The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index; 1 of the 11 sources its claims cite were checked for support.
A global survey of potential acceptance of a COVID-19 vaccine
Open accessAbstract
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 48.1% 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. Survey data from across 19 countries reveal heterogeneity in attitudes toward acceptance of a COVID-19 vaccine and suggest that trust in government is associated with vaccine confidence.
Overview
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Written from the paper’s full text.
TL;DR
In June 2020, a 19-country survey of 13,426 adults found that 71.5% would be very or somewhat likely to take a COVID-19 vaccine, but only 48.1% would accept their employer’s recommendation to do so. Acceptance varied widely by country and was strongly associated with trust in government.
Key findings
- Overall, 71.5% reported they would take a proven safe and effective COVID-19 vaccine, and 48.1% said they would accept an employer recommendation to get vaccinated.
- Country variation was large: China had the highest positive response for the general vaccine question (631/712, 88.6%) and Russia the lowest (373/680, 54.9%); Poland had the highest negative response (182/666, 27.3%). For employer recommendation, China had 83.7% positive responses (596/712), while Russia had the lowest willingness (184/680, 27.1%) and highest negative response (278/680, 40.9%).
- Trust in government was associated with acceptance: OR = 1.67 (95% CI 1.54, 1.80) for the general vaccine question and OR = 4.35 (95% CI 4.01, 4.72) for the employer-recommendation question.
- Older age and higher income/education were positively associated with acceptance. For example, respondents aged 65+ vs 18–24 had OR = 1.73 (95% CI 1.48, 2.02) for the general question, and those earning >$32/day vs <$2/day had OR = 2.18 (95% CI 1.79, 2.64). Men were slightly less likely than women to respond positively (OR = 0.84; 95% CI 0.78, 0.91), and self/family COVID-19 sickness was not associated with acceptance (OR = 0.97; 95% CI 0.87, 1.08).
Methods
- The study analyzed two vaccine-acceptance questions from the COVID-SCORE online survey, collected June 16–20, 2020, from 13,426 respondents aged 18+ in 19 countries, with 619–773 participants per country.
- Participants were recruited through multiple international online panel providers; sampling was random and described elsewhere.
- Responses used a five-point Likert scale. Logistic regressions coded “completely agree” or “somewhat agree” as 1 and all other responses as 0, with univariate models against age, gender, income, education, COVID-19 sickness in self/family, country cases and mortality per million, and trust in government.
Limitations
- The paper states that public surveys are snapshots at a point in time, the survey occurred in a highly dynamic COVID-19 landscape, and reported willingness may not predict actual acceptance. It also notes that events after the survey may have increased hesitancy, and that employment status was not investigated for the age/employer-recommendation finding.
- The text makes evident that the regressions are univariate rather than multivariable-adjusted, so associations may not be independent.
- The online panel design may limit representativeness despite random sampling, and the outcomes are self-reported intentions rather than observed vaccination behavior.
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Claims the paper makes
23 Own result · 8 Not checked yet · 6 Own method · 4 Own interpretation · 1 Partly supported · 1 No basis given · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In many countries, vaccine hesitancy and misinformation present substantial obstacles to achieving coverage and community immunity5,6.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Main
What the sources say
- Not checked yetcites [5] Larson, H. J., Jarrett, C., Eckersberger, E., Smith, D. M. D. & Paterson, P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine 32, 2150–2159 (2014). source
- Partly supportedcites [6] Lane, S., MacDonald, N. E., Marti, M. & Dumolard, L. Vaccine hesitancy around the globe: analysis of three years of WHO/UNICEF Joint Reporting Form data—2015–2017. Vaccine 36, 3861–3867 (2018). sourceExperimental — accuracy not yet measured
The Discussion reports vaccine hesitancy in the majority of countries and describes efforts to improve vaccine acceptance. That supports the claim that hesitancy is a widespread obstacle to vaccination, but our copy does not establish misinformation as a separate obstacle or assess effects on coverage or community immunity. (LLM, conf=0.83)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Respondents who said that they trusted their government were more likely to accept a vaccine than those who said that they did not (OR = 1.67; 95% CI (1.54, 1.80)).”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Gender differences were small, but the univariate association for both questions suggested that men were slightly less likely to respond positively than women, with an OR of 0.84 (95% CI (0.78, 0.91)) of men responding positively relative to women for the general question”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Differences in acceptance rates ranged from almost 90% (in China) to less than 55% (in Russia).”
