Understanding HPV Vaccination Policymaking in Rwanda: A Case of Health Prioritization and Public-Private-Partnership in a Low-Resource Setting.
Bibliographic record
Abstract
Abstract Background Rwanda was the first African nation to initiate a nationwide HPV vaccination program in 2011 where the incidence of cervical cancer was reduced from 34.5 cases to 28.2 cases per 100,000 women and the mortality rate declined from 25.4–20.1% from before 2011 to 2020, respectively. This study sought to clarify the HPV vaccination policymaking process in Rwanda and the lessons policymakers in other low-income settings can learn.Methods Kingdon's multiple stream framework and Foucault’s concept of governmentality were used as lenses to understand the Rwandan policymaking process that hastened the introduction of a national HPV vaccination program in 2011. Perspectives of policy makers engaged in HPV vaccination policy were gathered from published sources, along with key informant interviews.Results Rwanda presents a valuable case study of Kingdon’s multiple streams model clarifying how governmental priority setting (policy stream) for cervical cancer prevention (problem stream) along with public and private incentives and policy entrepreneurship (politics stream) aligned to foster program implementation. Rwanda’s track record of successful vaccination programs enabled by a culture of local accountability referred to as Imihigo created public and private sector incentives. Effective stakeholder engagement, health priority setting, and resource mobilization garnered locally and through international development aid, reflect indicators of policy success. The national HPV policymaking process in Rwanda unfolded in a relatively cohesive and stable policy network.Conclusion Although peripheral stakeholder resistance and a constrained national budget can present a threat to policy survival, the study shows that such factors as the engagement of policy entrepreneurs within a policy network, private sector incentives and international aid were effective in ensuring policy resolution.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".