The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Association Between Public Knowledge About COVID-19, Trust in Information Sources, and Adherence to Social Distancing: Cross-Sectional Survey
Open accessAbstract
Background: The success of behavioral interventions and policies designed to reduce the impact of the COVID-19 pandemic depends on how well individuals are informed about both the consequences of infection and the steps that should be taken to reduce the impact of the disease. Objective: The aim of this study was to investigate associations between public knowledge about COVID-19, adherence to social distancing, and public trust in government information sources (eg, the US Centers for Disease Control and Prevention), private sources (eg, FOX and CNN), and social networks (eg, Facebook and Twitter) to inform future policies related to critical information distribution. Methods: We conducted a cross-sectional survey (N=1243) between April 10 and 14, 2020. Data collection was stratified by US region and other demographics to ensure representativeness of the sample. Results: Government information sources were the most trusted among the public. However, we observed trends in the data that suggested variations in trust by age and gender. White and older populations generally expressed higher trust in government sources, while non-White and younger populations expressed higher trust in private sources (eg, CNN) and social networks (eg, Twitter). Trust in government sources was positively associated with accurate knowledge about COVID-19 and adherence to social distancing. However, trust in private sources (eg, FOX and CNN) was negatively associated with knowledge about COVID-19. Similarly, trust in social networks (eg, Facebook and Twitter) was negatively associated with both knowledge and adherence to social distancing. Conclusions: During pandemics such as the COVID-19 outbreak, policy makers should carefully consider the quality of information disseminated through private sources and social networks. Furthermore, when disseminating urgent health information, a variety of information sources should be used to ensure that diverse populations have timely access to critical knowledge.
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
(2013) · Cited by 44,563
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020) · Psychological Science · doi:10.1177/0956797620939054 · Cited by 1,715
Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.606716 · Cited by 1,338
Rubin, G. J., Amlot, R., Page, L., & Wessely, S. (2009) · BMJ · doi:10.1136/bmj.b2651 · Cited by 1,106
Allington, D., Duffy, B., Wessely, S., Dhavan, N., & Rubin, J. (2021) · Psychological Medicine · doi:10.1017/s003329172000224x · Cited by 904
Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter
Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., Zarka, J., Traboulsi, C., Akl, E., & Baddour, K. (2020) · Cureus · doi:10.7759/cureus.7255 · Cited by 899
SARS Risk Perception, Knowledge, Precautions, and Information Sources, the Netherlands
Brug, J., Aro, A. R., Oenema, A., de Zwart, O., Richardus, J. H., & Bishop, G. D. (2004) · Emerging Infectious Diseases · doi:10.3201/eid1008.040283 · Cited by 522
Cited by
Zhang, Y., Suhaimi, N., Yongsatianchot, N., Gaggiano, J. D., Kim, M., Patel, S. A., Sun, Y., Marsella, S., Griffin, J., & Parker, A. G. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501889 · Cited by 37
Brunette, M. F., Erlich, M. D., Edwards, M. L., Adler, D. A., Berlant, J., Dixon, L., First, M. B., Oslin, D. W., Siris, S. G., & Talley, R. M. (2023) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0000000000001734 · Cited by 26
Metadata
- Authors
- Fridman, I., Lucas, N., Henke, D., & Zigler, C. K.
- Venue
- JMIR Public Health and Surveillance
- Publisher
- JMIR Publications Inc.
- Published
- 2020-09-15
- Volume
- 6
- Issue
- 3
- Pages
- e22060
- ISSN
- 2369-2960 (ISSN-L)
- OpenAlex
- W3084738750
- Cited by
- 264
- References
- 24
- FWCI
- 54.60
- Type
- journal article
- Language
- en
- Open access
- gold
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: Association Between Public Knowledge About COVID-19, Trust in Information Sources, and Adherence to Social Distancing: Cross-Sectional Survey. https://app.cochat.ai/papers/10.2196/22060
- The paper
- Fridman, I., Lucas, N., Henke, D., & Zigler, C. K. (2020). Association Between Public Knowledge About COVID-19, Trust in Information Sources, and Adherence to Social Distancing: Cross-Sectional Survey. JMIR Public Health and Surveillance. https://doi.org/10.2196/22060
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.