Overview
There are two center projects within the CICM. These projects include multiple universities and various disciplines from statistics to developmental psychology and newborn medicine. Below are summaries of each project, its intention, and the leader(s) of each.
Center Projects
1.
The Child Welfare Data SMART
(Specification, Management, Analysis, Replication & Transfer)
DataSMART is a project where states can, with modest reformatting of their in-state existing data, run “off the shelf” programs designed by national child maltreatment researchers. States wishing to use DataSMART programs work with the project team to modify their inhouse data, and then execute available programs. Currently existing programs include analyses of racial disproportionality (while considering economic and other factors) and recurrence (configurable for timeframe and type). Utility programs allowing states to import census geographic data and attach those data to their in-state data are also available. More utility programs are in process, including programming allowing states to attach Social Vulnerability Index ratings based on zipcode or county. There is also a predictive risk modeling program in progress.
DataSMART will begin working with states in early February, 2023, and interested states should contact Brett Drake at brettd@wustl.edu
Site Principal Investigators
Paul Lanier, PhD
Member of the Scientific Learning Collaborative
- Email: planier@nospam.email.unc.edu
Professor at University of North Carolina at Chapel Hill
Emily Putnam-Hornstein, PhD
Member of the Scientific Learning Collaborative
John A. Tate Distinguished Professor for Children in Need and Director of Policy at the University of North Carolina at Chapel Hill
Co-Director Children’s Data Network at the University of Southern California
Jared Parrish, PhD
Member of the Scientific Learning Collaborative
Senior Epidemiologist at the Alaska Department of Health and Social Services
Terry Shaw, PhD
Member of the Scientific Learning Collaborative
Associate Professor at the University of Maryland, School of Social Work
Research Division Director at the Institute for Innovation and Implementation
In the past 10 years, the study of child maltreatment has been revolutionized by the availability of large datasets and improvements in computer technology. These advances have created a new opportunity for researchers and public organizations to join together and improve services for children and families. The DataSMART project will establish a ‘Common Data Structure,’ which can be adopted by participating states, along with a library of cutting edge policy-relevant analysis programs. This will allow any state to use ‘off the shelf’ analytic products to support a truly evidence-based policy approach.
Brett Drake
2.
The Identification of Newborns at High Risk for the Occurrence of Preventable Child Maltreatment Project (SURROUND)
This project, led by Mini Tandon at the WashU School of Medicine, will study and compare the performance of newborn screening for identifying the risk of child maltreatment. This RCT will use record data, birth records, and clinical assessments to improve the targeting of preventive services.
The study will implement long-term support strategies to prevent child abuse, initiated within the health system during the first days of a baby’s life.
John Constantino
3.
Scientific Learning Collaborative Dynamic Evidence Map
Child maltreatment (CM) is a significant and global public health concern with a complex array of causes and consequences. Identifying the most impactful modifiable factors at all levels of the ecosystem is a significant challenge for policymakers, practitioners, students, and researchers trying to grasp the multidisciplinary literature that accompanies such complex and dynamic problems. This project is creating a public facing dynamic literature map using a novel application of Group Model Building (GMB), as systems science method, with academic and field experts to develop a qualitative map of hypothesized paths to and following CM and then link this to empirical findings. The goal of the “map” is to address the problem of creating a publicly accessible summary of knowledge across disciplines that helps identify constructs and paths that may have received greater or lesser research attention so that it reflects a high level, and updateable, summary of what is known and gaps for further research. This multi-year process creates an accessible tool through use of electronic interactive visual mapping software in the hopes of better informing practice, policy, and research innovation.