整合数据评估全球健康大挑战

IF 0.2 Q4 SOCIAL SCIENCES, INTERDISCIPLINARY

Canadian Journal of Program Evaluation Pub Date : 2022-03-28 DOI:10.3138/cjpe.71259

Blen M. Biru, Andrea Taylor, Sowmya Rajan, Kathryn Crissman, O. Ogbuoji, Fernando Fernholz, Siddharth Dixit, Mina Shahid, Pratik A Doshi, A. Finnegan, K. Udayakumar, J. Baumgartner

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引用次数: 0

摘要

这篇文章描述了用于评估出生时拯救生命的综合、混合方法（MM）设计(SL@B)程序。SL@B是一个多方利益攸关方、捐助者支持的全球卫生倡议，旨在通过创新解决孕产妇和新生儿死亡率问题。自从SL@B自2011年启动以来，该项目已在全球范围内通过147个奖项支持了116项创新。这个大型复杂项目的评估包括一个很大程度上符合评估复杂性原则的回顾性MM设计。本文重点介绍了这些MM评估策略和用于完成SL@B评估，可以为投资组合级别的全球卫生项目的未来评估提供信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Integrating Data to Evaluate a Global Health Grand Challenge

Th is article describes the integrated, mixed methods (MM) design used to evaluate the Saving Lives at Birth (SL@B) program. SL@B is a multi-stakeholder, donor-supported global health initiative to tackle maternal and neonatal mortality via innovation. Since SL@B’s launch in 2011, the program has supported 116 innovationsthrough 147 awards around the globe. The evaluation for this large and complex program included a largely retrospective MM design aligned with principles of evaluating complexity. This paper highlights these MM evaluation strategies and integration dimensions employed to complete the SL@B evaluation that could inform future evaluations of portfolio-level global health programs.

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来源期刊

Canadian Journal of Program Evaluation SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

1.60

自引率

25.00%

发文量