Evidence map›Paper›PMID 35162542›Full record

ArticleInternational journal of environmental research and public health2022

Knowledge of COVID-19 and Its Relationship with Preventive Behaviors and Vaccination among Adults in Northern Thailand's Community.

Tharadon Pothisa, Parichat Ong-Artborirak, Katekaew Seangpraw, Prakasit Tonchoy, Supakan Kantow, Nisarat Auttama, Sorawit Boonyathee, Monchanok Choowanthanapakorn, Sasivimol Bootsikeaw, Pitakpong Panta and 1 more

Open access · goldAbstract 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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.8field-weighted citation impact, top 10% 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

12 citing papers in PubMed, 17 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

11 authors at 2 institutions in 1 country.

Tharadon PothisaSchool of Medicine, University of Phayao, Phayao 56000, Thailand.
Parichat Ong-ArtborirakFaculty of Public Health, Chiang Mai University, Chiang Mai 50200, Thailand.
Katekaew SeangprawSchool of Public Health, University of Phayao, Phayao 56000, Thailand.ORCID 0000-0002-7184-381X
Prakasit TonchoySchool of Public Health, University of Phayao, Phayao 56000, Thailand.
Supakan KantowSchool of Public Health, University of Phayao, Phayao 56000, Thailand.
Nisarat AuttamaSchool of Public Health, University of Phayao, Phayao 56000, Thailand.
Sorawit BoonyatheeSchool of Medicine, University of Phayao, Phayao 56000, Thailand.ORCID 0000-0001-8056-5359
Monchanok ChoowanthanapakornSchool of Public Health, University of Phayao, Phayao 56000, Thailand.
Sasivimol BootsikeawSchool of Public Health, University of Phayao, Phayao 56000, Thailand.
Pitakpong PantaSchool of Nursing, University of Phayao, Phayao 56000, Thailand.
Dech DokpuangSchool of Allied Health Sciences, University of Phayao, Phayao 56000, Thailand.ORCID 0000-0002-2980-3937
University of Phayao · THChiang Mai University · TH

Funding

University of Phayao MD65-01
6 · The paper itself

Abstract

(1) Background: the 2019 coronavirus disease outbreak (COVID-19) has posed a major threat to public health and had a significant impact on all areas of people's lives. Vaccines against COVID-19 have been developed to control the disease, and an array of personal hygiene measures has been introduced. As a result, information that will support and promote vaccination among populations as well as other health measures against COVID-19 are urgently needed. The goal of this research was to look into the knowledge about COVID-19 and how it relates to preventive behaviors and vaccination among people living in rural areas of northern Thailand. (2) Methods: a cross-sectional study was performed in four upper northern provinces of Thailand. A total of 1524 participants were recruited using the probability sampling technique. Questionnaires were distributed to collect data on general health information, as well as knowledge and preventive behaviors regarding COVID-19. (3) Results: more than half (55.9%) of the participants were female and had not received the COVID-19 vaccine (67.2%). Their mean age was 44.13 years. The majority had moderate COVID-19 knowledge and engaged in preventive behaviors (65.5% and 42.0%, respectively). A linear regression model showed that the COVID-19 knowledge score was related to the COVID-19 preventive behaviors score, with a standardized coefficient of 0.510, after adjusting for age, underlying disease, and body mass index (B = 2.64; 95%CI = 2.42, 2.87). Binary logistic regression revealed that after controlling for age, education, occupation, financial status, and current disease (AOR = 1.87; 95%CI = 1.64-2.13), the score of COVID-19 knowledge was significantly associated with having the COVID-19 vaccine. (4) Discussion: knowledge of COVID-19 is very important for people in rural regions to engage in COVID-19 prevention behaviors and vaccination. Relevant government agencies and health network partners should support proactive education campaigns emphasizing the risk of contracting the disease and its severity in order to promote vaccination against COVID-19 among unvaccinated groups. These campaigns can highlight COVID-19's positive benefit-risk balance in the short and long term and ensure public safety measures against COVID-19.

Indexed as

COVID-19COVID-19 VaccinesAdultCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansSARS-CoV-2Surveys and QuestionnairesThailandVaccinationCOVID-19 VaccinesadultsCOVID-19 vaccineknowledgenorthern Thailandpreventive behaviors

Identifiers

PMID35162542
PMCPMC8834673
OpenAlexW4210303860

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.