Evidence map›Paper›PMID 33048825›Full record

ArticleJournal of medical Internet research2020

Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study.

Jung Jae Lee, Kyung-Ah Kang, Man Ping Wang, Sheng Zhi Zhao, Janet Yuen Ha Wong, Siobhan O'Connor, Sook Ching Yang, Sunhwa Shin

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
141citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

141 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

81 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jung Jae LeeSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0001-9704-2116
Kyung-Ah KangCollege of Nursing, Sahmyook University, Seoul, Republic of Korea.ORCID 0000-0002-3799-9554
Man Ping WangSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-4000-2388
Sheng Zhi ZhaoSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-3024-0956
Janet Yuen Ha WongSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-3000-4577
Siobhan O'ConnorSchool of Health in Social Science, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0001-8579-1718
Sook Ching YangSchool of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom.ORCID 0000-0001-9003-0518
Sunhwa ShinCollege of Nursing, Sahmyook University, Seoul, Republic of Korea.ORCID 0000-0003-4052-9542

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOnline misinformation proliferation during the COVID-19 pandemic has become a major public health concern.

objectiveWe aimed to assess the prevalence of COVID-19 misinformation exposure and beliefs, associated factors including psychological distress with misinformation exposure, and the associations between COVID-19 knowledge and number of preventive behaviors.

methodsA cross-sectional online survey was conducted with 1049 South Korean adults in April 2020. Respondents were asked about receiving COVID-19 misinformation using 12 items identified by the World Health Organization. Logistic regression was used to compute adjusted odds ratios (aORs) for the association of receiving misinformation with sociodemographic characteristics, source of information, COVID-19 misinformation belief, and psychological distress, as well as the associations of COVID-19 misinformation belief with COVID-19 knowledge and the number of COVID-19 preventive behaviors among those who received the misinformation. All data were weighted according to the Korea census data in 2018.

resultsOverall, 67.78% (n=711) of respondents reported exposure to at least one COVID-19 misinformation item. Misinformation exposure was associated with younger age, higher education levels, and lower income. Sources of information associated with misinformation exposure were social networking services (aOR 1.67, 95% CI 1.20-2.32) and instant messaging (aOR 1.79, 1.27-2.51). Misinformation exposure was also associated with psychological distress including anxiety (aOR 1.80, 1.24-2.61), depressive (aOR 1.47, 1.09-2.00), and posttraumatic stress disorder symptoms (aOR 1.97, 1.42-2.73), as well as misinformation belief (aOR 7.33, 5.17-10.38). Misinformation belief was associated with poorer COVID-19 knowledge (high: aOR 0.62, 0.45-0.84) and fewer preventive behaviors (≥7 behaviors: aOR 0.54, 0.39-0.74).

conclusionsCOVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation.

Indexed as

AdultAgedCommunicationCOVID-19Cross-Sectional StudiesFemaleHumansKnowledgeMaleMiddle AgedPrevalenceSurveys and QuestionnairesTelemedicineYoung AdultanxietybehaviorCOVID-19depressioninfodemicinfodemiologyknowledgemisinformationpreventionpreventive behaviorsPTSD

Identifiers

PMID33048825
PMCPMC7669362

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.