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Record W4403816242 · doi:10.1093/eurpub/ckae144.096

The EU project VAXaction: tacking effectively vaccine hesitancy in Europe

2024· article· en· W4403816242 on OpenAlexaboutno aff
C Signorelli, Anna Odone, Cristiana Barbati, Rita Cuciniello, Carlo Lunetti, F Pennisi, Laura Viviani, Tiago Correia

Bibliographic record

VenueEuropean Journal of Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Law Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTackingBusinessMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract The main objective of VAX-ACTION is to support EU Member States and relevant stakeholders to implement the results of tailored, evidence-based interventions aimed to reduce vaccine hesitancy. Based on the evaluation of effectiveness of these interventions, which will be conducted in selected target regions in EU member states, recommendations will be drawn through diverse dissemination strategies to allow scaling up good practices to other member States, but also to the USA and Canada, where gaps in implementation strategies also have prevented effective, evidence-based interventions aimed to better deal with vaccine hesitancy. The first phase of the research involved some relevant systematic reviews with the aim of: 1. to map public health evidence and research results on large-scale vaccination programs in Europe and north America; 2. to map interventions aimed to address vaccine hesitancy regarding new and well-stablished vaccines and vaccination programs (Covid-19, mpox, national immunizations programs for children) in Europe and north America; 3. to identify successful and unsuccessful interventions designs aimed to address vaccine hesitancy, including challenges in the implementation, evaluation designs, and the feasibility of scaling-up solutions that are context-sensitive; 4. to identify significant similarities or dissimilarities in the designs and outcomes of interventions aimed to address vaccine hesitancy in northern countries where political and academic concern towards this phenomenon is growing; 5. to identify the extent to which ‘grounding’ (bottom-up) experiences aimed to address vaccine hesitancy are evidence-based, build on current international knowledge and guidelines, and whether they are internationally reported; 6. to systematize the mapping of interventions aimed to address vaccine hesitancy in the northern hemisphere in a way that can be shared and used by third parties. This contribution will illustrate the results of the first phase of the project.

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 imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0020.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.086
GPT teacher head0.375
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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