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Enhancing NH State Child Welfare Data Quality

Collaborative research project aiming to improve data on abused and neglected children in New Hampshire. Objectives include joint research, identifying key data elements, data reform steps, and ongoing partnership development.

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Enhancing NH State Child Welfare Data Quality

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  1. Research Partnership to Improve NH State Data on Abused and Neglected Children: NH PARCS* Glenda Kaufman Kantor, CCRC, UNH Melissa Correia, NH DCYF & Melissa Wells, UNH * Presented at 8th National Child Welfare Data Conference, Washington, D.C. July 2005 OJJDP award # 2003-JN-FX-0064

  2. PROJECT OBJECTIVES

  3. Project Objectives • 1. Develop joint research agenda • 2. Identify key data elements that improve on current risk & outcome measures • 3. Identify intermediate steps in data reforms • 4. Develop ongoing partnership

  4. Obj.1: Develop Joint Research Agenda • Research Synthesis • Research on Practice • Basic Research • Program Evaluation • Longitudinal Research

  5. Obj.1: Work in Progress • Determine NH DCYF needs • What is efficacy of NH DCYF & their contractors? • How have other systems approached data reforms & partnerships • What are limits of current data systems? • What are the important outcomes? • Review Literature • Research Partnerships • Use of outcome data • National data reforms

  6. Obj. 1: Accomplishments • What did we learn? • Partnerships • National data issues • Outcomes • SDM • What do we plan? • Possible Study of SDM • Conduct Longitudinal Analysis of AFCARS

  7. AFCARS Longitudinal Analysis • Spring 2006 project • Merge multiple years of NH AFCARS data • Conduct focus groups with DCYF employees to identify key questions • Analyze placement patterns, with specific emphasis on adolescents in foster care • Contact Melissa.Wells@unh.edu for additional information

  8. Objective 2: Identify Key Data Elements • Draft of Logic Model (evidence based) • Review of Model by NH PARCS • Review by Stakeholders • Revision Based on Feedback

  9. Objective 2 Accomplishment: Logic Model

  10. Elements for a DCYF Logic Model Target Population Program Characteristics Intermediate Outcomes Proximal Outcomes Distal Outcomes • Target: • Families substantiated • for CA/N • Families not substantiated but at risk for CA/N • Supports, Skills and Services • Child: • Develop. milestones • Social skills • Absence of anxiety/ depression • Involvement w/ community activities, mentors • Increase Parent’s: • Positive social supports • Coping skills • Parenting capacity (responsiveness, involvement w/ child, warmth and affection, discipline) • Social capacity (education, employment, move toward financial stability, job training; access financial supports, services, WIC/TANF) • Family Cohesion: • Eat together/rituals • Participate in family activities • Mutual help, support, respect • Family organization/rules • Utilization of services: • Child physical health • Adult and child mental health • Adult substance abuse treatment Family Well-being & Safety (“EFFICACY”) 1) Freedom from violence/abuse—no maltreatment for parent and child 2) Loving/stable relationship with an adult for parent and child 3) Parent’s and child’s ability to problem solve (coping) 4) Family’s integration into the community 5) Economic stability for the parent/ school functioning for the child 6) Physical and mental health (wellbeing) for the parent and child • Service Delivery • Systems: • Mental Health • Substance Abuse • Batterer Intervention • Availability of resources • Population Characteristics: • Child: • Developmental status • Mental health/sub. Abuse • Educational functioning • Community integration • (afterschool programs/clubs) • Parent: • Substance abuse • Physical/mental capacity • Stress/Coping ability • Parenting capacity • Domestic violence/trauma • Readiness to change • Community integration • (parent support groups, • church, employment) • Family: • Priors • Pattern of relationships • Culture/ethnicity • Community: • Housing • Prenatal, medical, dental care • Resources • Other services Family reunification Stability/ permanency of child placement Case Management

  11. Objective 3: Identify Intermediate Steps in Data Reforms • Review All Current Sources of Data • SACWIS; SDM forms; CFSR (Quality Control Reports) • Map available elements onto Logic Model

  12. Objective 3: Accomplishments • Data Matrix Developed • Data Report in Progress • CPSW Data overload • Data quality • Missing data

  13. Next Steps • Data Report • Recommendations for streamlining; • New Measures; • Assessments needed for accurate measurement • Data Reform Process • Gain consensus on goals, definitions, measures, assessments, training needs • Staff buy-in

  14. Objective 4: Develop Ongoing Partnership • Performance evaluation; Basic research; Data analysis and Reports • Sustainability

  15. Objective 5: Develop Cross-System Data Communication Process • Identify data sharing needs that would improve joint systems performance, and well-being of families and children that present themselves to multiple systems

  16. NH DCYF Perspective

  17. NH DCYF - Using Data to Manage ChangeWhere We’ve Come From • Manual data collection/disconnected sources • Minimal use/trust in SACWIS data – disconnect between “numbers” and “practice” • Production reports in print/no drill-down/no ability to customize • Lack of data definitions/inconsistent data from report to report • Production of reports not timely

  18. NH DCYF - Using Data to Manage ChangeWhere We Are Now • SACWIS reliance as sole data source • Process/Compliance oriented data • Driven by external forces • Accuracy of reports “proven” by providing drill-down – review of detail through supervision • Reports in Excel - Ability to customize by DO/CPSW – field supervisors requesting data! • Provision of complete data definitions and sources – including Bridges screen shots • Production of reports still requires time/resources

  19. NH DCYF - Using Data to Manage ChangeWhere We’re Going • Real time data for supervisor use – Data integrity requires streamlining data entry • Ability to pull from other state data systems • Sustainable methods for longitudinal analysis • SDM/Targeting of resources to highest need - quality measures are vital • Outcome vs. Process driven – Data analysis vs. Data collection Efficacy of services

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