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Record W4386237544 · doi:10.32598/jrh.13.5.2163.1

The Associations Between Sources of Vaccine News and the Intention to Change Adherence to COVID-19 Preventive Health Measures

2023· article· en· W4386237544 on OpenAlexaff
Zahra Ganji, Amirmohammad Ahmadzadeh, Shahabeddin Abhari, Maysam Rezapour

Bibliographic record

VenueJournal of Research and Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Waterloo
FundersMazandaran University of Medical Sciences
KeywordsCoronavirus disease 2019 (COVID-19)Social mediaMass mediaAssociation (psychology)PopulationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthCross-sectional studyPsychologyMedicineComputer-assisted web interviewing2019-20 coronavirus outbreakFamily medicineAdvertisingEnvironmental healthBusinessNursingComputer scienceVirologyWorld Wide WebDisease

Abstract

fetched live from OpenAlex

Background: Although the scientific literature has extensively discussed the impact of the media on people’s health-related behaviors, there is little evidence of the effect of different sources of COVID-19 vaccine news on changing the intention to adhere to health protocols. Therefore, the present study was conducted to investigate the sources of news on the COVID-19 vaccine and the association of each of these sources with the intention of changing adherence to COVID-19 preventive health measures (ICA-COVID-19-PHM). Methods: This cross-sectional study was conducted on 1000 public population (67.4% female and 32.6% male) of Mazandaran province (age range: 18 to 60 years). The data were collected between January and April 2021 by completing an unidentified online “Google form” questionnaire via social media platforms, such as Telegram, WhatsApp, Soroush, ETA, and Instagram. The two-level linear regression analysis was used to examine the association between the sources of news on the COVID-19 vaccine and the ICA-COVID-19-PHM (social and personal aspects). Results: The most common sources of receiving news were mass media (radio and television with 54%) and virtual networks with 49%. The results showed the ICA-COVID-19-PHM for the news source via virtual workgroups was positive (B=1.36; 95% CI, 0.31%, 2.41%; P=0.01) and for the news sources via virtual networks (B=-0.83; 95% CI, -1.62%, -0.05%; P=0.04) and satellites and foreign news agencies (B=-1.50; 95% CI, -2.64%, -0.36%; P=0.01) was negative. While, the ICA-COVID-19-PHM was not significant (P>0.05) for other sources, such as groups of friends and neighbors, newspapers and magazines, radio and television, and news websites. Conclusion: The management of news sources in epidemics is important because they have associations with adherence to preventive health measures. Policymakers should consider the distribution of vaccine news sources on the ICA-COVID-19-PHM.

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)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.361
GPT teacher head0.533
Teacher spread0.172 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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