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 |
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Hauptverfasser: | , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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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. |
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ISSN: | 0834-1516 1496-7308 |
DOI: | 10.3138/cjpe.71259 |