Implementation of the Meningitis Vaccine Project in Africa: lessons for vaccine implementation programs
Bibliographic record
Abstract
Background The Meningitis Vaccine Project (MVP) was an international partnership that began in 2010 and focused on the development, testing, licensure, and widespread introduction of a conjugate vaccine with the promise of protecting millions of lives from group A meningococcal meningitis. To bridge gaps upstream of vaccine administration and in understanding the project’s planning, delivery and outcomes, the primary objective of this study was to delineate the barriers to and facilitators of sustainable implementation in an upstream context of the delivery of the MenAfriVac vaccine and to draw critical lessons for other vaccination programs. Methods We conducted a qualitative study with key project partners and leaders who worked on the MVP project. The interviewees were initially identified through a literature review and a search of publications and evaluations of the project. Eighteen key stakeholders were interviewed. The interviewees included individuals in the roles of implementation oversight, champion, partner, funder, and frontline implementer. The Consolidated Framework for Implementation Research (CFIR) informed the identification of a priori codes for the directed content analysis. Results There were multiple barriers and facilitators to implementing the vaccine under all five domains of the CFIR framework, including intervention characteristics , outer setting , inner setting , characteristics of individuals , and process . The most common domains mentioned by stakeholders that facilitated the success of the MVP were inner setting and process ; specifically, three constructs were most representative— Tension for Change, Formally Appointed Implementation Leaders, and External Change Agents . Conclusions The tension for change—that is, the degree to which stakeholders perceive the current situation as intolerable or needing change— advocated by African leaders served as a crucial foundation for solid leadership for the MPV. The leadership came in the form of the project director and other formally appointed leaders, who could plan, engage and execute the project’s goals effectively. This leadership translated into consistent messaging, information sharing, and multiple levels of engagement with external change agents (including technical experts), which promoted a shared sense of readiness contributing to significant change investments in implementing MVP.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".