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Research Partnership to Improve NH State Data on Abused and Neglected Children: NH PARCS* Glenda Kaufman Kantor, CCRC, UNH Melissa Correia, NH DCYF & Melissa.

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Presentation on theme: "Research Partnership to Improve NH State Data on Abused and Neglected Children: NH PARCS* Glenda Kaufman Kantor, CCRC, UNH Melissa Correia, NH DCYF & Melissa."— Presentation transcript:

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 Target: Families substantiated for CA/N Families not substantiated but at risk for CA/N 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 Program Characteristics Intermediate Outcomes Proximal Outcomes Distal Outcomes Family reunification Stability/ permanency of child placement 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 Service Delivery Systems: Mental Health Substance Abuse Batterer Intervention Availability of resources 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 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 Change Where 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 Change Where 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 Change Where 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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