Integrating Data to Evaluate a Global Health Grand Challenge

This 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 sup...

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Veröffentlicht in:Canadian journal of program evaluation 2022, Vol.36 (3), p.336-354
Hauptverfasser: Biru, Blen, Taylor, Andrea, Rajan, Sowmya, Crissman, Kate, Ogbuoji, Osondu, Fernholz, Fernando, Dixit, Siddharth, Shahid, Minahil, Doshi, Pratik, Udayakumar, Krishna, Finnegan, Amy, Baumgartner, Joy Noel
Format: Artikel
Sprache:eng
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Zusammenfassung:This 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 innovations through 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.
ISSN:0834-1516
1496-7308
DOI:10.3138/cjpe.71259