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Record W3135734946 · doi:10.47326/ocsat.2021.02.12.1.0

Behavioural Science Principles for Supporting COVID-19 Vaccine Confidence and Uptake Among Ontario Health Care Workers

2021· report· en· W3135734946 on OpenAlexaboutno aff
Justin Presseau, Laura Desveaux, Upton Allen, Trevor Arnason, Judy L. Buchan, Kimberly Corace, Vinita Dubey, Gerald A. Evans, Leandre R. Fabrigar, Jeremy Grimshaw, Anne Hayes, J. Shelby House, Douglas G. Manuel, Robert J. Reid, Robert M. Steiner, Ashini Weerasinghe, Brian Schwartz

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicWork (physics)Health careEthnic groupVaccination2019-20 coronavirus outbreakSet (abstract data type)MedicinePublic healthPersonal protective equipmentBusinessNursingFamily medicinePublic relationsEnvironmental healthPsychologyPolitical scienceVirologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Health Care Workers (HCWs) are the backbone of Ontario’s COVID-19 pandemic response and are a key vaccination priority group. About 80% of Ontario HCWs intend to receive COVID-19 vaccine.1 Challenges include the logistics of delivering the vaccine to this mobile and diverse group and improving vaccine confidence in the remaining 20%. These challenges can be overcome by allaying safety concerns and highlighting personal benefits; tailoring messages to factors associated with lower intention (e.g. age, gender, ethnicity and work setting); employing trusted leaders to set the tone and peers to build social norms; and leveraging public health organizations and health institutions as existing channels of influence.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.413
Teacher spread0.290 · 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.

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

Citations11
Published2021
Admission routes1
Has abstractyes

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