Robust protective factors that help youths achieve positive adjustment

Research has shown that children of a parent with depression are high-risk of developing multiple maladjustments because these families tend to face multiple psychosocial risks (Garber, 2005; Hammen & Brenna, 2001).  However, few studies have examined ecological protective mechanisms on this population while controlling for the potential co-occurring risks along with parental depression (Rutter, 2006).  Research also has devoted less attention to protective factors with a broad range of protection across developmental domains (called robust protective factors, in this study).  Based on a resilience framework, this study identifies robust protective factors for minority children who have a primary caregiver with a diagnosis of major depression, and have been exposed to financial and community stressors.

This study examines 126 families where the primary caregiver had a diagnosis of major depression.  The data comes from a longitudinal data set: Family and Community Health Study (FACHS).  By using a series of random sampling strategies from state to local community level, FACHS recruited 898 African American families in poor and rural areas of Georgia and Iowa.  The study includes first and second wave data, in which children were ages 10-14.  Three types of theoretical protective factors in individual-family-community levels and four children's developmental outcomes were selected from the FACHS, including depression (MD), conduct behavior (CD), school performance (SP), and education aspiration (EA), using instruments with sound validity and reliability.  Factor analysis and internal consistency were conducted for these 126 families.  Cronbach's alpha of these instruments ranged from .60 to .81.

Separate hierarchical regression analyses were conducted for each of children's developmental outcomes.  Risk factors and developmental outcome at wave 1 data were entered on step 1, and then all theoretical protective factors at wave 2 were entered simultaneously on step 2.  Before conducting the regression analyses, power analysis, data screening and regression assumptions assessment were conducted.  One to four outliers were identified and removed from these models.  These four models were statistically significant.  The model (R²_adj) explained 11.5 % of variance of MD, 29.8 % of CD, 15.2 % of SP, and 18.7 % of EA.  Children's optimism (ß=-.215) significantly contributed to the MD model.  Children's self control (ß=-.210), prosocial friendship (ß=-.187), and parental monitoring (ß=-.250) significantly contributed to the CD model. Parental monitoring (ß=.189) significantly contributed to the SP model.  Parental monitoring (ß=.278) and teacher's support (ß=.292) significantly contributed to the EA model.

This study suggests that overall, with the exception of parental monitoring, these theoretical protective factors only operated in specific developmental domains.  Only parental monitoring was identified as a robust protective factor for this population.  When the focus of an intervention is to create a broad protection for these children's behavioral and academic-related outcomes, the improvement of parental monitoring skills could be essential.  A first step in getting there could be to address the stress of these parents.  Two directions for future research are to explore how parental monitoring and other protective factors operate in these children's late adolescence to young adulthood, and to exam the interaction between risk and protection.