MétaCan
Menu
Back to cohort
Record W4297982495 · doi:10.15173/child.v1i1.3127

COVID-19 Vaccination Distribution and Uptake: Addressing Vaccine Hesitancy in Canada

2022· article· en· W4297982495 on OpenAlexaboutno aff
Nancy Du, Sukhsahij Gill, Gillian Grant-Allen, Atiya Iqbal, B. Adebayo Samson, Nicole Wu

Bibliographic record

VenueThe Child Health Interdisciplinary Literature and Discovery Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationHerd immunityPandemicMedicinePublic healthPopulationCoronavirus disease 2019 (COVID-19)Environmental healthDiseaseFamily medicineImmunologyInfectious disease (medical specialty)Nursing

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has prompted the urgent development and distribution of novel vaccinations to reduce the global disease burden and establish herd immunity. Vaccination is a cost-effective public health measure that is critical for disease prevention; as of March 2022, Health Canada has authorized the distribution of the Pfizer-BioNTech and Moderna mRNA COVID-19 vaccines in pediatric populations. However, vaccine hesitancy among caregivers remains a significant barrier to vaccine uptake in the pediatric population. Increased research on the intentions, motivations, and perceptions of pediatric COVID-19 vaccine efficacy and safety may facilitate the development of public health strategies to address pediatric vaccine knowledge translation, accessibility, and administration barriers. This perspective paper aims to explore the major barrier of vaccine hesitancy and potential solutions to achieve effective vaccine uptake in the Canadian pediatric population.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.734

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.329
Teacher spread0.309 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Child Health Interdisciplinary Literature and Discovery JournalSame topicVaccine Coverage and HesitancyFrench-language works237,207