§ A global survey of potential acceptance of a COVID-19 vaccine > Abstract
plainn = 13,426- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This difference was strongest (odds ratio (OR) = 1.73; 95% confidence interval (CI) (1.48, 2.02)) when responses from the oldest age cohort and those from the youngest age cohort were compared (Table 2).”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Respondents from Poland reported the highest proportion of negative responses (182 of 666, 27.3%)”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and 48.1% reported that they would accept their employer’s recommendation to do so.”
- Also stated in: § Discussion · hedged
“and 48.1% said that they would get vaccinated if their employer recommended it”
§ A global survey of potential acceptance of a COVID-19 vaccine > Abstract
hedgedn = 13,426- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Discussion · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we observed high heterogeneity in responses between countries.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Discussion
plainn = 13,426- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“People aged 25–54, 55–64 and 65+ were more likely to accept the vaccine than those who were aged 18–24.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and an OR of 0.93 (95% CI (0.87, 1.00)) for the business-related question.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 7
- The quote only partly states this claim. 1
- Probably not a substantial claim. 11
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When asked whether “You would accept a vaccine if it were recommended by your employer and was approved safe and effective by the government,” 14.0% (1,881 of 13,426) completely agreed, whereas 25.9% (3,478 of 13,426) somewhat or completely disagreed (Table 1).”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Respondents from China gave the highest proportion of positive responses (631 of 712 respondants, 88.6%) and the lowest proportion of negative responses (5 of 712, 0.7%) when asked if they would take a ‘proven, safe and effective vaccine’.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A similar trend was observed in regard to acceptance of the vaccine if one’s employer required it, except that responses from the oldest age cohort were similar to the youngest age cohort.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Russia had the highest proportion of negative responses (278 of 680, 40.9%) and the lowest proportion of respondents (184 of 680, 27.1%) willing to accept their employer’s recommendation (Supplementary Table 1).”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was considerable variation by country, with China again having the highest proportion of positive responses (596 of 712, 83.7%) and the lowest proportion of negative responses (26 of 712, 3.7%).”
§ A global survey of potential acceptance of a COVID-19 vaccine > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of these, 71.5% of participants reported that they would be very or somewhat likely to take a COVID-19 vaccine”
- Also stated in: § Discussion · plain
“Of these, 71.5% responded that they would take a vaccine if it were proven safe and effective”
§ A global survey of potential acceptance of a COVID-19 vaccine > Abstract
plainn = 13,426- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
- Also stated in: § Discussion · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No personally identifiable information was collected or stored.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Methods > Study participants
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Misinformation spread through multiple channels could have a considerable effect on the acceptance of a COVID-19 vaccine8.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Main
What the sources say
- Not checked yetcites [8] Cornwall, W. Officials gird for a war on vaccine misinformation. Science 369, 14–19 (2020). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Anti-vaccination activists are already campaigning in multiple countries against the need for a vaccine, with some denying the existence of COVID-19 altogether7.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Main
What the sources say
- Not checked yetcites [7] Enserink, M. & Cohen, J. Fact-checking Judy Mikovits, the controversial virologist attacking Anthony Fauci in a viral conspiracy video. Science https://www.sciencemag.org/news/2020/05/fact-checking-judy-mikovits-controversial-virologist-attacking-anthony-fauci-viral (2020). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Respondents’ identities were verified using IP addresses and mobile phone numbers to ensure that each participant was real and unique upon initial registration.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Methods > Study participants
