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Record W3136157989 · doi:10.1136/bmjopen-2021-qhrn.34

34 COVID-19, vaccination, and trust: an interview study

2021· article· en· W3136157989 on OpenAlexaffabout
Jenna Vikse, Vivian Nelson, Kieran C. O’Doherty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPandemicContext (archaeology)Coronavirus disease 2019 (COVID-19)BusinessPublic relationsSoftware deploymentVaccinationPublic healthHealth carePolitical scienceEnvironmental healthMedicineEconomic growthVirologyEconomicsNursingComputer scienceGeography

Abstract

fetched live from OpenAlex

In addition to direct health threats of the COVID-19 pandemic, societies are experiencing significant harms and burdens associated with measures to mitigate the effects of the virus. In this context, a possible vaccine is perhaps the most highly regarded prospect to combat the novel coronavirus and enable societies to lift COVID related restrictions. Governments and other institutions around the world have invested large amounts of resources into the development, testing, and production capacity for several different vaccines. When vaccines become available, public health authorities will need information about the concerns and decision-making considerations of constituents. We outline here the key findings from interviews with residents of Ontario, Canada, (n=40) in July and August of 2020 regarding their views, concerns, and intentions with respect to a prospective COVID-19 vaccine. In particular, participants expressed concern about the safety and efficacy of any prospective vaccine that is developed in a short timeframe, despite eagerness to eventually take it. Additionally, participants expressed considerations that, while not directly related to vaccines, nevertheless factored into their attitudes about accepting a possible COVID vaccination. These included how successful governments have managed COVID-19 so far, existing relationships with healthcare providers, and how they have assessed their risk of contracting or becoming very ill from COVID-19. Trust in science, regulators, and governments will play a critical role in the successful deployment of a COVID-19 vaccine. Governments and public health institutions can take actions to earn trust. Implementing monitoring programs for long-term adverse effects would measure and potentially mitigate risk of unforeseen effects. Supports that provide financial and social stability during the wait for rigorously tested vaccines may increase trust in governments to act in the best interest of their constituents.

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)0.013
metaresearch head score (Gemma)0.025
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 score0.168
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0130.008
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.070
GPT teacher head0.388
Teacher spread0.318 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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