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Record W4382073086 · doi:10.1080/15358593.2022.2123251

“There was a lot of that [coercion and manipulation] happening and well, that’s not very trustworthy”: a qualitative study on COVID-19 vaccine hesitancy in Canada

2023· article· en· W4382073086 on OpenAlexaffabout
Melissa MacKay, Abhinand Thaivalappil, Jennifer E. McWhirter, Daniel Gillis, Andrew Papadopoulos

Bibliographic record

VenueReview of Communication · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDistrustThematic analysisPublic relationsGovernment (linguistics)TrustworthinessQualitative researchPublic healthPerceptionCrisis communicationPopulationPublic trustCoercion (linguistics)Stigma (botany)Political sciencePsychologySocial psychologyMedicineNursingSociologyEnvironmental healthLawPsychiatry

Abstract

fetched live from OpenAlex

Although a large proportion of the Canadian population is fully vaccinated against COVID-19, millions of eligible individuals remain unvaccinated. Trust in public health and government impacts the effectiveness of crisis communication and the public’s willingness to follow health recommendations. This qualitative study involved semistructured interviews with 12 COVID-19 vaccine-hesitant adults in Canada. Using thematic analysis, four themes were generated, including (1) perceived low use of crisis communication guiding principles contributes to distrust in officials; (2) risk perception and decisions are influenced by a range of sources; (3) concerns regarding vaccine safety, the industry, and politicization of efforts are impacting trust; and (4) stigma around vaccine status further entrenches views and erodes trust. This study highlights the importance of trust and how vaccine hesitancy is fueled by perceived ineffective crisis communication by officials.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.389
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.113
GPT teacher head0.401
Teacher spread0.287 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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