Evidence map›Paper›PMID 37665096›Full record

ArticleJournal of health communication2023

Trust in COVID-19 Information from Different Media Types and Its Association with Preventive Measures Adoption in the U.S.

Hongmei Li, Baojiang Chen, Zhuo Chen, Liwei Chen, Donglan Zhang, Ming Wen, Jian Li, Yan Li, Xusong Han, Dejun Su

Open access · greenAbstract read
In one paragraph

Article in Journal of health communication, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
8.1field-weighted citation impact, top 3% 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

7 citing papers in PubMed, 14 citations in OpenAlex.

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  7. Cancer misinformation on social media.CA: a cancer journal for clinicians
    Review
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

10 authors at 8 institutions in 2 countries.

Hongmei LiStrategic Communication Program, Department of Media, Journalism and Film, Miami University, Oxford, Ohio, USA.ORCID 0000-0002-6923-6754
Baojiang ChenDepartment of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Austin, Texas, USA.
Zhuo ChenDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens, Georgia, USA.ORCID 0000-0002-5351-3489
Liwei ChenDepartment of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California,USA.
Donglan ZhangCenter for Population Health and Health Services Research, Department of Foundations of Medicine, NYU Grossman Long Island School of Medicine, Mineola, NY, USA.
Ming WenDepartment of Sociology, The University of Hong Kong.ORCID 0000-0001-6397-3473
Jian LiDepartment of Environmental Health Sciences, Fielding School of Public Health, School of Nursing, University of California Los Angeles, Los Angeles, California, USA.
Yan LiDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA.
Xusong HanSurveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia, USA.
Dejun SuDepartment of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA.ORCID 0000-0002-7723-3262
University of California, Los Angeles · USAmerican Cancer Society · USIcahn School of Medicine at Mount Sinai · USMiami University · USThe University of Texas Health Science Center at Houston · USUniversity of Georgia · USUniversity of Hong Kong · HKUniversity of Nebraska Medical Center · US

Funding

Using machine learning and causal inference approaches to investigate disparities in Alzheimer’s disease and related dementias outcomesR01MD013886 · NIMHD · UNIVERSITY OF GEORGIA · PI ZHANG, DONGLAN · 2019 to 2022
$2.4M
NIMHD NIH HHS R01 MD013886
6 · The paper itself

Abstract

This study compares and contrasts how key factors influence Americans' trust in different types of media (broadcast, print, and social) as COVID-19 information sources and how people's media trust is associated with their adoption of preventive measures. Our results from a national survey (sample = 2571) showed that age, political party affiliation, and race and ethnicity and income level were significantly associated with people's trust in different media types as COVID information sources. Elder adults trusted print and broadcast media more, while younger adults trusted social media more. Democrats and Lean Democrats had more trust in all three forms of media than Republicans and Lean Republicans. Asians had the highest levels of trust in all three media types, while Whites had the lowest level of trust in broadcast and social media. Trust in broadcast media was found to be associated with facial mask wearing, but trust in social media, however, did not contribute to the adoption of any COVID-19 preventive measures. This study contributes to a general understanding of media trust and mediated health communication and provides nuanced understanding of how demographic factors shape media trust and the consequence of media trust during a historical pandemic.

Indexed as

COVID-19AdultAgedHumansInformation SourcesMass MediaPandemicsTrust

Identifiers

PMID37665096
PMCPMC11929583
OpenAlexW4386407781

What OpenQuestion holds

Textmetadata
LicenceTDM
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.