Evidence map›Paper›PMID 33784240›Full record

ArticleJournal of medical Internet research2021

Socioeconomic Disparities in eHealth Literacy and Preventive Behaviors During the COVID-19 Pandemic in Hong Kong: Cross-sectional Study.

Ziqiu Guo, Sheng Zhi Zhao, Ningyuan Guo, Yongda Wu, Xue Weng, Janet Yuen-Ha Wong, Tai Hing Lam, Man Ping Wang

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 4 pooled it
27.3field-weighted citation impact, top 1% 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

65 citing papers in PubMed, 4 syntheses or guidelines pooled it, 120 citations in OpenAlex.

  1. Pooled it
  2. The level of electronic health literacy among older adults: a systematic review and meta-analysis.Archives of public health = Archives belges de sante publique · 2024
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5 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Ziqiu GuoSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-4727-9101
Sheng Zhi ZhaoSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-3024-0956
Ningyuan GuoSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-1790-3244
Yongda WuSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-0226-0984
Xue WengSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-9564-4284
Janet Yuen-Ha WongSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-3000-4577
Tai Hing LamSchool of Public Health, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-2033-9971
Man Ping WangSchool of Nursing, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-4000-2388
University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundeHealth literacy can potentially facilitate web-based information seeking and taking informed measures.

objectiveThis study aimed to evaluate socioeconomic disparities in eHealth literacy and seeking of web-based information on COVID-19, and their associations with COVID-19 preventive behaviors.

methodsThe COVID-19 Health Information Survey (CoVHIns), using telephonic (n=500) and web-based surveys (n=1001), was conducted among adults in Hong Kong in April 2020. The Chinese eHealth literacy scale (eHEALS; score range 8-40) was used to measure eHealth literacy. COVID-19 preventive behaviors included wearing surgical masks, wearing fabric masks, washing hands, social distancing, and adding water or bleach to the household drainage system. Adjusted beta coefficients and the slope indices of inequality for the eHEALS score by socioeconomic status, adjusted odds ratios (aORs) for seeking of web-based information on COVID-19 by socioeconomic status, and aORs for the high adherence to preventive behaviors by the eHEALS score and seeking of web-based information on COVID-19 were calculated.

resultsThe mean eHEALS score was 26.10 (SD 7.70). Age was inversely associated with the eHEALS score, but education and personal income were positively associated with the eHEALS score and seeking of web-based information on COVID-19 (for all, P for trend<.05). Participants who sought web-based information on COVID-19 showed high adherence to the practice of wearing surgical masks (aOR 1.56, 95% CI 1.15-2.13), washing hands (aOR 1.33, 95% CI 1.05-1.71), social distancing (aOR 1.48, 95% CI 1.14-1.93), and adding water or bleach to the household drainage system (aOR 1.67, 95% CI 1.28-2.18). Those with the highest eHEALS score displayed high adherence to the practice of wearing surgical masks (aOR 3.84, 95% CI 1.63-9.05), washing hands (aOR 4.14, 95% CI 2.46-6.96), social distancing (aOR 2.25, 95% CI 1.39-3.65), and adding water or bleach to the household drainage system (aOR 1.94, 95% CI 1.19-3.16), compared to those with the lowest eHEALS score.

conclusionsChinese adults with a higher socioeconomic status had higher eHealth literacy and sought more web-based information on COVID-19; both these factors were associated with a high adherence to the guidelines for preventive behaviors during the COVID-19 pandemic.

Indexed as

AdultCOVID-19Cross-Sectional StudiesFemaleHealth LiteracyHealth SurveysHong KongHumansMalePandemicsSARS-CoV-2Socioeconomic FactorsTelemedicineCOVID-19eHealth literacypreventive behaviorssocioeconomic disparitiesweb-based information seeking

Identifiers

PMID33784240
PMCPMC8048711
OpenAlexW3148320849

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.