Lessons learned in using realist evaluation to assess maternal and newborn health programming in rural Bangladesh
Citation
Adams, A., Sedalia, S., McNab, S., & Sarker, M. (2016). Lessons learned in using realist evaluation to assess maternal and newborn health programming in rural bangladesh. Health Policy and Planning, 31(2), 267-275. doi:10.1093/heapol/czv053Abstract
Realist evaluation furnishes valuable insight to public health practitioners and policy makers about how and why interventions work or don't work. Moving beyond binary measures of success or failure, it provides a systematic approach to understanding what goes on in the 'Black Box' and how implementation decisions in real life contexts can affect intervention effectiveness. This paper reflects on an experience in applying the tenets of realist evaluation to identify optimal implementation strategies for scale-up of Maternal and Newborn Health (MNH) programmes in rural Bangladesh. Supported by UNICEF, the three MNH programmes under consideration employed different implementation models to deliver similar services and meet similar MNH goals. Programme targets included adoption of recommended antenatal, post-natal and essential newborn care practices; health systems strengthening through improved referral, accountability and administrative systems, and increased community knowledge. Drawing on focused examples from this research, seven steps for operationalizing the realist evaluation approach are offered, while emphasizing the need to iterate and innovate in terms of methods and analysis strategies. The paper concludes by reflecting on lessons learned in applying realist evaluation, and the unique insights it yields regarding implementation strategies for successful MNH programming.
Description
This article was published in Health Policy and Planning [© 2016 Oxford University Press] and the definite version is available at: http://heapol.oxfordjournals.org/content/early/2015/06/22/heapol.czv053.short?rss=1Department
James P Grant School of Public Health, BRAC UniversityType
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