Evidence map›Paper›PMID 37993854›Full record

ArticleBMC public health2023

Determine the factors that affected COVID-19 prevention behaviors based on constructs of social cognition theory.

Raheleh Soltani, Mohsen Shamsi, Atefe Moradi

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

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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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

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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

3 authors at 1 institution in 1 country.

Raheleh SoltaniDepartment of Health Education and Health Promotion, School of Health, Arak University of Medical Sciences, Arak, Iran.
Mohsen ShamsiDepartment of Health Education and Health Promotion, School of Health, Arak University of Medical Sciences, Arak, Iran. mohsen_shamsi1360@yahoo.com.
Atefe MoradiDepartment of Health Education and Health Promotion, School of Health, Arak University of Medical Sciences, Arak, Iran.
Arak University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 is a universal challenge and novel disease is one of the core public health concerns. This study aimed to determine the factors that affected COVID-19 prevention behaviors (CPB) based on constructs of Social Cognition Theory (SCT).

methodsThis cross-sectional study was conducted on 426 participants referred to health centers of Arak from October 2021 to February 2022, and they were selected through a multi-stage random sampling method. Data were collected via a self-administered questionnaire which includes socio-demographic data (6 items), COVID-19 prevention behaviors (12 items), and SCT constructs related to COVID-19 (32 items). SPSS Ver-16 statistical software was used to analyze the data with one-way ANOVA, independent samples t-test, and multiple linear regressions. The significance level of α = 0.05 was considered for all tests.

resultsThe mean age was 37.8 years (SD = 12.2) and ranged from 17 to 81. According to the results, 51.2% had higher education and 52.6% of the participants were female. The mean (SD) of COVID-19 preventive behaviors was 43 (SD = 7.8) out of 60. There was statistical association between CPB and three constructs of SCT. Multiple regression showed that the outcomes expectations (β = 0.11, p < 0.001), self-regulation (β = 0.41, p < 0.001), and self-efficacy (β = 0.30, p < 0.001), age, gender, and history of COVID 19infection were associated with CPB and those described 61% of the variance of CPB.

conclusionRendering to the result of this study constructs of SCT is the key predictor of participants' CPB. Hence, based on these predictors, effective interventions and healthy messages could be designed based on this predictor-outcomes expectations, self-regulation, social support, and self-efficacy which can be beneficial to improve healthy behavior.

Indexed as

COVID-19AdultCross-Sectional StudiesFemaleHealth BehaviorHumansMaleSocial CognitionSurveys and QuestionnairesCOVID-19Prevention and controlSelf-regulationSocial cognition theory

Identifiers

PMID37993854
PMCPMC10664475
OpenAlexW4388907789

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.