ReviewCritical care explorations2020
Misinformation During the Coronavirus Disease 2019 Outbreak: How Knowledge Emerges From Noise.
Review in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Clinical trials in COVID-19 management & prevention: A meta-epidemiological study examining methodological quality.Journal of clinical epidemiology · 2021Pooled it
- Status of evidence on efficacy and safety of Indian traditional medicine (Ayush) for COVID-19: a qualitative review and evidence map synthesis.Systematic reviews · 2026Review
- Article
- Trust in information sources as a moderator of the impact of COVID-19 anxiety and exposure to information on conspiracy thinking and misinformation beliefs: a multilevel study.BMC psychology · 2023Article
- COVID-19 Lessons Learned: Response to the Anticipated Ventilator Shortage.Respiratory care · 2023Article
- The need for a holistic approach toward pandemic control: lessons from a cross-sectional study on COVID-19 in Meghalaya, India.Journal of public health policy · 2022Article
- Health information use by patients with systemic lupus erythematosus (SLE) pre and during the COVID-19 pandemic.Lupus science & medicine · 2022Article
- Social Media and the Quest for Equity and Diversity in Oncology: On Safe Spaces and the Concept of the Public Physician.JCO oncology practice · 2022Review
- Public perceptions during the first wave of the COVID-19 pandemic in Canada: a demographic analysis of self-reported beliefs, behaviors, and information acquisition.BMC public health · 2022Article
- Discerning Fact From Fiction: An Assessment of Coronavirus-19 Misinformation Among Patients in Rural Michigan.Cureus · 2022Article
- Perceptions of adult Arkansans regarding trusted sources of information about the COVID-19 pandemic.BMC public health · 2021Article
- Systems to support scholarly social media: a qualitative exploration of enablers and barriers to new scholarship in academic medicine.Canadian medical education journal · 2021Article
- Changing perceptions about COVID-19 risk and adherence to preventive strategies in Uganda: Evidence from an online mixed-methods survey.Scientific African · 2021Article
- The COVID-19 puzzle: deciphering pathophysiology and phenotypes of a new disease entity.The Lancet. Respiratory medicine · 2021Review
- Medical Education During the COVID-19 Pandemic.Chest · 2021Review
- How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises.JMIR public health and surveillance · 2021Article
- Knowledge, Attitudes, and Safety Practices About COVID-19 Among High School Students in Iran During the First Wave of the Pandemic.Frontiers in public health · 2021Article
- Good practices in harnessing social media for scholarly discourse, knowledge translation, and education.Perspectives on medical education · 2021Article
- Assessing adolescents' critical health literacy: How is trust in government leadership associated with knowledge of COVID-19?PloS one · 2021Article
- Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 9 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although the amount of information generated during this most recent coronavirus disease 2019 pandemic is enormous, much is of uncertain trustworthiness. This review summaries the many potential sources of information that clinicians turn to during pandemic illness, the challenges associated with performing methodologically sound research in this setting and potential approaching to conducting well done research during a health crisis. DATA SOURCES: Not applicable. STUDY SELECTION: Not applicable. DATA EXTRACTION: Not applicable. DATA SYNTHESIS: Not applicable.
conclusionsPandemics and healthcare crises provide extraordinary opportunities for the rapid generation of reliable scientific information but also for misinformation, especially in the early phases, which may contribute to public hysteria. The best way to combat misinformation is with trustworthy data produced by healthcare researchers. Although challenging, research can occur during pandemics and crises and is facilitated by advance planning, governmental support, targeted funding opportunities, and collaboration with industry partners. The coronavirus disease 2019 research response has highlighted both the dangers of misinformation as well as the benefits and possibilities of performing rigorous research during challenging times.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.