Lessons Learned From Implementing Prospective, Multicountry Mixed-Methods Evaluations For Gavi And The Global Fund

GLOBAL HEALTH-SCIENCE AND PRACTICE(2020)

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摘要
Introduction: As global health programs have become increasingly complex, corresponding evaluations must be designed to assess the full complexity of these programs. Gavi and the Global Fund have commissioned 2 such evaluations to assess the full spectrum of their investments using a prospective mixed-methods approach. We aim to describe lessons learned from implementing these evaluations.Methods: This article presents a synthesis of lessons learned based on the Gavi and Global Fund prospective mixed-methods evaluations, with each evaluation considered a case study. The lessons are based on the evaluation team's experience from over 7 years (2013-2020) implementing these evaluations. The Centers for Disease Control and Prevention Framework for Evaluation in Public Health was used to ground the identification of lessons learned.Results: We identified 5 lessons learned that build on existing evaluation best practices and include a mix of practical and conceptual considerations. The lessons cover the importance of (1) including an inception phase to engage stakeholders and inform a relevant, useful evaluation design; (2) aligning on the degree to which the evaluation is embedded in the program implementation; (3) monitoring programmatic, organizational, or contextual changes and adapting the evaluation accordingly; (4) hiring evaluators with mixed-methods expertise and using tools and approaches that facilitate mixing methods; and (5) contextualizing recommendations and clearly communicating their underlying strength of evidence.Conclusion: Global health initiatives, particularly those leveraging complex interventions, should consider embedding evaluations to understand how and why the programs are working. These initiatives can learn from the lessons presented here to inform the design and implementation of such evaluations.
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global fund,gavi,implementing prospective,mixed-methods
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