课题基金 / 基金详情

Participatory Decision Making and Policy Production in Child Welfare

Participatory Decision Making and Policy Production in Child Welfare
儿童福利的参与式决策和政策制定
批准号:
LP0667779
负责人:
Prof Karen Healy
金额:
$18.64万
依托单位国家:
澳大利亚
项目类别:
Linkage Projects
财政年份:
2006
资助国家:
澳大利亚
项目状态:
已结题
起止时间:
2006-04-03 至 2010-12-03

项目摘要

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中文摘要
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英文摘要
Our research focuses on one of the most sensitive and complex areas of citizen participation - the participation of families in child welfare decision making and policy production. The project will build practical models for participatory practices with families with young children who are engaged with child welfare services. By enhancing family members' participation in decision making and policy production this project will contribute to strengthening the family safety net for young vulnerable children and to the creation of policies that better recognise the diverse concerns of young families engaged in child welfare systems.
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会议论文
Enhancing Children's Journey in Out-of-Home Care:A Multi-perspective Study
  • 批准号:
    LP190100131
  • 项目类别:
    Linkage Projects
  • 资助金额:
    $54.64万
  • 财政年份:
    2020
  • 负责人:
    Prof Karen Healy
  • 依托单位:
A study of best practice in intervention with parental agreement: creating change with families in statutory child protection services
  • 批准号:
    LP110200423
  • 项目类别:
    Linkage Projects
  • 资助金额:
    $8.83万
  • 财政年份:
    2011
  • 负责人:
    Prof Karen Healy
  • 依托单位:
A study of best practice in workplace support and development of newly qualified community services workers
  • 批准号:
    LP100100418
  • 项目类别:
    Linkage Projects
  • 资助金额:
    $9.21万
  • 财政年份:
    2010
  • 负责人:
    Prof Karen Healy
  • 依托单位:
Families on the Fringe: Promoting the Social Inclusion of Young Families who have Relocated to Non-Metropolitan Areas
  • 批准号:
    LP0560740
  • 项目类别:
    Linkage Projects
  • 资助金额:
    $16.51万
  • 财政年份:
    2005
  • 负责人:
    Prof Karen Healy
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis