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Development of a facilitation intervention to increase the use of research evidence in long term care.

Development of a facilitation intervention to increase the use of research evidence in long term care.
制定促进干预措施，以增加长期护理中研究证据的使用。
批准号：
180461
负责人：
Cranley Lisa A
金额：
$8.38万
依托单位：
University of Alberta
依托单位国家：
加拿大
项目类别：
Fellowship Programs
财政年份：
2009
资助国家：
加拿大
项目状态：
已结题
起止时间：
2009-02-01 至 2011-02-01
来源：
https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=180461&lang=en
关键词：
Care Delivery Decision Making Facilitation Health Services Research Intervention Development Knowledge Translation Long Term Care Research Utilization Unlicensed Care Providers

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中文摘要
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英文摘要
The goal of this project is to develop and test an intervention to increase health care aides' use of research evidence in long-term care. In nursing homes in Canada 70-80% of direct care is delivered by health care aides and 70-80% if not more of nursing
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Balancing Infection Prevention and Control Measures with Person-Centered Care to Support Resident Well-Being in Long-Term Care
  • 批准号：
    498852
  • 项目类别：
    Operating Grants
  • 资助金额：
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  • 财政年份：
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  • 负责人：
    Cranley Lisa A
  • 依托单位：
    University of Toronto
Balancing Infection Prevention and Control Measures with Person-Centered Care to Support Resident Well-Being in Long-Term Care
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  • 负责人：
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  • 依托单位：
    University of Toronto
Balancing Infection Prevention and Control Measures with Person-Centered Care to Support Resident Well-Being in Long-Term Care.
  • 批准号：
    477337
  • 项目类别：
    Operating Grants
  • 资助金额：
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  • 财政年份：
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  • 依托单位：
    University of Toronto
Implementing an intervention to foster meaningful engagement and shared decision-making among residents, families and healthcare teams in long-term care: A mixed methods approach.
  • 批准号：
    371665
  • 项目类别：
    Operating Grants
  • 资助金额：
    $11.15万
  • 财政年份：
    2017
  • 负责人：
    Cranley Lisa A
  • 依托单位：
    University of Toronto
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis