Category: Process, Quality, and Capacity Investments

Guest Blogger Susan Dreyfus, President and CEO of the Alliance for Strong Families and Communities, reflects on the importance of the finding that the more agencies rely on research evidence the better outcomes they achieve for children in care.... Read more >

This new research brief illustrates how reliance on group care placement differs throughout the country and how indiviudal and county characteristics interact to influence a child's likelihood of being placed in a group setting.... Read more >

Today, public child welfare agencies are taking stock of their capacity for Continuous Quality Improvement (CQI) and considering the investments they will make in order to build that capacity. How these CQI systems develop will vary from agency to agency depending on administrative structure, staffing patterns, available resources, and a host of other factors. They will all, however, be responsible for facilitating the same basic CQI process—a cycle of problem solving activities that requires the deliberate use of evidence. Given that shared responsibility, the child welfare field will benefit from a common vocabulary for describing what CQI is, the core principles on which CQI rests, and the critical role that evidence plays throughout the CQI process. In keeping with a century-long tradition of CQI that has guided improvement efforts in other fields, we put forth a common language for child welfare CQI in a new publication, Principles, Language, and Shared… Read more >

Care days is a strategic quantity that lies at the core of planning for, implementing, and monitoring foster care interventions, especially in the context of a Title IV-E waiver. This post summarizes Fred Wulczyn’s January 24, 2014 webinar “Care Days, Waivers, CQI, and Intervention Design.” ... Read more >

No matter the specific issue to which it is applied, improving outcomes for children requires agencies to work through the four basic phases of the CQI cycle: Plan, Do, Study, Act. In this post, we describe the cycle of CQI in a child welfare context and highlight the major activities that take place during each phase of the process.... Read more >

This is the last in a 4-part series that shows how to use the web tool to hone in on the needs of infants in foster care. In the last three posts, we used different parts of the web tool to learn how many infants are in our system in the first place, how long they spend in foster care, and what is unique about their placement and exit experience. When we left off, we had just learned that, among other things, infants in our sample county are more likely than older children to be placed in non-relative foster care, to exit to adoption, and when they reunify, to reunify within 90 days. What can we learn about what goes on behind the scenes in these cases that can give us more information about what might expedite permanency for infants in foster care? In this Recipe, I’ll show you how… Read more >

In Part 1 of this series of Recipes, we learned about a county where infants represent the largest proportion of children entering foster care. In Part 2, we found out that those infants stayed in care longer than children who entered care at older ages. In this Recipe, we’ll use another function of the web tool to learn more about who these infants are and what they experience while in care, and use what we learn to inform our decision making about the type of intervention they might need. This Recipe will take about 10 minutes to complete. Question: What are the demographic and case-related characteristics of the infants in my system? In what ways are the children in my system who enter as infants different from the children who enter care at older ages? Follow the steps in the previous Recipe. On the results page, scroll to the top and… Read more >

The best data analyses start with well-formed analytic questions. Therefore, before you set out to study your system’s administrative data—whether you are doing so with the Data Center’s web tool, your state’s complete longitudinal file, or any other dataset—it is important to spend time at the front end making sure that you’ve identified exactly what it is you want to know. What is your question? Which group of children will you need to examine in order to answer it? And what type of data will get you the answer that you are looking for? Longitudinal analyses of foster care data center around two main components—the likelihood of events and the speed with which they happen. In other words, good longitudinal inquiries are always variations on the same two-pronged theme: What happens to children who enter foster care (the likelihood of events), and how long does it take for children to… Read more >