Making sense of Rwanda’s remarkable vaccine coverage success
Bibliographic record
Abstract
After the Rwandan genocide in 1994, vaccine coverage was close to zero. Several factors, including extreme poverty, rural populations and mountainous geography affect Rwandans’ access to immunizations. Post-conflict, various other factors were identified, including the lack of immunization program infrastructure, and lack of population-level knowledge and demand. In recent years, Rwanda is one of few countries that has demonstrated a sustained increase to near universal vaccination coverage, with a current rate of 98%. Our aim was to ask why and how Rwanda achieved this success so that it could potentially be replicated in other countries.Literature searches of scientific and grey literature, as well as other background research, was conducted from September 2016 through August 2017, including primary fieldwork in Rwanda. We determined that four factors have had a major influence on the Rwandan vaccine program, including strong central government leadership (political will), a culture of accountability, local ownership and a strong health value chain. Rwanda’s national immunization program is rooted in a political landscape shaped by unique aspects of Rwandan history and culture. Rwanda has a strong central government and a hierarchical chain of command supported by decentralized implementation bodies. A culture of accountability transcends the entire health system and there is local-level ownership of the immunization program, including the role of engaged community health workers and a strong health information system. Together, these four factors likely account for Rwanda’s vaccination coverage success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".