Evidence map›Paper›PMID 32756457›Full record

ArticleInternational journal of environmental research and public health2020

Online Newspaper Framing of Non-Communicable Diseases: Comparison of Mainland China, Taiwan, Hong Kong and Macao.

Angela Chang, Peter J Schulz, Angus Wenghin Cheong

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. 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
4.0field-weighted citation impact, top 5% 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, 17 citations in OpenAlex.

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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

3 authors at 2 institutions in 2 countries.

Angela ChangDepartment of Communication, Faculty of Social Sciences, University of Macau, Macao, China.ORCID 0000-0003-3663-151X
Peter J SchulzInstitute of Communication and Health, Lugano University, 6900 Lugano, Switzerland.ORCID 0000-0003-4281-489X
Angus Wenghin CheongERS e-Research & Solutions, Macao, China.
Università della Svizzera italiana · CHUniversity of Macau · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As non-communicable diseases (NCDs) are now well recognized as the leading cause of mortality among adult populations worldwide, they are also increasingly the focus of media coverage. As such, the objective of this study is to describe the framing of NCDs in the coverage of newspapers, with the understanding that it says something about the society producing it. Automatic content analysis was employed to examine disease topics, risks, and cost consequences, thus providing lay people with a chance of learning the etiology of NCDs and information available for fighting diseases. The result of the computational method identified a total of 152,810 news articles with one of the seven supra-categories of NCDs. The category of metabolic diseases was covered most frequently in the past ten years. Three health risks received ample attention in all 11 newspapers: stress burden, tobacco use, and genetic predispositions. The results evidenced how media framed risk information of illnesses would distort the way in which diseases were selected, interpreted, and the outcome communicated. Future research building on our findings can further examine whether news framing affects the way the readers perceive and prevent NCDs.

Indexed as

Newspapers as TopicNoncommunicable DiseasesChinaCommunicationHong KongHumansMacauTaiwanbehavioral factorcausal assertionsframingnon-communicable diseaseonline pressrisk factors

Identifiers

PMID32756457
PMCPMC7432841
OpenAlexW3046525294

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.