Evidence map›Paper›PMID 41278374›Full record

ArticleDigital health

Readability of COVID-19 health science information on the official websites of 27 Provincial Health Commissions in China.

Yuxi Zhang, Jia Liu, Qingqing Chen, Chen Chen, Siqi Luo, Lan Ji, Fei Li, Lunfang Xie

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yuxi ZhangSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.ORCID https://orcid.org/0009-0009-3593-0180
Jia LiuSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.
Qingqing ChenSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.
Chen ChenSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.
Siqi LuoSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.
Lan JiSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.
Fei LiThe First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Lunfang XieSchool of Nursing, Anhui Medical University, Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The study aimed to evaluate the readability of COVID-19 health science information released by the Provincial Health Commissions of China and analyze the problems existing in the current health information webpage, so as to provide an improvement direction for the development of highly readable health information related to public health events. Methods: The main research data were obtained from health articles on COVID-19 published on the websites of 27 Provincial Health Commissions of China. Two researchers independently evaluated the readability of webpages using an Online Health Information Readability Evaluation Tool across seven dimensions. Besides, we also applied linguistic analysis techniques to assess readability from text factors. Results: Readability scores ranged from 0.49 to 0.98 for 92 included articles, with an average of 0.78 ± 0.10. Among the evaluation dimensions, organizational layout obtained the highest score, whereas visual assistance received the lowest score. No statistically significant differences were observed in readability level across different regions or information topics ( Conclusions: The overall readability of the COVID-19 health information published by China's Provincial Health Commissions is acceptable, but the improvement measures for public understanding are needed. Health information producers should better align with the general public's literacy level through using shorter sentences, providing specific behavioral guidance with clear visual assistance, and utilize question-and-answer formats, which will be helpful to enhance the dissemination of the COVID-19 health information in China.

Indexed as

coronavirusCOVID-19health departmentonline health informationpublic healthreadability

Identifiers

PMID41278374
PMCPMC12638678

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.