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Record W4386258032 · doi:10.1016/j.anai.2023.08.600

A qualitative investigation into vaccine hesitancy and confidence among people managing allergy

2023· article· en· W4386258032 on OpenAlexafffundabout
Ayel Luis R. Batac, Kaitlyn A. Merrill, Michael A. Golding, Elissa M. Abrams, Philippe Bégin, Moshe Ben‐Shoshan, Erika Ladouceur, Leslie E. Roos, Vladan Protudjer, Jennifer L. P. Protudjer

Bibliographic record

VenueAnnals of Allergy Asthma & Immunology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsGeorge & Fay Yee Centre for Healthcare InnovationMcGill University Health CentreMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversity of British ColumbiaCentre Hospitalier Universitaire Sainte-JustineMontreal Children's HospitalUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersCanadian Institutes of Health ResearchAimmune TherapeuticsSanofi
KeywordsMedicineFamily medicineAllergyConfidence intervalImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Vaccine hesitancy has a multifactorial etiology, and, among atopic individuals, is exacerbated by documented, but rare, cases of allergic reactions to COVID-19 vaccines.1–3 Furthermore, conflicting information regarding the safety of COVID-19 vaccines may have caused confusion among individuals with history of severe allergies. We performed semi-structured qualitative interviews to better understand how some adults and families managing allergy perceive vaccine messaging, and what influences their decisions to be vaccinated.

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)0.023
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0120.015
Scholarly communication0.0060.006
Open science0.0030.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.355
Teacher spread0.311 · 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 designQualitative
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

Citations1
Published2023
Admission routes3
Has abstractyes

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