Shaping global vaccine acceptance with localized knowledge: a report from the inaugural VARN2022 conference
Bibliographic record
Abstract
2022. This inaugural event brought together a global representation of experts to discuss key priorities and opportunities emerging across the ecosystem of vaccine acceptance and demand, from policies to programs and practice. Convened by the Sabin Vaccine Institute, VARN aims to support dialogue among multidisciplinary stakeholders to enhance the uptake of social and behavioral science-based solutions for vaccination decision-makers and implementers. The conference centered around four key themes: 1) Understanding vaccine acceptance and its drivers; 2) One size does not fit all: community- and context-specific approaches to increase vaccine acceptance and demand; 3) Fighting the infodemic and harnessing social media for good; and 4) Frameworks, data integrity and evaluation of best practices. Across the conference, presenters and participants considered the drivers of and strategies to increase vaccine acceptance and demand relating to COVID-19 vaccination and other vaccines across the life-course and across low-, middle- and high-income settings. VARN2022 provided a wealth of evidence from around the world, highlighting the need for human-centered, multi-sectoral and transdisciplinary approaches to improve vaccine acceptance and demand. This report summarizes insights from the diverse presentations and discussions held at VARN2022, which will form a roadmap for future research, policy making, and interventions to improve vaccine acceptance and demand globally.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".