The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Trust in government, intention to vaccinate and COVID-19 vaccine hesitancy: A comparative survey of five large cities in the United States, United Kingdom, and Australia
Open accessAbstract
BACKGROUND: There is widespread hesitancy towards COVID-19 vaccines in the United States, United Kingdom, and Australia. OBJECTIVE: To identify predictors of willingness to vaccinate against COVID-19 in five cities with varying COVID-19 incidence in the US, UK, and Australia. DESIGN: Online, cross-sectional survey of adults from Dynata's research panel in July-September 2020. PARTICIPANTS, SETTING: Adults aged 18 and over in Sydney, Melbourne, London, New York City, or Phoenix. MAIN OUTCOMES AND MEASURES: Willingness to receive a COVID-19 vaccine; reason for vaccine intention. STATISTICAL METHODS: To identify predictors of intention to receive a COVID-19 vaccine, we used Poisson regression with robust error estimation to produce prevalence ratios. RESULTS: The proportion willing to receive a COVID-19 vaccine was 70% in London, 71% NYC, 72% in Sydney, 76% in Phoenix, and 78% in Melbourne. Age was the only sociodemographic characteristic that predicted willingness to receive a COVID-19 vaccine in all five cities. In Sydney and Melbourne, participants with high confidence in their current government had greater willingness to receive the vaccine (PR = 1.24; 95% CI = 1.07-1.44 and PR = 1.38; 95% CI = 1.74-1.62), while participants with high confidence in their current government in NYC and Phoenix were less likely to be willing to receive the vaccine (PR = 0.78; 95% CI = 0.72-0.85 and PR = 0.85; 95% CI = 0.76-0.96). LIMITATIONS: Consumer panels can be subject to bias and may not be representative of the general population. CONCLUSIONS: Success for COVID-19 vaccination programs requires high levels of vaccine acceptance. Our data suggests more than 25% of adults may not be willing to receive a COVID-19 vaccine, but many of them were not explicitly anti-vaccination and thus may become more willing to vaccinate over time. Among the three countries surveyed, there appears to be cultural differences, political influences, and differing experiences with COVID-19 that may affect willingness to receive a COVID-19 vaccine.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Barros, A. J., & Hirakata, V. N. (2003) · BMC Medical Research Methodology · doi:10.1186/1471-2288-3-21 · Cited by 4,140
A global survey of potential acceptance of a COVID-19 vaccine
Lazarus, J. V., Ratzan, S. C., Palayew, A., Gostin, L. O., Larson, H. J., Rabin, K., Kimball, S., & El-Mohandes, A. (2021) · Nature Medicine · doi:10.1038/s41591-020-1124-9 · Cited by 2,904
Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period
Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020) · Science · doi:10.1126/science.abb5793 · Cited by 2,611
Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars.
Maguire, M., & Delahunt, B. (2017) · All Ireland Journal of Higher Education · doi:10.62707/aishej.v9i3.335 · Cited by 2,099
Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis
Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., et al. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.04.021 · Cited by 2,074
Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01056-1 · Cited by 1,800
Determinants of COVID-19 vaccine acceptance in the US
Malik, A. A., McFadden, S. M., Elharake, J., & Omer, S. B. (2020) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100495 · Cited by 1,540
Doing a Thematic Analysis: A Practical, Step-by-Step Guide for Learning and Teaching Scholars
Delahunt, M. A. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5828312 · Cited by 1,354
The Effects of Anti-Vaccine Conspiracy Theories on Vaccination Intentions
Jolley, D., & Douglas, K. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089177 · Cited by 1,232
Schmid, P., Rauber, D., Betsch, C., Lidolt, G., & Denker, M.-L. (2017) · PLOS ONE · doi:10.1371/journal.pone.0170550 · Cited by 1,228
Cited by
Appealing to Americans’ altruism is not enough to nudge them to accept novel vaccines
Drabo, E. F., Kelley, M. A., & Gong, C. L. (2024) · Journal of Public Health · doi:10.1093/pubmed/fdae048 · Cited by 3
Metadata
- Authors
- Trent, M., Seale, H., Chughtai, A. A., Salmon, D., & MacIntyre, C. R.
- Venue
- Vaccine
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 40
- Issue
- 17
- Pages
- 2498-2505
- ISSN
- 0264-410X (ISSN-L)
- OpenAlex
- W3176235170
- Cited by
- 257
- References
- 29
- FWCI
- 27.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Trust in government, intention to vaccinate and COVID-19 vaccine hesitancy: A comparative survey of five large cities in the United States, United Kingdom, and Australia. https://app.cochat.ai/papers/10.1016/j.vaccine.2021.06.048
- The paper
- Trent, M., Seale, H., Chughtai, A. A., Salmon, D., & MacIntyre, C. R. (2022). Trust in government, intention to vaccinate and COVID-19 vaccine hesitancy: A comparative survey of five large cities in the United States, United Kingdom, and Australia. Vaccine. https://doi.org/10.1016/j.vaccine.2021.06.048
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.