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Record 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 on OpenAlexaff
Allyson Gallant, Andrew Harding, Catie Johnson, Audrey Steenbeek, Janet Curran

Bibliographic record

VenueJBI Evidence Synthesis · 2023
Typereview
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsIzaak Walton Killam Health CentreNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineVirologyFamily medicineOutbreakInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from 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 Published and unpublished literature were eligible for inclusion.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.

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)0.005
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.147
GPT teacher head0.472
Teacher spread0.324 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2023
Admission routes1
Has abstractyes

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