Challenges to sustainable immunization systems in Gavi transitioning countries
Bibliographic record
Abstract
The Global Vaccine Action Plan 2011-2020 (GVAP) aims to extend the full benefit of vaccination against vaccine-preventable diseases to all individuals. More than halfway through the Decade of Vaccines, countries classified as Middle-Income by the World Bank struggle to achieve several GVAP targets. Countries transitioning from Gavi, the Vaccine Alliance, represent a key sub-group of Middle Income Countries. Through a review of available literature on the subject, this study documents the lack of comparative analyses on immunization system performance in countries transitioning from Gavi support. Despite increased emphasis on the importance of programmatic sustainability beyond financing through the Gavi 2016-2020 Strategy and availability of data, existing literature has predominantly documented challenges related to domestic financing of immunization. This study complements a review of current literature with an analysis of country assessments conducted by immunization partners since 2011, in an effort to document programmatic challenges related to decision-making for immunization policy, delivery of services, and access to affordable and timely supply in Gavi transitioning countries. In light of the findings, we suggest continued systematic compilation of country performance data beyond financing to inform policy-making, in particular for: (i) development of a more nuanced theory of change towards sustainable immunization programmes and (ii) measurement of progress and key areas for attention and investment.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".