Evidence map›Paper›PMID 36246042›Full record

ArticleJournal of the Association for Information Science and Technology2022

Trust in COVID-19 public health information.

Nitin Verma, Kenneth R Fleischmann, Le Zhou, Bo Xie, Min Kyung Lee, Kate Rich, Kristina Shiroma, Chenyan Jia, Tara Zimmerman

Abstract read
In one paragraph

Article in Journal of the Association for Information Science and Technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
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  8. Trust in COVID-19 public health information.Journal of the Association for Information Science and Technology · 2022
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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

9 authors.

Nitin VermaSchool of Information The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0003-2997-934X
Kenneth R FleischmannSchool of Information The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0002-0323-3526
Le ZhouDepartment of Work and Organizations University of Minnesota Minneapolis Minnesota USA.ORCID https://orcid.org/0000-0003-3507-8716
Bo XieSchool of Information The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0002-6016-6008
Min Kyung LeeSchool of Information The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0002-2696-6546
Kate RichDepartment of Communication University of Washington Seattle Washington USA.ORCID https://orcid.org/0000-0002-1936-4723
Kristina ShiromaSchool of Information The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0003-2348-002X
Chenyan JiaSchool of Journalism and Media The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0002-8407-9224
Tara ZimmermanSchool of Information The University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0001-7605-9429

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID-19. We present findings from a cross-sectional survey of 454 US adults-243 older (65+) and 211 younger (18-64) adults-who responded to questionnaires on human values, trust in COVID-19 information sources, attention to information quality, self-efficacy, and factual knowledge about COVID-19. Path analysis showed that trust in direct personal contacts (

Identifiers

PMID36246042
PMCPMC9538952

What OpenQuestion holds

Textmetadata
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.