Building and sustaining public and political commitment to the value of vaccination: Recommendations for the Immunization Agenda 2030 (Strategic Priority Area 2)
Bibliographic record
Abstract
Vaccines have contributed to substantial improvements in health and social development outcomes for millions in recent decades. However, equitable access to immunization remains a critical challenge that has stalled progress toward improving several health indicators around the world. The COVID-19 pandemic has also negatively impacted routine immunization services around the world further threatening universal access to the benefits of lifesaving vaccines. To overcome these challenges, the Immunization Agenda 2030 (IA2030) focuses on increasing both commitment and demand for vaccines. There are three broad barriers that will need to be addressed in order to achieve national and subnational immunization targets: (1) shifting leadership priorities and resource constraints, (2) visibility of disease burden, and (3) social and behavioral drivers. IA2030 proposes a set of interventions to address these barriers. First, efforts to ensure government engagement on immunization financing, regulatory, and legislative frameworks. Next, those in subnational leadership positions and local community members need to be further engaged to ensure local commitment and demand. Governance structures and health agencies must accept responsibility and be held accountable for delivering inclusive, quality, and accessible services and for achieving national targets. Further, the availability of quality immunization services and commitment to adequate financing and resourcing must go hand-in-hand with public health programs to increase access to and demand for vaccination. Last, strengthening trust in immunization systems and improving individual and program resilience can help mitigate the risk of vaccine confidence crises. These interventions together can help ensure a world where everyone, everywhere has access to and uses vaccines for lifesaving vaccination.
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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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".