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Record W2765975205 · doi:10.1002/pra2.2017.14505401118

Health information assessment by vaccine hesitant parents

2017· article· en· W2765975205 on OpenAlexaffabout
Devon Greyson, Julie A. Bettinger

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsPublic healthVaccinationHealth informationPopulationHealth communicationPsychologyEnvironmental healthMedicineSocial psychologyPublic relationsNursingPolitical scienceHealth careImmunology

Abstract

fetched live from OpenAlex

ABSTRACT Vaccines are widely considered to be one of the greatest successes of public health, but vaccine hesitancy has been internationally recognized as a health threat. Efforts to use information to increase vaccine acceptance have largely been unsuccessful, highlighting the need to better understand the information practices, values and experiences of hesitant populations. This constructivist grounded theory study explored health information experiences of 23 mothers in the Greater Vancouver region of Canada who had changed their minds about vaccines, in order to understand the ways information may have influenced their vaccination beliefs and practices. While health information sources were similar across the study population, assessment of vaccine‐related information varied a great deal. In particular, mothers reported difficulty triangulating and assessing information during periods of affective stress, such as following their first birth or around a child's illness. Helping mothers triangulate information among trusted sources may help build vaccine confidence.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0010.009
Open science0.0010.000
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 head0.010
GPT teacher head0.313
Teacher spread0.302 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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