Evidence map›Paper›PMID 35329126›Full record

ArticleInternational journal of environmental research and public health2022

Validating the Digital Health Literacy Instrument in Relation to COVID-19 Information (COVID-DHL-K) among South Korean Undergraduates.

Heeran Chun, Eun-Ja Park, Seul Ki Choi, Hyeran Yoon, Orkan Okan, Kevin Dadaczynski

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

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0cells of the map it votes in
21citing 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

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3 · Its place in the literature

Who cites it

21 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

6 authors.

Heeran ChunDepartment of Health Administration, Jungwon University, Chungbuk 28024, Korea.ORCID 0000-0002-2955-1004
Eun-Ja ParkKorea Institute for Health Social Affairs, Sejong 30147, Korea.
Seul Ki ChoiGraduate School of Urban Public Health, University of Seoul, Seoul 02504, Korea.
Hyeran YoonDepartment of Health Administration, Jungwon University, Chungbuk 28024, Korea.
Orkan OkanDepartment of Sports and Health Sciences, Technical University Munich, 80809 Munich, Germany.ORCID 0000-0003-1714-4783
Kevin DadaczynskiDepartment of Nursing and Health Science, Fulda University of Applied Sciences, 36037 Fulda, Germany.ORCID 0000-0002-7367-5362

Funding

Korea Research Foundation NRF 2020R1I1A3073554, NRF-2019S1A5C2A03081040)
6 · The paper itself

Abstract

Digital health literacy is crucial in accessing and applying health information in the COVID-19 pandemic period. Young college students are exposed daily to digital technologies, and they have further increased the use of digital information during the COVID-19 period. This study aimed to adapt DHLI into Korean and to assess the psychometric properties, during the COVID-19 pandemic period. A cross-sectional, nationwide, and web-based survey was conducted among 604 Korean undergraduates from 23 December 2020 to 8 January 2021. On the basis of the Digital Health Literacy Instrument (DHLI) by the Global COVID HL Network, the Korean questionnaire was developed by group translation, expert reviews, and forward-backward translation for validation. The scale reliability and validity were examined using Cronbach's alpha and confirmatory factor analysis. Results support the theoretical and empirical four-factor structure (search, express, evaluate, use) in the coronavirus-related DHL among Korean University students. Internal reliability of the overall scale was high (Cronbach's α = 0.908). The four-factor model was supported by confirmatory factor analysis (GFI = 0.972, CFI = 0.984, TLI = 0.978, RMSEA = 0.045). This study revealed that the COVID-DHL-K is a valid and reliable measure with appropriate psychometric characteristics.

Indexed as

COVID-19Health LiteracyCross-Sectional StudiesHumansPandemicsReproducibility of ResultsRepublic of KoreaStudentsdigital health literacymeasureuniversity studentsvalidation

Identifiers

PMID35329126
PMCPMC8950100

What OpenQuestion holds

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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.