Workforce Metrics Using Child Welfare Data

Though most workforce-related data are stored in human resources and training databases, some workforce metrics can be constructed from child welfare systems data alone. These metrics fall into three categories: caseload, case continuity, and worker- and unit-level variability.

  1. Caseload metrics are among the most commonly used workforce metrics that are obtained from child welfare data. They can serve a variety of purposes, such as informing case assignment decisions; assessing the work burden experienced by staff, including potential workload inequities among workers; and informing workforce planning or staffing decisions.
  2. Case continuity measures address the degree to which cases receive attention from the same worker over time. The rationale for this type of measure is that greater continuity of involvement of the same workers over time may lead to improved outcomes due to greater levels of family engagement, reduced loss of case-related information, and fewer disruptions of ongoing progress assessments and other processes. Measuring case continuity and its connections to case outcomes may provide greater insight into whether and how personnel movement impacts children and families. Case continuity measures are a relatively new thing, and their use in child welfare management is still rare.
  3. Worker- and unit-level metrics can be used to assess potential variability in child welfare practice and outcomes. The notion here is that a worker or a group of workers (e.g., in a supervisory unit) acquires certain “characteristics” over time as they experience work with children and families. These “characteristics” will vary because the experiences at work are different. Though there will be other types of variation across workers and units, the idea here is that there may be systematic and stable differences in the way work “culture” is transmitted that are worthy of exploration. To be clear, these variations can contribute to differences in case outcomes. Thus, depending on the nature and impact of the variation, some experiential differences may be worth replicating across staff, whereas others may require an intervention to realign practice. Metrics designed to capture and analyze staff characteristics and their relationship to case outcomes is on the frontier of workforce analytics, as are efforts to test interventions based on these ideas.

In this video, John Fluke, QIC-WD Evaluation Specialist, provides further details on calculating caseload metrics.

An additional resource, Workforce Metrics Using Child Welfare Data, documents key points from the video presentation and provides additional examples of how to use child welfare data to calculate workforce metrics.

The content contained in this blog post was developed as part of the QIC-WD’s Child Welfare Workforce Analytics Institute. The Institute was designed to facilitate growth and collaboration between leaders in child welfare and human resources in their awareness, knowledge, and use of data analytics.