Association for Public Policy Analysis & Management

This paper will discuss First 5 LA’s 5-year countywide implementation of the Parent Child Interaction Therapy (PCIT) program.  Proponents of PCIT point to it as a high-quality parenting program with the potential to positively influence child behavior, reduce recidivism into the child welfare system, and increase caregiver’s confidence and self-esteem (Dombrowski, Timmer, Blacker, & Urquiza, 2005; Timmer, Urquiza, & Zebell, 2006, 2009).  First 5 LA was created in 1998 and is currently one of the leading early childhood advocate organizations working in Los Angeles County.  The $20 million countywide implementation of PCIT began in 2012 and has been a collaborative effort between First 5 LA, the Los Angeles Department of Mental Health (DMH), the Los Angeles Department of Children and Family Services (DCFS), and the University of California, Davis (UC Davis).  The authors of this paper were contracted to conduct an evaluation of the implementation of the countywide PCIT program and preliminary outcomes were analyzed after the first round of data collection in early 2015.  During this round of data collection, 10 major stakeholders from the 4 major implementing organizations and other early childhood advocates participated in semi-structured interviews.

After a brief review of the literature, this paper will discuss this case to examine examples of how research evidence and data analysis influenced the policymaking and implementation process.  First, the paper will present examples of how research evidence influenced the perceptions and goals of the major stakeholders involved with the policy.  Second, it will discuss examples of how data analysis and research were used to make adaptations during the early stages of the program’s implementation throughout the county.  Third, the paper will discuss bureaucratic challenges and constraints with the collaborative use of research and data.  Finally, the paper will conclude with some recommendations on how research and data can effectively be used to influence policy implementation in large-scale implementations involving collaborative efforts among multiple organizations.