‘Anti-Vaxxers’ and Vaccine Citizenship: Insights for Medical Education

Fisayo, Temitope

doi:10.1007/s40670-020-01197-4

Cited by 2 publications

(1 citation statement)

References 32 publications

(38 reference statements)

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“…At present, an increasing number of anti-vaccination groups generally consider vaccination as risky and unessential [ 8 ]. Individuals who completely oppose vaccination are known as “ anti -vax” or “ anti -vaxxers.” However, not all vaccine-hesitant people are “ anti -vaxxers” [ 9 ]. In fact, vaccine hesitancy exists on a spectrum ranging from complete acceptance to complete rejection, with hesitant people falling somewhere in the middle of this range.…”

Section: Introductionmentioning

confidence: 99%

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy

Alamoodi

Zaidan

Al-Masawa

et al. 2021

Computers in Biology and Medicine

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A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline ( i.e., social, medical, and technological ). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: ( 1 ) social, ( 2 ) medical, public health, and ( 3 ) technology sciences. Therefore, this research aimed to review and analyze articles related different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science , Scopus , PubMed , IEEEXplore , ScienceDirect , and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.

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Section: Introductionmentioning

confidence: 99%