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Enregistrement W4324128567 · doi:10.11124/jbies-22-00112

Identifying H1N1 and COVID-19 vaccine hesitancy or refusal among health care providers: a scoping review

2023· review· en· W4324128567 sur OpenAlexaff
Allyson Gallant, Andrew Harding, Catie Johnson, Audrey Steenbeek, Janet Curran

Notice bibliographique

RevueJBI Evidence Synthesis · 2023
Typereview
Langueen
DomaineSocial Sciences
ThématiqueVaccine Coverage and Hesitancy
Établissements canadiensIzaak Walton Killam Health CentreNova Scotia Health AuthorityDalhousie University
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineVirologyFamily medicineOutbreakInfectious disease (medical specialty)Internal medicine

Résumé

récupéré en direct d'OpenAlex

OBJECTIVES: The objective of this review was to describe and map the evidence on COVID-19 and H1N1 vaccine hesitancy or refusal by physicians, nurses, and pharmacists in North America, the United Kingdom and the European Union, and Australia. INTRODUCTION: Since 2009, we have experienced two pandemics: H1N1 "swine flu" and COVID-19. While severity and transmissibility of these viruses varied, vaccination has been a critical component of bringing both pandemics under control. However, uptake of these vaccines has been affected by vaccine hesitancy and refusal. The vaccination behaviors of health care providers, including physicians, nurses, and pharmacists, are of particular interest as they have been priority populations to receive both H1N1 and COVID-19 vaccinations. Their vaccination views could affect the vaccination decisions of their patients. INCLUSION CRITERIA: Studies were eligible for inclusion if they identified reasons for COVID-19 or H1N1 vaccine hesitancy or refusal among physicians, nurses, or pharmacists from the included countries. Published and unpublished literature were eligible for inclusion. Previous reviews were excluded; however, the reference lists of relevant reviews were searched to identify additional studies for inclusion. METHODS: A search of CINAHL, MEDLINE, PsycINFO, and Academic Search Premier databases was conducted April 28, 2021, to identify English-language literature published from 2009 to 2021. Gray literature and citation screening were also conducted to identify additional relevant literature. Titles, abstracts, and eligible full-text articles were reviewed in duplicate by 2 trained reviewers. Data were extracted in duplicate using a structured extraction tool developed for the review. Conflicts were resolved through discussion or with a third team member. Data were synthesized using narrative and tabular summaries. RESULTS: In total, 83 articles were included in the review. Studies were conducted primarily across the United States, the United Kingdom, and France. The majority of articles (n=70) used cross-sectional designs to examine knowledge, attitudes, and uptake of H1N1 (n=61) or COVID-19 (n=22) vaccines. Physicians, medical students, nurses, and nursing students were common participants in the studies; however, only 8 studies included pharmacists in their sample. Across health care settings, most studies were conducted in urban, academic teaching hospitals, with 1 study conducted in a rural hospital setting. Concerns about vaccine safety, vaccine side effects, and perceived low risk of contracting H1N1 or COVID-19 were the most common reasons for vaccine hesitancy or refusal across both vaccines. CONCLUSIONS: With increased interest and attention on vaccines in recent years, intensified by the COVID-19 pandemic, more research that examines vaccine hesitancy or refusal across different health care settings and health care providers is warranted. Future work should aim to utilize more qualitative and mixed methods research designs to capture the personal perspectives of vaccine hesitancy and refusal, and consider collecting data beyond the common urban and academic health care settings identified in this review.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,034
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,442
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,034
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0020,000
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,147
Tête enseignante GPT0,472
Écart entre enseignants0,324 · 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 tête enseignante, pas un consensus.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

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

En bref

Citations12
Publié2023
Routes d'admission1
Résumé présentoui

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