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This study was approved by Emerson College, USA (institutional review board protocol no. 20–023-F-E-6/12), with an expiration date of June 11, 2021.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Methods > Study participants
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This particular survey was conducted in the context of a highly dynamic and changing landscape, with daily variations in perceived disease threat and COVID-19 vaccine development itself.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Discussion
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, reporting one’s willingness to get vaccinated might not be necessarily a good predictor of acceptance, as vaccine decisions are multifactorial and can change over time.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The accelerated pace of vaccine development has further heightened public anxieties and could compromise acceptance9.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Main
What the sources say
- Not checked yetcites [9] Fadda, M., Albanese, E. & Suggs, L. S. When a COVID-19 vaccine is ready, will we all be ready for it? Int. J. Public Health 65, 711–712 (2020). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Lessons learned from previous infectious disease outbreaks and public health emergencies, including HIV, H1N1, SARS, MERS and Ebola, remind us that trusted sources of information and guidance are fundamental to disease control16.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Discussion
What the sources say
- Not checked yetcites [16] Siegrist, M. & Zingg, A. The role of public trust during pandemics: implications for crisis communication. Eur. Psychol. 19, 23–32 (2014). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Researchers have identified promising interventions for building confidence and reducing vaccine hesitancy in different contexts12,13”
§ A global survey of potential acceptance of a COVID-19 vaccine > Discussion
What the sources say
- Not checked yetcites [12] Thomson, A., Vallée-Tourangeau, G. & Suggs, L. S. Strategies to increase vaccine acceptance and uptake: from behavioral insights to context-specific, culturally-appropriate, evidence-based communications and interventions. Vaccine 36, 6457–6458 (2018). source
- Not checked yetcites [13] European Centre for Disease Prevention and Control. Catalogue of interventions addressing vaccine hesitancy. https://www.ecdc.europa.eu/sites/portal/files/documents/Catalogue-interventions-vaccine-hesitancy.pdf (2017).
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It must be kept in mind that all public surveys of the type reported here are snapshots taken at a point in time.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The online questionnaire was administered by Emerson College to gather information from respondents after obtaining their written, informed consent about the survey and this project.”
§ A global survey of potential acceptance of a COVID-19 vaccine > Methods > Study participants
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The level of vaccine acceptance required for community immunity is higher than the levels of willingness observed in most of the 19 countries.
“In most of the 19 countries surveyed in our study, current levels of willingness to accept a COVID-19 vaccine are insufficient to meet the requirements for community immunity.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“The heterogeneity analysis reported in Fig. 5, in which we analysed whether vaccination rates differ by age, sex, schooling and wealth variables, was not pre-specified and followed heterogeneity tests that are common in the vaccine literature54.”
From Last-mile delivery increases vaccine uptake in Sierra Leone (2024) · cites this paper as [54]
In the citing paper
“For COVID-19, information from employers and government sources has been shown to improve vaccine acceptance rates, which range from 55% to 90% around the world244.”
From mRNA vaccines for infectious diseases: principles, delivery and clinical translation (2021) · cites this paper as [244]
In the citing paper
“For COVID-19, information from employers and government sources has been shown to improve vaccine acceptance rates, which range from 55% to 90% around the world244.”
From mRNA vaccines for infectious diseases: principles, delivery and clinical translation (2021) · cites this paper as [244]
