Caseload factors predictive of family abuse and neglect treatment outcomes

In child welfare, caseloads are frequently far higher than optimal. Not all cases are created equal; however, little is known about which combination and interaction of factors make caseloads more challenging and impact child and family outcomes.

This study aims to identify which case, provider, and organizational factors most strongly differentiate between families with favorable and less-than-positive treatment outcomes.

Participants were 25 family advocacy program providers and 17 supervisors at 11 Department of the Air Force installations.

Following informed consent, participants completed demographic and caseload questionnaires, and we collected information about organizational factors. Providers were sent a weekly case update and burnout questionnaire for seven months. We used linear mixed-effects model tree (LMM tree) algorithms to determine the provider, client, and organizational characteristics that best distinguish between favorable vs. unfavorable outcomes.

The LMM tree predicting provider-rated treatment success yielded three significant partitioning variables: (a) commander involvement, (b) case complexity, and (c) % of clients in a high-risk field. The LMM predicting client-rated treatment progress yielded seven significant partitioning variables: (a) command involvement; (b) ease of reaching tenant unit command; (c) # of high-risk cases; (d) % of clients receiving Alcohol and Drug Abuse Prevention and Treatment services; (e) ease of reaching command; (f) % of clients with legal involvement; (g) provider age.

This study is a first step toward developing a dynamic caseload management tool. An intelligent, algorithm-informed approach to case assignment could help child welfare agencies operate in their typically resource-scarce contexts in a manner that improves outcomes.