Investigating the Role of Contextual Factors in Predicting the Likelihood of Foster Care Placement Disruption

Introduction: Ensuring the stability of foster care placements is important for promoting the safety, health, and well being of maltreated children. Research suggests that child behavior problems (Chamberlain, et. al., 2006; Fisher, et. al., 2011) and foster family influences such as parental negativity (Cheung, et. al., 2011) are predictive of placement disruption. Sibling foster care placements have been shown to impact home integration (Leathers, 2005), and kinship placements have been shown to improve behavioral well-being (Rubin, 2008). Despite what is known about the differential impacts of child, caregiver, and home setting characteristics on placement stability, these distinct constructs have not been tested simultaneously to ascertain their influence on placement stability. Informed by Bioecological and Social Learning theoretical frameworks, this study investigates the role of child integration,  Foster Parent Report of Impact of Negative Behavior (FPRNB) , and foster setting characteristics on the likelihood of placement change over a 6 month period of time.

 

Methods: The current study examined stability for a sample of older siblings in foster care who were participating in a relationship development-focused RCT (N=66 youth, age x=13 SE=.14). Logistic regression models were employed to assess the odds of placement disruption in relation to child integration (alpha=.894), CBCL externalizing T-scores, and foster care setting characteristics. Placement disruption was measured at 6-month follow-up using a binary variable (no transition/one or more transitions). Length of time in care at baseline was measured in months (X=23.92, SD=32.89). Foster care setting characteristics included placement type (kinship/non-relative) and sibling-placement status (together/apart). Diagnostic procedures identified low-to-tolerable levels of colinearity between predictor variables. 

Findings: The first model was first run without the length of time in foster home variable. This model trended toward significance X² (df=4, 8.601, p=.072); however the full model containing number of months in foster home was statistically significant, X² (df=5, 25.967, p<.000). Although the final model correctly classified 76.1% of the cases, the only statistically significant predictor of placement disruption was length of time in foster home (OR=.917), suggesting that for every month in the foster home prior to baseline measurement respondents were 9% less likely to experience a disruption over the following 6 months. Kinship home, sibling based placement, and child behavior problems did not account for unique outcome variance for placement change.

Conclusions: While this study was able to account for placement transition relating to length of time in the home,  reasons for each transition remain unknown and may have implications for the model. Ensuring stable and developmentally-appropriate foster homes is essential for child well-being and for prevention programming with foster youth, and prevention scientists should consider cognitive, relational, and behavioral targets of intervention across child, sibling, and caregivers.