Citations
Cites
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Self-citationMeasuring trust in vaccination: A systematic review
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Self-citationExposing concerns about vaccination in low- and middle-income countries: a systematic review
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Le, C. N., Nguyen, U. T. T., & Hoang, D. T. (2022) · BMC Public Health · doi:10.1186/s12889-022-13236-3 · Cited by 25
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Exploring the determinants of global vaccination campaigns to combat COVID-19
Yin, F., Ji, M., Yang, Z., Wu, Z., Xia, X., Xing, T., She, Y., & Zhi, H. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01106-7 · Cited by 26
Khaskheli, M. B., Wang, S., Hussain, R. Y., Butt, M. J., Yan, X., & Majid, S. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1035536 · Cited by 24
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Ghasemi, P., Goodarzian, F., Šimić, V., & Tırkolaee, E. B. (2023) · International Journal of Systems Science Operations & Logistics · doi:10.1080/23302674.2023.2224105 · Cited by 24
Using Rhetorical Situations to Examine and Improve Vaccination Communication
Ihlen, Ø., Toledano, M., & Just, S. N. (2021) · Frontiers in Communication · doi:10.3389/fcomm.2021.697383 · Cited by 24
Factors influencing COVID-19 vaccination uptake in an elderly sample in PolandPreprint
Malesza, M., & Bozym, M. (2021) · medRxiv · doi:10.1101/2021.03.21.21254047 · Cited by 24
Zhao, X., Ma, Z., Xu, S., & Austin, L. L. (2024) · Health Communication · doi:10.1080/10410236.2022.2154017 · Cited by 23
Understanding vaccine hesitancy: the evidence
While, A. (2021) · British Journal of Community Nursing · doi:10.12968/bjcn.2021.26.6.278 · Cited by 23
Dubé, È., Dionne, M., Pelletier, C., Hamel, D., & Gadio, S. (2021) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2021.1947096 · Cited by 25
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The future of COVID-19: A vaccine review
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Fallucchi, F., Görges, L., Machado, J., Pieters, A., & Suhrcke, M. (2021) · Health Policy · doi:10.1016/j.healthpol.2021.05.003 · Cited by 22
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Willingness to take COVID-19 vaccination in low-income countries: Evidence from Ethiopia
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Factors associated with COVID-19 vaccination intent in Singapore, Australia and Hong Kong
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Kyprianidou, M., Konstantinou, P., Álvarez‐Gálvez, J., Ceccarelli, L., Gruszczyńska, E., Mierzejewska-Floreani, D., Loumba, N., Montagni, I., Tavoschi, L., Karekla, M., et al. (2022) · Behavioral Medicine · doi:10.1080/08964289.2022.2071198 · Cited by 22
Jiang, L. C., Sun, M.-R., Chu, T.-H., & Chia, S. C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.976091 · Cited by 19
Chang, Y., Lv, G., Liu, C., Huang, E., & Luo, B. (2022) · Expert Review of Vaccines · doi:10.1080/14760584.2023.2150169 · Cited by 22
Sialubanje, C., Nawa, M., Ng'andu, M., Sumani, E. M., Nkonkomalimba, M., Lyatumba, D. E., Mwale, A., Mpiana, F., Zulu, J. M., Mweempwa, B., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-058028 · Cited by 22
Public Health Needs the Public Trust: A Pandemic Retrospective
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Determinants of COVID-19 vaccine readiness and hesitancy among adults in sub-Saharan Africa
Abubakari, S. W., Workneh, F., Asante, K. P., Hemler, E. C., Madzorera, I., Wang, D., Ismail, A., Assefa, N., Azemraw, T., Lankoandé, B., et al. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0000713 · Cited by 22
Kigongo, E., Kabunga, A., Tumwesigye, R., Musinguzi, M., Izaruku, R., & Acup, W. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289295 · Cited by 22
Lu, Y., Wang, Q., Zhu, S., Xu, S., Kadirhaz, M., Zhang, Y., Zhao, N., Fang, Y., & Chang, J. (2023) · Social Science & Medicine · doi:10.1016/j.socscimed.2023.116270 · Cited by 22
Wang, Q., Xiu, S., Yang, L., Li, L., Yang, M., Wang, X., Shen, Y., Wang, W., & Lin, L. K. (2024) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2024.2310916 · Cited by 22
VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors
Khowaja, S. A., Khuwaja, P., Dev, K., & D’Aniello, G. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06434-4 · Cited by 22
The popularity of contradictory information about COVID-19 vaccine on social media in China
Wang, D., & Zhou, Y. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107320 · Cited by 22
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Systematic Review and Meta-analysis on COVID-19 Vaccine HesitancyPreprint
