The second annual Vaccination Acceptance Research Network Conference (VARN2023): Shifting the immunization narrative to center equity and community expertise
Bibliographic record
Abstract
Promoting vaccine acceptance and demand is an essential, yet often underrecognized component of ensuring that everyone has access to the full benefits of immunization. Convened by the Sabin Vaccine Institute, the Vaccination Acceptance Research Network (VARN) is a global network of multidisciplinary stakeholders driving strengthened vaccination acceptance, demand, and delivery. VARN works to advance and apply social and behavioral science insights, research, and expertise to the challenges and opportunities facing vaccination decision-makers. The second annual VARN conference, When Communities Lead, Global Immunization Succeeds, was held June 13-15, 2023, in Bangkok, Thailand. VARN2023 provided a space for the exploration and dissemination of a growing body of evidence, knowledge, and practice for driving action across the vaccination acceptance, demand, and delivery ecosystem. VARN2023 was co-convened by Sabin and UNICEF and co-sponsored by Gavi, the Vaccine Alliance. VARN2023 brought together 231 global, regional, national, sub-national, and community-level representatives from 47 countries. The conference provided a forum to share learnings and solutions from work conducted across 40+ countries. This article is a synthesis of evidence-based insights from the VARN2023 Conference within four key recommendations: (1) Make vaccine equity and inclusion central to programming to improve vaccine confidence, demand, and delivery; (2) Prioritize communities in immunization service delivery through people-centered approaches and tools that amplify community needs to policymakers, build trust, and combat misinformation; (3) Encourage innovative community-centric solutions for improved routine immunization coverage; and (4) Strengthen vaccination across the life course through building vaccine demand, service integration, and improving the immunization service experience. Insights from VARN can be applied to positively impact vaccination acceptance, demand, and uptake around the world.
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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 | 0.051 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".