Evidence map›Paper›PMID 34714727›Full record

ArticleHuman vaccines & immunotherapeutics2021

Factors associated with COVID-19 vaccination acceptance among industrial workers in the post-vaccination era: a large-scale cross-sectional survey in China.

Di Yin, Hongbiao Chen, Zhaomin Deng, Yue Yuan, Musha Chen, He Cao, Xiaofeng Zhou, Jingwei Luo, Wei Zhang, Zihao Gu and 2 more

Open access · bronzeAbstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 19% 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

8 citing papers in PubMed, 13 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

12 authors at 2 institutions in 2 countries.

Di YinSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Hongbiao ChenDepartment of Epidemiology and Infectious Disease Control, Longhua Key Discipline of Public Health for the Prevention and Control of Infectious Diseases, Longhua Centre for Disease Control and Prevention, Shenzhen, China.
Zhaomin DengSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Yue YuanSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Musha ChenSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
He CaoDepartment of Epidemiology and Infectious Disease Control, Longhua Key Discipline of Public Health for the Prevention and Control of Infectious Diseases, Longhua Centre for Disease Control and Prevention, Shenzhen, China.
Xiaofeng ZhouDepartment of Epidemiology and Infectious Disease Control, Longhua Key Discipline of Public Health for the Prevention and Control of Infectious Diseases, Longhua Centre for Disease Control and Prevention, Shenzhen, China.
Jingwei LuoDepartment of Epidemiology and Infectious Disease Control, Longhua Key Discipline of Public Health for the Prevention and Control of Infectious Diseases, Longhua Centre for Disease Control and Prevention, Shenzhen, China.
Wei ZhangDepartment of Epidemiology and Infectious Disease Control, Longhua Key Discipline of Public Health for the Prevention and Control of Infectious Diseases, Longhua Centre for Disease Control and Prevention, Shenzhen, China.
Zihao GuDepartment of Epidemiology and Infectious Disease Control, Longhua Key Discipline of Public Health for the Prevention and Control of Infectious Diseases, Longhua Centre for Disease Control and Prevention, Shenzhen, China.
Ziyu WenSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Caijun SunSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.ORCID 0000-0002-2000-7053
Shenzhen Center for Disease Control and Prevention · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 pandemic continues to pose a huge threat to public health. Mass vaccination is needed to achieve herd immunity against SARS-CoV-2. Currently, several vaccines are being inoculated on a large-scale. The willingness of COVID-19 vaccination had been well investigated in the pre-vaccination era, but no reported data in the post-vaccination era yet.

methodsWe conducted a large-scale survey among industrial workers during the vaccination campaign in China. Chi-square test and rank sum test were used to identify differences for various intentions regarding COVID-19 vaccination. Univariate analysis and multivariate regression models were utilized to analyze the relationship among demographic factors, related influencing factors and acceptance of COVID-19 vaccination.

resultsA total of 23,940 industrial workers were included, 66.0% were willing to take COVID-19 vaccine, 16.6% were unwilling, and 17.4% were unsure. Participants were more likely to get vaccinated if they were male, aged 45-65, being good educated, married, or being recommended by doctors or nurses. Participants with strong risk perception of COVID-19 infection, strong confidence in COVID-19 vaccine, high attention to COVID-19 vaccine, good health status, bad health habit, and a history of vaccination within three months were also more likely to be vaccinated.

conclusionsThis study calls for more attention and health-related education among industrial workers to improve their acceptance of COVID-19 vaccination.

Indexed as

COVID-19COVID-19 VaccinesAgedChinaCross-Sectional StudiesHumansMaleMass VaccinationMiddle AgedPandemicsSARS-CoV-2VaccinationCOVID-19 VaccinesattitudeCOVID-19industrial workersvaccinewillingness

Identifiers

PMID34714727
PMCPMC8903899
OpenAlexW3209537154

What OpenQuestion holds

Textmetadata
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.