Evidence map›Paper›PMID 36891332›Full record

ArticleFrontiers in public health2023

Relationship between health literacy and COVID-19 knowledge: A cross-sectional study.

Zhenbo Tao, Qianqian Xu, Yingying Zhu, Qiuhong Mei, Hongwei Feng, Qiuyan Jin, Shige Ding, Ying Dong

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

8 authors.

Zhenbo TaoNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Qianqian XuNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Yingying ZhuNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Qiuhong MeiNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Hongwei FengNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Qiuyan JinNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Shige DingNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.
Ying DongNingbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Health literacy (HL) is a protective factor for some chronic diseases. However, its role in the Coronavirus Disease 2019 (COVID-19) pandemic has not been clarified. This study aims to explore the association between HL and COVID-19 knowledge among residents in Ningbo. Methods: A total of 6,336 residents aged 15-69 years in Ningbo were selected by multi-stage stratified random sampling method. The "Health Literacy Questionnaire of Chinese Citizens (2020)" was used to evaluate the relationship between COVID-19 knowledge and HL. Chi-square test, Mann-Whitney Results: The HL and COVID-19 knowledge levels of Ningbo residents were 24.8% and 15.7%, respectively. After adjusting for confounding factors, people with adequate HL were the more likely to have adequate COVID-19 knowledge compared with those with limited HL ( Conclusion: COVID-19 knowledge is significantly associated with HL. Improving HL may influence people's knowledge about COVID-19, thereby changing people's behaviors, and finally combating the pandemic.

Indexed as

COVID-19Health BehaviorHealth Knowledge, Attitudes, PracticeHealth LiteracyChinaCross-Sectional StudiesHumansPandemicsSurveys and QuestionnairesattitudebehaviorCOVID-19health literacyknowledge

Identifiers

PMID36891332
PMCPMC9986311

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Registered trials

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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.