Evidence map›Paper›PMID 35155334›Full record

ArticleFrontiers in public health

Social Media Use, eHealth Literacy, Knowledge, Attitudes, and Practices Toward COVID-19 Vaccination Among Chinese College Students in the Phase of Regular Epidemic Prevention and Control: A Cross-Sectional Survey.

Ning Qin, Shuangjiao Shi, Yinglong Duan, Guiyue Ma, Xiao Li, Zhiying Shen, Shuhua Zhang, Aijing Luo, Zhuqing Zhong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
3.4field-weighted citation impact, top 8% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Ning QinNursing Department, Third Xiangya Hospital, Central South University, Changsha, China.
Shuangjiao ShiDepartment of Cardiology, The Third Xiangya Hospital of Central South University, Changsha, China.
Yinglong DuanDepartment of Emergency, The Third Xiangya Hospital of Central South University, Changsha, China.
Guiyue MaXiangya School of Nursing, Central South University, Changsha, China.
Xiao LiXiangya School of Nursing, Central South University, Changsha, China.
Zhiying ShenNursing Department, Third Xiangya Hospital, Central South University, Changsha, China.
Shuhua ZhangDepartment of Emergency, The Third Xiangya Hospital of Central South University, Changsha, China.
Aijing LuoKey Laboratory of Medical Information Research, College of Hunan Province, Central South University, Changsha, China.
Zhuqing ZhongNursing Department, Third Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNThird Xiangya Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCollege students are at a high risk of being infected with COVID-19, and they are one of the key population clusters that should be vaccinated. The present study aimed to investigate the knowledge, attitudes, and practices (KAP) toward COVID-19 vaccination among Chinese college students, and to determine the relationships among social media use, eHealth literacy, and KAP toward COVID-19 vaccination among Chinese college students.

methodsA cross-sectional survey was conducted by administering questionnaires to evaluate KAP toward COVID-19 vaccination, social media use, and eHealth literacy in one of the groups of Chinese college students. Multiple linear regression analysis was performed to determine the association among social media use, eHealth literacy, and KAP regarding COVID-19 vaccination.

resultsAmong the 3,785 validated questionnaires collected from Chinese college students, male students accounted for 59.74%, and the mean age of the college students was (20.90 ± 3.14) years. More than four-fifths (83.43%) of the college students spent <2 h a week on social media, and the official and public social media were most common social media types. Additionally, the scores for KAP toward COVID-19 vaccination ranging from 0 to 48 among college students were high (39.73 ± 5.58), lowest for knowledge domain (3.07 ± 0.76), and the highest for practice domain (3.47 ± 0.63). Female college students who were in good health status and who spent more time browsing social media, frequently used official and public social media, rarely used aggregated social media, and had a relatively strong self-perception of eHealth literacy and information acquisition of eHealth literacy were more likely to have high levels of KAP regarding COVID-19 vaccination.

conclusionsOverall, Chinese college students have excellent KAP toward COVID-19 vaccination. Based on the findings of this study, we recommend that health counseling regarding COVID-19 vaccination should target male students and those with inferior health status. Dissemination of health education regarding COVID-19 vaccination should be purposely conducted, and cooperation with official and public social media platforms should be promoted. Finally, eHealth literacy, which is one of the predictors of the level of KAP regarding COVID-19 vaccination, should be emphasized.

Indexed as

COVID-19Health LiteracySocial MediaTelemedicineAdolescentAdultChinaCOVID-19 VaccinesCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansMaleSARS-CoV-2StudentsVaccinationCOVID-19 Vaccinesand practices)attitudesCOVID-19 vaccinationeHealth literacyKAP (knowledgemedia usesocial media

Identifiers

PMID35155334
PMCPMC8829334
OpenAlexW4210387424

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.