Evidence map›Paper›PMID 37808241›Full record

ArticleDigital health

Cut-off value of the eHEALS score as a measure of eHealth skills among rural residents in Gansu, China.

Yang Zhu, Xiaohui Wang, Xinyi You, Hongmei Zhao, Yawei Guo, Wangnan Cao, Meiqi Xin, Jinghua Li

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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

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0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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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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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Yang ZhuSchool of Public Health, Sun Yat-sen University, Guangzhou, China.
Xiaohui WangSchool of Public Health, Lanzhou University, Lanzhou, China.
Xinyi YouSchool of Public Health, Sun Yat-sen University, Guangzhou, China.
Hongmei ZhaoSchool of Public Health, Lanzhou University, Lanzhou, China.
Yawei GuoSchool of Public Health, Sun Yat-sen University, Guangzhou, China.
Wangnan CaoSchool of Public Health, Peking University, Bejjing, China.
Meiqi XinDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Jinghua LiSchool of Public Health, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0002-9571-7359

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In this study, our aim was to quantify eHealth literacy and determine a cut-off value for eHEALS scores that signifies adequate eHealth literacy among rural residents in Gansu, China. Methods: This cross-sectional investigation encompassed 18 rural areas across three cities-Lanzhou, Wuwei, and Dingxi-in Gansu. A total of 451 residents were recruited and underwent evaluations for both eHealth literacy and eHealth skills, utilizing the eHEALS and self-constructed tasks, respectively. A receiving operator characteristic curve was plotted with eHealth skills as the dependent variable and eHealth literacy levels as the independent variable, aiming to determine a cut-off value for eHEALS indicating adequate eHealth literacy and evaluate its predictive capacity. Results: Among the 451 respondents, 10.9% did not possess a personal electronic device with Internet access, while 6.4% owned but had never used them. Within the remaining 373 residents, the mean eHealth literacy score was 25.85 (SD:10.93), item scores ranged from 3.12 (SD:1.45) to 3.42 (SD:1.60). Completion rates for three eHealth skills varied from 39.1% to 59.8%. The cut-off value was 29.5 determined by the Youden index. The area under the receiver operating curve was 0.829, with a sensitivity of 86.7%, and a specificity of 66.8%. Conclusions: Our findings emphasized that eHealth literacy among rural residents remains at a low level. Moreover, we identified a cut-off value of 29.5 for eHEALS scores that signifies adequate eHealth literacy within this demographic.

Indexed as

Chinacut-off valueeHealth literacyeHealth skillsrural residents

Identifiers

PMID37808241
PMCPMC10552485

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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.