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Record W2790708240 · doi:10.1080/14760584.2018.1456921

Educating children and adolescents about vaccines: a review of current literature

2018· review· en· W2790708240 on OpenAlexaff
Alexander R. Maisonneuve, Holly O. Witteman, Jamie Brehaut, Ève Dubé, Kumanan Wilson

Bibliographic record

VenueExpert Review of Vaccines · 2018
Typereview
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsOttawa HospitalInstitut National de Santé Publique du QuébecUniversité LavalUniversity of Ottawa
Fundersnot available
KeywordsCurrent (fluid)MedicinePsychologyImmunology

Abstract

fetched live from OpenAlex

INTRODUCTION: Until recently, research on vaccine hesitancy has focused primarily on parent populations. Although adolescent knowledge and views are gaining momentum within the literature, particularly with regards to the human papillomavirus and influenza, children remain a virtually unstudied population with regards to vaccine hesitancy. AREAS COVERED: This review focuses on the lack of literature in this area and argues for more vaccine hesitancy research involving child and adolescent populations. It also outlines special issues to consider when framing health promotion messages for children and adolescents. Finally, we explore the use of new and existing technologies as delivery mechanisms for education on vaccines and immunizations in populations of children and adolescents. EXPERT COMMENTARY: Children undergo cognitive development and experiences with vaccines (e.g. pain or education) have the potential to create future attitudes toward vaccines. This can influence future vaccine behaviour, including their participation in decision-making around adolescent vaccines, their decisions to vaccinate themselves when they are adults, and their decisions to vaccinate their own children. Interventions aimed at children, such as education, can create positive attitudes toward vaccines. These can also potentially influence parental attitudes toward vaccines as children convey this knowledge to them. Both of these impacts require further study.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.410
Teacher spread0.381 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations27
Published2018
Admission routes1
Has abstractyes

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