Lessons Learned From Implementing Prospective, Multicountry Mixed-Methods Evaluations for Gavi and the Global Fund
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".