Evaluating Global Health Partnerships: A Case Study of a Gavi HPV Vaccine Application Process in Uganda
Bibliographic record
Abstract
BACKGROUND: Global health partnerships have grown rapidly in number and scope, yet there has been less emphasis on their evaluation. Gavi, the Vaccine Alliance, is one such public-private partnership; in Gavi-eligible countries partnerships are dynamic networks of immunization actors who work together to support all stages and aspects of Gavi support. This paper describes a conceptual framework - the partnership framework - and analytic approach for evaluating the perceptions of partnerships' added value as well as the results from an application to one case in Uganda. METHODS: We used a mixed-methods case study design embedded in the Gavi Full Country Evaluations (FCE) to test the partnership framework on Uganda's human papillomavirus (HPV) vaccine application partnership. Data from document review, interviews, and social network surveys enabled the testing of the relationships between partnership framework domains (context, structure, practices, performance, and outcomes). Topic guides were based on the framework domains and network surveys identified working together relationships, professional trust, and perceptions of the effectiveness, efficiency, and legitimacy of the partnership's role in this process. RESULTS: Data from seven in-depth interviews, 11 network surveys and document review were analyzed according to the partnership framework, confirming relationships between the framework domains. Trust was an important contributor to the perceived effectiveness of the process. The network was structured around the EPI program, who was considered the leader of this process. While the structure and composition of the network was largely viewed as supporting an effective and legitimate process, the absence of the Ministry of Education (MoE) may have had downstream consequences if this study's results had not been shared with the Ministry of Health (MoH) and acted upon. The partnership was not perceived to have increased the efficiency of the process, perhaps as a result of unclear or absent guidelines around roles and responsibilities. CONCLUSION: The health and functioning of global health partnerships can be evaluated using the framework and approach presented here. Network theory and methods added value to the conceptual and analytic processes and we recommend applying this approach to other global health partnerships to ensure that they are meeting the complex challenges they were designed to address.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".