Evidence map›Paper›PMID 38720234›Full record

ArticleKorean journal of family medicine2024

Understanding the "Infodemic" Threat: A Case Study of the COVID-19 Pandemic.

Albert Andrew

Abstract read
In one paragraph

Article in Korean journal of family medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
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

1 author.

Albert AndrewSchool of Medicine, University of Auckland, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic is notable among infectious diseases for its distinctive impact, which has halted millions of livelihoods owing to strict social distancing rules and lockdowns. Consequently, millions of individuals have turned to online sources, particularly social media, to remain informed about the virus. The transition to digital sources has resulted in an abundance of information, including both accurate and misleading or false content being shared and consumed on online platforms, contributing to what is commonly referred to as an "infodemic." Although these platforms have been valuable tools for healthcare professionals and public health authorities in disseminating crucial public health messages, they have also aided in the spread of misleading and false information. The widespread dissemination of false information has been instrumental in propagating harmful beliefs and behaviors such as vaccine hesitancy, promoting discriminatory attitudes, and endorsing false beliefs about the efficacy of certain therapeutic products for treating COVID-19. False information has undoubtedly become a challenge and burden for governments, health professionals, and the general population. This review has three main objectives: (1) to assess the scope of the "infodemic" issue, including investigating the factors contributing to the spread of false information online; (2) to examine the multifaceted consequences resulting from false information; and (3) to argue that an interdisciplinary, multi-layered approach, encompassing a focus on prevention, deterrence, and education, should be adopted to prevent the conception and dissemination of false information in this modern digital age.

Indexed as

CoronavirusCOVID-19DisinformationMisinformationPandemic

Identifiers

PMID38720234
PMCPMC11273163

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.