Patterns of out-of-home placement decision-making in child welfare

Out-of-Home Placement Decision-Making and Outcomes in Child Welfare: A Longitudinal Study

“…Table 3 presents the rates and frequencies of concordant and discordant placement decisions in our sample within each placement type recommended by the CANS Algorithm (Chor et al 2013). Concordance (i.e., the on-diagonal cells) was similar and relatively high in specialized foster care (64.1 %), foster care (59.3 %), and residential treatment (53.5 %), but much lower in group home (13.1 %).…”

“…Given a priori guidance on operationalizing concordance and discordance (Chor et al 2013; Magura et al 2003), state child welfare systems that invest in multiple decision-making models to inform best placement practice should consider comparing these models to examine existing patterns of decisions, and how these patterns might impact the children getting placed. This effort can further states’ recommendations to address children’s placement stability by using specialized placement units, specialized caseworkers, and assessment tools (Blakey et al 2012).…”

“…Significant challenges include inconsistent placement criteria and a greater emphasis on safe removal of children from homes than on subsequent stability in out-of-home placements (Chor 2013; Chor et al 2013). Decision-making ecology frameworks further highlight uncertainty and multi-level factors (e.g., caseworker, organization, policy) that influence child welfare decision-making (Baumann et al.…”

“…To address the first question, one study compared two decision-making models in the Illinois child welfare system: the multidisciplinary Child and Youth Investment Teams (CAYIT), and the Child and Adolescent Needs and Strengths (CANS) Algorithm, a need-based, decision support algorithm that recommends the best placement using a child’s clinical assessment (Chor et al 2013). In 2005–2013, Illinois Department of Children and Family Services (IDCFS) mandated that the CAYIT had the ultimate decision-making authority on the actual placement of a child and the teams were provided with a placement recommendation by the CANS Algorithm.…”

“…These findings indicate that the two models work in concert to identify and place children in the least and the most restrictive settings. They also guide the state to monitor and review cases when there is discordance, which may reveal a child’s unique placement needs (Chor et al 2013). …”