University of Calgary
$11,291,583.00 CAD
- Department
- Global Affairs Canada
- Program
- —
- Recipient country
- Canada
- Fiscal year
- 2015-2016
- Agreement period
- March 31, 2016 – June 30, 2020
- Project status
- Closed
- Reference
- CA-3-D001998001
Published purpose
This project aims to reduce the number of maternal, child and newborn deaths by strengthening local health systems. Project activities include: (1) providing technical assistance to regional and local-level health management teams and health service providers to improve planning and delivery of health services; (2) training health providers and community health workers on delivering culturally-appropriate health services in line with international standards; (3) equipping health facilities with relevant supplies needed to deliver key maternal and child health services; (4) working with key community figures and local committees to improve maternal and child health; and (5) informing and engaging the Canadian public on key maternal and child health issues. This project is aims to reach close to 400,000 people in Tanzania, including 125,000 women of reproductive age, 112,000 children under five, 90,000 pregnant women and 80,000 newborns. This project is being implemented through a consortium led by the University of Calgary, with Agriteam Canada, the Canadian Paediatric Society, Save the Mothers and the Mbarara University of Science and Technology. The University of Calgary is implementing the project in Tanzania in collaboration with local partners that include the Catholic University of Health and Allied Science and the Regional Council Health Management Team.
In plain English AI-generated
This project, led by the University of Calgary and funded by Global Affairs Canada, aims to reduce maternal, child, and newborn deaths in Tanzania by strengthening local health systems, training health workers, equipping facilities, and engaging communities, reaching nearly 400,000 people.
Category: Foreign Aid Development Primarily funds activity outside Canada
Based on the published description. See how AI categorization works — the linked official record is authoritative.
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