Identifying H1N1 and COVID-19 vaccine hesitancy or refusal among health care providers: a scoping review
Notice bibliographique
Résumé
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 Published and unpublished literature were eligible for inclusion.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 machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,108 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,018 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».