Evidence map›Paper›PMID 32217507›Full record

ArticleJMIR public health and surveillance2020

Assessment of Health Information About COVID-19 Prevention on the Internet: Infodemiological Study.

Ignacio Hernández-García, Teresa Giménez-Júlvez

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 4 pooled it
79.6field-weighted citation impact, top 1% of its field
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

90 citing papers in PubMed, 4 syntheses or guidelines pooled it, 239 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Pooled it
  5. Trial
  6. Article
  7. Article
  8. Article
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  10. When Patients Go to "Dr. Google" Before They Go to the Emergency Department.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Observational
  11. Article
  12. Article
  13. Parental COVID-19-related health information practises, sources, evaluations and needs: A qualitative interview study.Health expectations : an international journal of public participation in health care and health policy · 2023
    Article
  14. Article
  15. Observational
  16. Article
  17. A Study of Pandemic Prevention Health Behavior in Adults.International journal of environmental research and public health · 2022
    Article
  18. Article
  19. Article
  20. Article

30 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

2 authors at 2 institutions in 1 country.

Ignacio Hernández-GarcíaDepartment of Preventive Medicine, Lozano Blesa University Clinical Hospital of Zaragoza, Zaragoza, Spain.ORCID 0000-0003-1334-6783
Teresa Giménez-JúlvezDepartment of Preventive Medicine, Miguel Servet University Hospital of Zaragoza, Zaragoza, Spain.ORCID 0000-0003-1159-314X
Hospital Clínico Universitario Lozano Blesa · ESHospital Universitario Miguel Servet · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe internet is a large source of health information and has the capacity to influence its users. However, the information found on the internet often lacks scientific rigor, as anyone may upload content. This factor is a cause of great concern to scientific societies, governments, and users.

objectiveThe objective of our study was to investigate the information about the prevention of coronavirus disease 2019 (COVID-19) on the internet.

methodsOn February 29, 2020, we performed a Google search with the terms "Prevention coronavirus," "Prevention COVID-19," "Prevención coronavirus," and "Prevención COVID-19". A univariate analysis was performed to study the association between the type of authorship, country of publication, and recommendations to avoid COVID-19 according to the World Health Organization (WHO).

resultsIn total, 80 weblinks were reviewed. Most of them were produced in the United States and Spain (n=58, 73%) by digital media sources and official public health organizations (n=60, 75%). The most mentioned WHO preventive measure was "wash your hands frequently" (n=65, 81%). A less frequent recommendation was to "stay home if you feel unwell" (n=26, 33%). The analysis by type of author (official public health organizations versus digital media) revealed significant differences regarding the recommendation to wear a mask when you are healthy only if caring for a person with suspected COVID-19 (odds ratio [OR] 4.39). According to the country of publication (Spain versus the United States), significant differences were detected regarding some recommendations such as "wash your hands frequently" (OR 9.82), "cover your mouth and nose with your bent elbow or tissue when you cough or sneeze" (OR 4.59), or "stay home if you feel unwell" (OR 0.31).

conclusionsIt is necessary to urge and promote the use of the websites of official public health organizations when seeking information on COVID-19 preventive measures on the internet. In this way, users will be able to obtain high-quality information more frequently, and such websites may improve their accessibility and positioning, given that search engines justify the positioning of links obtained in a search based on the frequency of access to them.

Indexed as

Consumer Health InformationCoronavirusInformation Storage and RetrievalInternetBetacoronavirusCoronavirus InfectionsCOVID-19HumansPandemicsPneumonia, ViralSARS-CoV-2SpainUnited StatesauthorshipcoronavirusCOVID-19digital mediaevaluationinfodemicinfodemiologyinformationinternetofficial public health organizationspreventionWorld Health Organization

Identifiers

PMID32217507
PMCPMC7117090
OpenAlexW3013917522

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.