Aboelsaad, I., Hafez, D. M., Almaghraby, A. M., Abdelmoneim, S. A., El-Ganainy, S. O., Hamdy, N. A., Deghidy, E. A., El-Deen, A. E.-S. N., Elrewany, E. M. A., Khalil, A. H. T., et al. (2021) · medRxiv · doi:10.1101/2021.05.15.21257261 · Cited by 22
Hammam, N., Tharwat, S., El-Shereef, R. R., Elsaman, A. M., Khalil, N. M., Fathi, H. M., Salem, M. N., El‐Saadany, H. M., Samy, N., El-Bahnasawy, A. S., et al. (2021) · Rheumatology International · doi:10.1007/s00296-021-04941-0 · Cited by 21
Ahmed, T. F., Ahmed, A., Ahmed, S. K. N., & Ahmed, H. U. (2021) · Expert Review of Vaccines · doi:10.1080/14760584.2021.1964963 · Cited by 21
Chen, T., Li, X., Li, Q., Huang, L., Cai, Q., Wang, Y., Jiang, Y., Xu, Q., Lv, Q., & Wang, J. (2021) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2021.1984133 · Cited by 21
Kashif, M., Fatima, L., Ahmed, A. M., Ali, S. A., Memon, R. S., Afzal, M., Saeed, U., Gul, S., Aḥmad, J., Malik, F., et al. (2021) · Cureus · doi:10.7759/cureus.19106 · Cited by 24
Dietze, P., Hall, C., Price, O. H., Stewart, A. C., Crawford, S., Peacock, A., & Maher, L. (2021) · Drug and Alcohol Review · doi:10.1111/dar.13399 · Cited by 21
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Yoneoka, D., Eguchi, A., Nomura, S., Kawashima, T., Tanoue, Y., Murakami, M., Sakamoto, H., Maruyama‐Sakurai, K., Gilmour, S., Shi, S., et al. (2021) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2021.100330 · Cited by 21
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Jankowska‐Polańska, B., Sarzyńska, K., Czwojdziński, E., Świątoniowska-Lonc, N., Dudek, K. D., & Piwowar, A. (2022) · Vaccines · doi:10.3390/vaccines10040535 · Cited by 21
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Pacheco, M. L., Islam, T., Mahajan, M., Shor, A., Yin, M., Ungar, L., & Goldwasser, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.427 · Cited by 23
Bullivant, B., Alderman, K. B., King, C. L., & Steffens, M. (2022) · Public Health in Practice · doi:10.1016/j.puhip.2022.100349 · Cited by 21
The top 100 most cited articles on COVID-19 vaccine: a bibliometric analysis
Wang, W., Wang, H., Yao, T., Li, Y., Yi, L., Gao, Y., Lian, J., Feng, Y., & Wang, S. (2023) · Clinical and Experimental Medicine · doi:10.1007/s10238-023-01046-9 · Cited by 21
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Atuheirwe, M., Richard, O., Male, K. J., Ahimbisibwe, S., Sackey, J. D., & Sande, O. J. (2024) · BMC Public Health · doi:10.1186/s12889-024-17678-9 · Cited by 21
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Kopfová, J., Nábělková, P., Rachinskii, D. I., & Rouf, S. C. (2021) · Journal of Mathematical Biology · doi:10.1007/s00285-021-01629-8 · Cited by 21
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Metadata
- Authors
- Lazarus, J. V., Ratzan, S. C., Palayew, A., Gostin, L. O., Larson, H. J., Rabin, K., Kimball, S., & El-Mohandes, A.
- Affiliations
- Barcelona Institute for Global Health (ES)
- City University of New York (US)
- Georgetown University (US)
- London School of Hygiene & Tropical Medicine (GB)
- Emerson College (US)
- Corresponding author
- Lazarus, Jeffrey V.
- ORCID
- Venue
- Nature Medicine
- Published
- 2020-10-20
- Volume
- 27
- Issue
- 2
- Pages
- 225-228
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7573523
- OpenAlex
- W3093974194
- Semantic Scholar
- abe8c2a1c2a072c6c0f729204a329d82fffad38c
- Cited by
- 2,904
- References
- 19
- Citations by year
- 2026: 91 · 2025: 109 · 2024: 276 · 2023: 541 · 2022: 950 · 2021: 901 · 2020: 32
- FWCI
- 335.12
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-01-11)
- Open access
- bronze
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Scholarship - GEORGETOWN LAW (Georgetown University) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Funders
- City University of New York
- Ministerio de Ciencia, Innovación y Universidades (CEX 2018-000806-S)
- Centres de Recerca de Catalunya (CEX2018-000806-S)
- Generalitat de Catalunya (CEX2018-000806-S)
- Università Bocconi
- Topics
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Keywords
- COVID-19 vaccines, vaccine acceptance, vaccine hesitancy, vaccine confidence, public trust in government, cross-national comparison, vaccine attitudes
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Vaccines, Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Patient Acceptance of Health Care, Surveys and Questionnaires, Regression Analysis, Internationality, Young Adult
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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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 ›
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