Predicting outcomes of children in residential treatment: A comparison of a decision support algorithm and a multidisciplinary team decision model

Ka Ho Brian Chor, Gary M. McClelland, Dana A. Weiner, Neil Jordan, John S. Lyons

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Residential treatment is the most intensive and costly component of all child welfare systems per episode of care. At the same time, decisions to place in residential treatment centers are prioritized by the practice of least restrictive setting and best interest for children. There are, however, no standard evidence-based criteria for placing children in residential treatment. Clinical judgment, staffing dynamics, and other system factors are part of the decision-making process. Thus, some residential placements may be unnecessary and may be even harmful. The present study compares two models of decision-making, a multidisciplinary team approach and an objective decision support algorithm, and assesses outcomes when the two models either concur or not. Concordant decisions predicted greater clinical improvement than discordant decisions, but no differences were found in length of stay in placement. Policy implications for the decision-making process in child welfare are discussed.

Original languageEnglish
Pages (from-to)2345-2352
Number of pages8
JournalChildren and Youth Services Review
Volume34
Issue number12
DOIs
StatePublished - 2012

Bibliographical note

Funding Information:
This study was funded by the Illinois Department of Children and Family Services (IDCFS) . The authors would like to give special thanks to Brice Bloom-Ellis and the Child and Youth Investment Teams (CAYIT), especially Teddy Savas and Lee Annes for their invaluable insight and input.

Keywords

  • Child welfare
  • Decision support algorithm
  • Multidisciplinary decision-making
  • Out-of-home placements
  • Residential treatment

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Sociology and Political Science

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