relationship quality (MRQ) among children and adult mentors enrolled in Big Brothers Big Sisters (BBBS) programs. Further evidence points to positive associations between environment and self-efficacy. However, the extent to which mentor self-efficacy mediates the relationship between environmental supports and MRQ remains unclear. Hypotheses: (1) Environmental supports (caseworker support, parent support, and mentor training) will each be positively associated with MRQ; (2) The positive relationships between environmental supports and MRQ will be mediated by mentor self-efficacy. The plausibility of the mediation model will be explored across categories of children’s age and gender and match gender composition. Study Design: A cross-sectional study involving 300 families and mentors will be conducted using the 12-month follow up data from an on-going longitudinal investigation of BBBS community match relationships across Canada. Methods: Data are collected via in-person interviews for children and self-administered questionnaires for mentors and parents. Structural equation modeling will be used to address the study objectives including mediation and moderation analyses. Results: Preliminary results to be presented. Annalise J. Ferro is a PhD Candidate in the Department of Epidemiology and Biostatistics at the University of Western Ontario. Her doctoral research investigates the association between environmental factors (i.e., parent support, caseworker support, mentor training, and mentor self-efficacy) and match relationship quality among children and adult mentors in Big Brothers Big Sisters community match relationships. Annalise is a current recipient of a Doctoral Fellowship from the Social Sciences and Humanities Research Council. Her research interests include child and family health, health services, and structural equation modeling. Prior to commencing graduate studies, she obtained a BScN from the University of Victoria and worked as a paediatric critical care nurse at British Columbia Children’s Hospital. David De Wit, Ph.D., is Senior Research Scientist with the Centre for Addiction and Mental Health located in London, Ontario and an Associate Professor in the Department of Epidemiology and Biostatistics at the University of Western Ontario. He is also an Associate Scientist with the Lawson Health Research Institute (also in London). His primary research interests include the evaluation of community interventions for vulnerable populations, environmental influences on child health and well-being, and risk and protective factors for child and youth mental health problems and substance addiction. He has led or co-led a number of grant-funded projects including a five year study funded by the National Institutes of Health to evaluate the effectiveness of a family skills-based intervention for children of parents struggling with alcohol problems. He recently received funding from the Canadian Institutes of Health Research (De Wit, P.I., $1.7 million) to study the impact of Big Brother Big Sister community match relationships across Canada on the health and well-being of children in need of an adult mentor. Throughout his career he has published in several scientific journals and delivered numerous presentations to the scientific and lay communities. He recently completed a five year term on the Editorial Board for the Journal of Marriage and the Family and currently serves as a grant reviewer for the Child Health Committee, Canadian Institutes of Health Research. He has given a number of guest lectures to students in both undergraduate and graduate level courses primarily dealing with statistical techniques for handling missing values and techniques for analyzing longitudinal data. He currently supervises one Masters level student and one Ph.D. student