Shaping global vaccine acceptance with localized knowledge: a report from the inaugural VARN2022 conference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it