The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
‘Vaccine hesitancy’ among university students in Italy during the COVID-19 pandemic
Open accessAbstract
The debate around vaccines has been in the spotlight over the last few years in Europe, both within the scientific community and the general public debate. In this regard, the case of the Italian vaccination debate is particularly worrying given that Italy has been one of the European countries with the highest number of measles cases in the recent past. According to this scenario, we conducted a cross-sectional study on a convenience sample of Italian university students aimed at: (1) exploring their attitudes towards a future vaccine to prevent COVID-19 and; (2) evaluating the impact of the university curricula (healthcare vs. non-healthcare curricula) on the intention to vaccinate. Descriptive analysis on the 735 students that answered to the question on the intention to vaccinate showed that 633 (86.1%) students reported that they would choose to have a vaccination for the COVID-19 coronavirus; on the other side, 102 (13.9%) students reported that they would not or be not sure to vaccine (low intention to vaccinate). This means that in our sample more than one student out of 10 shows low intention to vaccinate (vaccine hesitancy). Furthermore, when running analysis comparing healthcare students versus non-healthcare students we found no significant differences in responses’ percentage distribution (p = .097). Understanding the student’s perspective about the future COVID-19 vaccine and supporting their health engagement and consciousness may be useful in planning adequate response and multidisciplinary educational strategies—including the psychological perspective on vaccine hesitancy underlying factors - in the post-pandemic period.
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
Using social and behavioural science to support COVID-19 pandemic response
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0884-z · Cited by 4,964
Vaccine confidence in the time of COVID-19
Harrison, E. A., & Wu, J. W. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-020-00634-3 · Cited by 477
Mandatory Influenza Vaccination of Health Care Workers: Translating Policy to Practice
Babcock, H. M., Gemeinhart, N., Jones, M., Dunagan, W. C., & Woeltje, K. F. (2010) · Clinical Infectious Diseases · doi:10.1086/650752 · Cited by 289
Clinical prevention and population health
Allan, J. D., Barwick, T. A., Cashman, S. B., Cawley, J. F., Day, C., Douglass, C. W., Evans, C. S., Garr, D. R., Maeshiro, R., McCarthy, R., et al. (2004) · American Journal of Preventive Medicine · doi:10.1016/s0749-3797(04)00206-5 · Cited by 160
Clinical prevention and population health
Allan, J. D., Barwick, T. A., Cashman, S. B., Cawley, J. F., Day, C., Douglass, C. W., Evans, C. H., Garr, D. R., Maeshiro, R., McCarthy, R. L., et al. (2004) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2004.08.010 · Cited by 146
Talbot, T. R., Bradley, S., Cosgrove, S. E., Ruef, C., Siegel, J. D., & Weber, D. J. (2005) · Infection Control and Hospital Epidemiology · doi:10.1086/502512 · Cited by 108
Seanehia, J., Treibich, C., Holmberg, C., Müller‐Nordhorn, J., Casin, V., Raude, J., & Mueller, J. E. (2017) · Vaccine · doi:10.1016/j.vaccine.2017.03.086 · Cited by 58
Prevention for the 21st Century
Pomrehn, P. R., Davis, M. V., Chen, D. W., & Barker, W. H. (2000) · Academic Medicine · doi:10.1097/00001888-200007001-00002 · Cited by 57
Marotta, C., Raia, D. D., Ventura, G., Casuccio, N., Dieli, F., D’Angelo, C., Restivo, V., Costantino, C., Vitale, F. M., & Casuccio, A. (2017) · Journal of preventive medicine and hygiene · doi:10.15167/2421-4248/jpmh2017.58.2.720 · Cited by 45
A Multidisciplinary Research Agenda for Understanding Vaccine-Related Decisions
Larson, H. J., Leask, J., Aggett, S., Sevdalis, N., & Thomson, A. (2013) · Vaccines · doi:10.3390/vaccines1030293 · Cited by 42
Cited by
COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates
Sallam, M. (2021) · Vaccines · doi:10.3390/vaccines9020160 · Cited by 2,128
Skjefte, M., Ngirbabul, M., Akeju, O., Escudero, D., Hernandez-Diaz, S., Wyszynski, D. F., & Wu, J. W. (2021) · European Journal of Epidemiology · doi:10.1007/s10654-021-00728-6 · Cited by 558
Cascini, F., Pantovic, A., Al-Ajlouni, Y., Failla, G., & Ricciardi, W. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101113 · Cited by 396
Taylor, S., Landry, C. A., Paluszek, M. M., Groenewoud, R., Rachor, G. S., & Asmundson, G. J. G. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.575950 · Cited by 282
Acceptance of a COVID-19 Vaccine in Japan during the COVID-19 Pandemic
Machida, M., Nakamura, I., Kojima, T., Saito, R., Nakaya, T., Hanibuchi, T., Takamiya, T., Odagiri, Y., Fukushima, N., Kikuchi, H., et al. (2021) · Vaccines · doi:10.3390/vaccines9030210 · Cited by 281
Metadata
- Authors
- Barello, S., Nania, T., Dellafiore, F., Graffigna, G., & Caruso, R.
- Venue
- European Journal of Epidemiology
- Published
- 2020-08
- Volume
- 35
- Issue
- 8
- Pages
- 781-783
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- OpenAlex
- W3047643527
- Cited by
- 422
- References
- 12
- FWCI
- 49.23
- Type
- journal article
- Language
- en
- Open access
- bronze
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: ‘Vaccine hesitancy’ among university students in Italy during the COVID-19 pandemic. https://app.cochat.ai/papers/10.1007/s10654-020-00670-z
- The paper
- Barello, S., Nania, T., Dellafiore, F., Graffigna, G., & Caruso, R. (2020). ‘Vaccine hesitancy’ among university students in Italy during the COVID-19 pandemic. European Journal of Epidemiology. https://doi.org/10.1007/s10654-020-00670-z
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.