For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
44
views
59
references
 Top references
cited by
157
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
1 collections
    0
    shares
      1,684
      similar
       All similar
      • Record: found
      • Abstract: not found
      • Article: not found

      Trust in a COVID-19 vaccine in the U.S.: A social-ecological perspective.

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Highlights • Low COVID-19 vaccine trust associated with vaccines distributed too soon. • Social norms were strongly associated with COVID-19 vaccine trust. • High trustworthiness in CDC as for information was linked to vaccine trust. • Females expressed lower vaccine trust than males.

          Related collections

          Most cited references59

          • Record: found
          • Abstract: found
          • Article: not found
          Is Open Access

          Vaccine hesitancy: Definition, scope and determinants.

          Noni MacDonald (2015)
          The SAGE Working Group on Vaccine Hesitancy concluded that vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services. Vaccine hesitancy is complex and context specific, varying across time, place and vaccines. It is influenced by factors such as complacency, convenience and confidence. The Working Group retained the term 'vaccine' rather than 'vaccination' hesitancy, although the latter more correctly implies the broader range of immunization concerns, as vaccine hesitancy is the more commonly used term. While high levels of hesitancy lead to low vaccine demand, low levels of hesitancy do not necessarily mean high vaccine demand. The Vaccine Hesitancy Determinants Matrix displays the factors influencing the behavioral decision to accept, delay or reject some or all vaccines under three categories: contextual, individual and group, and vaccine/vaccination-specific influences.
          Article 0 comments Cited 1916 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A global survey of potential acceptance of a COVID-19 vaccine

            Jeffrey V Lazarus, Scott C Ratzan, Adam Palayew … (2020)
            Several coronavirus disease 2019 (COVID-19) vaccines are currently in human trials. In June 2020, we surveyed 13,426 people in 19 countries to determine potential acceptance rates and factors influencing acceptance of a COVID-19 vaccine. Of these, 71.5% of participants reported that they would be very or somewhat likely to take a COVID-19 vaccine, and 61.4% reported that they would accept their employer’s recommendation to do so. Differences in acceptance rates ranged from almost 90% (in China) to less than 55% (in Russia). Respondents reporting higher levels of trust in information from government sources were more likely to accept a vaccine and take their employer’s advice to do so.
            Article 0 comments Cited 1278 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Purposeful selection of variables in logistic regression

              Zoran Bursac, C Gauss, David Williams … (2008)
              Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
              Article 0 comments Cited 790 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (iso-abbrev): Soc Sci Med
                Title: Social science & medicine (1982)
                Publisher: Elsevier BV
                ISSN (Electronic): 1873-5347
                ISSN (Print): 0277-9536
                Publication date (Electronic): February 2021
                Volume: 270
                Affiliations
                [1 ] Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, USA; Division of Infectious Diseases, Johns Hopkins University School of Medicine, USA. Electronic address: carl.latkin@jhu.edu.
                [2 ] Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, USA.
                [3 ] Krieger School of Arts & Sciences, Johns Hopkins University, USA.
                [4 ] Division of Community Health Sciences, University of Illinois at Chicago School of Public Health, Chicago, IL, USA.
                Article
                Publisher Item ID: S0277-9536(21)00016-2 Mid ID: NIHMS1661238
                DOI: 10.1016/j.socscimed.2021.113684
                PMC ID: 7834519
                PubMed ID: 33485008
                SO-VID: edaffe02-3e81-407b-8a17-f60997db39ed
                History

                Data availability:

                Comments

                Comment on this article

                scite_

                Similar content1,684

                See all similar

                Cited by157

                See all cited by

                Most referenced authors482

                See all reference authors