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Enregistrement W7070439279

Public Perceptions of COVID-19 Vaccine Information: Quantitative Analyses of Survey and Twitter Data in the United States

2022· other· en· W7070439279 sur OpenAlexaboutno aff

Notice bibliographique

RevueeScholarship (California Digital Library) · 2022
Typeother
Langueen
DomaineSocial Sciences
ThématiqueVaccine Coverage and Hesitancy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMetropolitan areaPerceptionPopulationGeocodingPhonePublic opinionPublic healthSurvey data collectionSurvey methodology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

ABSTRACTObjectives: We aimed to understand the public perception of COVID-19 vaccines using survey and Twitter data. For the survey study, we focused on examining the COVID-19 vaccine perspectives of the rural population in the Central Valley of California, which was predominantly Latinx. Specifically, we looked at the level of trust in the source and content of the vaccine information they received, their view of the safety and effectiveness of vaccines, and their accessibility to vaccines and information at the time when vaccines were readily available to the public. For the Twitter study, we focused on metropolitan and nonmetropolitan communities in the United States and examined tweet sentiment and emotion scores in the early stage of the pandemic and through the public release of the first COVID-19 vaccines. Methods: For the survey data, a total of 900 survey responses were collected in two rural counties in the State of California from March 30 to April 25, 2021. The survey was offered via web and phone in English, Spanish, Punjabi, and Hmong. The respondents were asked about their perceptions of COVID-19 vaccines, messaging, and sources of information. For the Twitter data, we used 127,648 tweets for the analysis after data cleaning, reverse geocoding of tweets, and assigning geographical designations to compare public perception between metropolitan and nonmetropolitan areas. We quantified public perception using the VADER (Valence Aware Dictionary for Sentiment Reasoning) lexicon to calculate sentiment scores and the NRCLex (National Research Council Canada Lexicon) to calculate emotions scores for the tweets. Next, we explored patterns in public perception of COVID-19 vaccines from March 11, 2020, to September 12, 2021. Then, we compared public perception between two separate periods (i.e., before and after December 11, 2020, when the Food and Drug Administration issued an emergency use authorization of Pfizer, the first COVID-19 vaccine). Results: In the survey approach, 41% of respondents were Latinx. The most frequent concerns noted for COVID-19 vaccine hesitancy were lack of confidence in the vaccine and the state and federal government (46-56%). However, complacency about the seriousness of the COVID-19 vaccines and disease (35%) and convenience or issues in access, travel time, and cost of vaccines (20%) were not associated with decisions regarding COVID-19 vaccination. In the Twitter approach, we found that public sentiment and emotion varied by geography though our findings did not significantly differ for metropolitan and nonmetropolitan residents. Fear was prevalent in the early times when COVID-19 was announced as a pandemic. However, this was quickly taken over by the emotion of trust later as the breakthroughs in COVID-19 vaccines were announced. Specifically, trust peaked on November 9, 2020, when Pfizer announced its vaccine was 90% effective. Then, around December 11, 2020, positive and negative tweet sentiments started diverging more clearly than the extreme sentiment fluctuations before this period.Conclusions: For urban or rural and metropolitan or nonmetropolitan communities, news and social media are potent outlets for health information and can significantly change the public’s perceptions about COVID-19 vaccines. The survey data shows rural residents in the Central Valley of California, predominantly Latinx, have high confidence or trust in healthcare providers, and the county public health department. However, approximately 40% of these rural residents were still unlikely to get vaccinated, similar to rural populations throughout the country. Recommendations to combat COVID-19 vaccine hesitancy amongst Latinx rural residents include leveraging trusted sources such as local doctors, family/friends, and local public health departments to encourage vaccination amongst this population. In addition, Twitter data shows that announcements from the public media or private institutions appear associated with the public’s perception of COVID-19 vaccines, such as the first news of the effectiveness of the Pfizer COVID-19 vaccine or the notice of blood clot issues caused by the Johnson & Johnson COVID-19 vaccine. Also, there was no significant difference in the mean sentiment or emotion scores between geographical distributions from March 2020 to September 2021. Overall, COVID-19 vaccine news appears to penetrate the public whether people are in urban or rural and metropolitan or nonmetropolitan communities.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,029

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,023
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0000,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,141
Tête enseignante GPT0,354
Écart entre enseignants0,213 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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