Incorporating data-driven decision making in family preservation
An examination of recurrence rates, case reviews, and lessons learned
Dados Bibliográficos
| ID | 12803442 |
|---|---|
| Autores | James David Simon (0000-0002-5741-009X, California State University Los Angeles, autor correspondente), Sofya Bagdasaryan (California State University Los Angeles) |
| Ano | 2022 |
| Volume | 17 |
| Fascículo | 4 |
| Páginas | 722-746 |
| Data de publicação | 2022-07-26 |
| Peer Reviewed | Sim |
| Open Access | Não |
| Tipo | ARTICLE |
| Periódico | Journal of Public Child Welfare (JOURNAL) |
| Identificadores do periódico | ISSN: 1554-8732 • E-ISSN: 1554-8740 |
| Editora | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/15548732.2022.2101174 |
| OpenAlex | W4288041028 |
| Idioma | EN |
| Citações recebidas | 1 |
| Referências citadas | 35 |
Data-driven decision-making (DDDM) in public child welfare (PCW) has become increasingly important with the passage of the Family First Prevention Services Act (FFPSA), making PCW agencies across the U.S. examine their various programs to ensure that they meet the service requirements of FFPSA. Family Preservation (FP) is an important program that is offered by PCW agencies nationwide, yet little is known about how programs like FP can implement DDDM to examine outcomes to improve practice. This study describes how one of the largest PCW agencies nationwide adopted DDDM in their FP program and presents preliminary findings along with lessons learned as part of the process to meet FFPSA requirements. For example, FP established a baseline recurrence rate using the standard federal definition of the recurrence of maltreatment adapted for FP; this rate was 6.6% for families receiving FP in the target county compared to 8.4% for families not receiving FP services. Subsequent case reviews revealed issues related to engagement, family expectations, and termination codes, which led to standardized definitions and practice changes. Several lessons learned are provided as part of the incorporation of DDDM in FP as well as implications for practice and research
Baseline (sea · Best practice · Business · Medical education · Political science · Process (computing · Public relations · Service (business · Welfare · Computer Science · Evaluation and Performance Assessment · Health Policy Implementation Science · Medicine · Primary Care and Health Outcomes · Psychology · Marketing
Family preservation and family support programs
Substantiation and Recidivism
Thematic Analysis
Preventing the Recurrence of Maltreatment
How Can Data Drive Policy and Practice in Child Welfare? Making the Link in Canada
Building the Evidence Base for Intensive Family Preservation Services
Development of a Quality Assurance and Continuous Quality Improvement (CQI) Model in Public Child Welfare Systems
Four Principles of Big Data Practice for Effective Child Welfare Decision Making
Client participation and outcomes of intensive family preservation services
Family-Centered Services
Continuous Quality Improvement Processes in Child Protection
The Effectiveness of Aftercare Services for African American Families in an Intensive Family Preservation Program
Evaluating family preservation services
Homebuilders and family preservation
What Works Best For Whom? A Closer Look at Intensive Family Preservation Services
Effects of the duration, intensity, and breadth of family preservation services
Moving upstream
Technical reviewing for the Family First Prevention Services Act
A meta-analysis of intensive family preservation programs
The intersection of child welfare services and public assistance
Comparing Response Rates from Web and Mail Surveys
Designing and Conducting Mixed Methods Research
| Obras citantes distintas | 1 |
|---|---|
| Citações por ano | 1 |
| Intervalo de citações | 2025 - 2025 (1) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 1 |