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Vaccination, Values, and Health Information Search Behaviour in the New Folk Medicine

2022· article· en· W4386226046 on OpenAlexaffabout
Jordan Richard Schoenherr

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCarleton University
Fundersnot available
KeywordsConformityVaccinationConservatismPandemicSocial mediaUncertainty avoidanceCoronavirus disease 2019 (COVID-19)The InternetCultural diversityPsychologyInternet privacySocial psychologyEnvironmental healthMedicinePolitical scienceComputer scienceVirologyPoliticsWorld Wide WebLaw

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has caused rapid changes within our societies. Nowhere is this more evident than health information seeking behaviour (HISB) such that medical and governmental websites, general search engines, and social media platforms were relied upon to retrieve credible information. Using state-level measures of conformity bias (cultural tightness) and conservatism in the US and a province-level measure of conservativism in Canada, the present study considers the relationship between cultural tightness, conservativism, and vaccination rates. The analyses demonstrate that cultural tightness and conservatism were associated with lower levels of vaccination and a reduced rate of vaccination. Replicating previous studies, this study also represents a correlation between internet HISB and consequential health behaviours, in this case, changes in vaccination rate and regional mobility.

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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.362
Teacher spread0.325 · 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 routes2
Has abstractyes

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