Building a way forward: Enabling community voices to forge the path toward successful immunization for all
Bibliographic record
Abstract
Community engagement is vital to the development of people-centered, successful vaccination programs. The diverse Vaccination Acceptance Research Network (VARN) community brings together interdisciplinary professionals from across the immunization ecosystem who play a crucial role in vaccination acceptance, demand, and delivery. Over the course of the VARN2023 conference, researchers and practitioners alike shared ideas and experiences focused on strategies and approaches to building trust between communities and health systems to increase equity in vaccination. Health professionals and community members must have equal value in the design and delivery of community-centered immunization services, while key vaccination decision-makers must also consider community experiences, concerns, and expertise in program design and policymaking. Therefore, strategies for community engagement and cultivating trust with communities are crucial for the success of any immunization program. Furthermore, health workers need additional skills, support, and resources to effectively communicate complex information about immunization, including effective strategies for countering misinformation. This article summarizes three skills-building sessions offered at the VARN2023 conference, focused on human-centered design, motivational interviewing, and engaging with journalists to leverage the voices of communities. These sessions offered practical, evidence-based tools for use across geographic and social settings that can be used by practitioners, researchers, and other stakeholders to increase vaccination demand and uptake in their communities.
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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 | 0.039 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".