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Record W4200611258 · doi:10.1080/13548506.2021.2014911

Anti-vaccination attitudes are associated with less analytical and more intuitive reasoning

2021· article· en· W4200611258 on OpenAlexafffund
Fernando Caravaggio, Natasha Porco, Julia Kim, Gagan Fervaha, Ariel Graff‐Guerrero, Philip Gerretsen

Bibliographic record

VenuePsychology Health & Medicine · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersCampbell Family Mental Health Research InstituteCentre for Addiction and Mental Health Foundation
KeywordsReligiosityVaccinationPublic health interventionsCognitionPsychological interventionConfoundingPsychologyPublic healthSocial psychologyMedicineImmunologyPsychiatry

Abstract

fetched live from OpenAlex

Online anti-vaccination rhetoric has produced far reaching negative health consequences. Persons who endorse anti-vaccination attitudes may employ less analytical reasoning when problem solving. Considering limitations in previous research, we used an online web-based survey (n = 760; mean age = 47.69; 388 males, 372 females) to address this question. Analytical reasoning was negatively correlated with anti-vaccination attitudes (r = −.18, p < .0001). This relationship remained significant after statistically controlling for potential confounders, including age, sex, education, and religiosity (r = −.16, p < .0001). We hope that elucidating the cognitive, non-information-based aspects of anti-vaccination attitudes will help to guide effective educational interventions aimed at improving public health in the future.

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)0.022
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.058
GPT teacher head0.439
Teacher spread0.380 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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