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Record W2910343997 · doi:10.3389/fpubh.2018.00381

Combating Vaccine Hesitancy: Teaching the Next Generation to Navigate Through the Post Truth Era

2019· article· en· W2910343997 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Public Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsImmunizationVariety (cybernetics)PopulationMedicineKey (lock)Internet privacyPsychologyPublic relationsComputer scienceImmunologyPolitical scienceEnvironmental healthComputer security

Abstract

fetched live from OpenAlex

Despite scientific evidence supporting the fact that vaccines are fundamental tools for preventing infectious diseases, a percentage of the population still refuses some or all of them. Vaccine hesitancy has become a widespread issue, and its complexity lies in the great variety of factors that can influence decisions about immunization, which are not just vaccine-related concerns, but also involve personal and societal levels. Our research group performed an extensive literature review to analyze: (1) different age groups, their relation to the problem and their characteristics; (2) the most important information (key messages) about immunization that could be used to counteract hesitancy; and (3) best approaches to transmit the messages to the target groups. We propose a long-term approach to overcome vaccine hesitancy that involves the education of children and adolescents on the basics about immunization and critical thinking, using different communication channels.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.325
Teacher spread0.271 · 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