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Record W3204523936 · doi:10.3390/vaccines9101138

COVID-19 Vaccine Concerns about Safety, Effectiveness, and Policies in the United States, Canada, Sweden, and Italy among Unvaccinated Individuals

2021· article· en· W3204523936 on OpenAlexaboutno aff
Rachael Piltch‐Loeb, Nigel Walsh Harriman, Julia Healey, Marco Bonetti, Veronica Toffolutti, Marcia A. Testa, Max Su, Elena Savoia

Bibliographic record

VenueVaccines · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
FundersNorth Atlantic Treaty OrganizationU.S. Department of Homeland Security
KeywordsVaccinationGovernment (linguistics)PopulationCoronavirus disease 2019 (COVID-19)Public healthEnvironmental healthPolitical scienceMedicineGeographyVirologyDisease

Abstract

fetched live from OpenAlex

Despite the effectiveness of the COVID-19 vaccine, global vaccination distribution efforts have thus far had varying levels of success. Vaccine hesitancy remains a threat to vaccine uptake. This study has four objectives: (1) describe and compare vaccine hesitancy proportions by country; (2) categorize vaccine-related concerns; (3) rank vaccine-related concerns; and (4) compare vaccine-related concerns by country and hesitancy status in four countries-the United States, Canada, Sweden, and Italy. Using the Pollfish survey platform, we sampled 1000 respondents in Canada, Sweden, and Italy and 750 respondents in the United States between 21-28 May 2021. Results showed vaccine-related concerns varied across three topical areas-vaccine safety and government control, vaccine effectiveness and population control, and freedom. For each thematic area, the top concern was statistically significantly different in each country and among the hesitant and non-hesitant subsamples within each county. Concerns related to freedom were the most universal. Understanding the specific concerns among individuals when it comes to the COVID-19 vaccine can help to inform public communications and identify which, if any, salient narratives are global.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.118
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.310
Teacher spread0.289 · 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.

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

Citations31
Published2021
Admission routes1
Has abstractyes

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