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Research Partnership to Improve NH State Data on Abused and Neglected Children: NH PARCS*

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.

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Research Partnership to Improve NH State Data on Abused and Neglected Children: NH PARCS*

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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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