Increasing vaccine acceptance using evidence-based approaches and policies: Insights from research on behavioural and social determinants presented at the 7th Annual Vaccine Acceptance Meeting
Bibliographic record
Abstract
BACKGROUND: In 2019, the World Health Organization (WHO) flagged vaccine hesitancy as one of the top 10 threats to global health. The drivers of and barriers to under-vaccination include logistics (access to and awareness of affordable vaccines), as well as a complex mix of psychological, social, political, and cultural factors. INCREASING VACCINE UPTAKE: There is a need for effective strategies to increase vaccine uptake in various settings, based on the best available evidence. Fortunately, the field of vaccine acceptance research is growing rapidly with the development, implementation, and evaluation of diverse measurement tools, as well as interventions to address the challenging range of drivers of and barriers to vaccine acceptance. ANNUAL VACCINE ACCEPTANCE MEETINGS: Since 2011, the Mérieux Foundation has hosted Annual Vaccine Acceptance Meetings in Annecy, France that have fostered an informal community of practice on vaccination confidence and vaccine uptake. Mutual learning and sharing of knowledge has resulted directly in multiple initiatives and research projects. This article reports the discussions from the 7th Annual Vaccine Acceptance Meeting held September 23-25, 2019. During this meeting, participants discussed emergent vaccine acceptance challenges and evidence-informed ways of addressing them in a programme that included sessions on vaccine mandates, vaccine acceptance and demand, training on vaccine acceptance, and frameworks for resilience of vaccination programmes.
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.
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 | 0.074 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".