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The protection motivation theory for predict intention of COVID-19 vaccination in Iran: a structural equation modeling approach.
BMC Public Health. 2021 06 17; 21(1):1165.BP

Abstract

BACKGROUND

Many efforts are being made around the world to discover the vaccine against COVID-19. After discovering the vaccine, its acceptance by individuals is a fundamental issue for disease control. This study aimed to examine COVID-19 vaccination intention determinants based on the protection motivation theory (PMT).

METHODS

We conducted a cross-sectional study in the Iranian adult population and surveyed 256 study participants from the first to the 30th of June 2020 with a web-based self-administered questionnaire. We used Structural Equation Modeling (SEM) to investigate the interrelationship between COVID-19 vaccination intention and perceived susceptibility, perceived severity, perceived self-efficacy, and perceived response efficacy.

RESULTS

SEM showed that perceived severity to COVID-19 (β = .17, p < .001), perceived self-efficacy about receiving the COVID-19 vaccine (β = .26, p < .001), and the perceived response efficacy of the COVID-19 vaccine (β = .70, p < .001) were significant predictors of vaccination intention. PMT accounted for 61.5% of the variance in intention to COVID-19 vaccination, and perceived response efficacy was the strongest predictor of COVID-19 vaccination intention.

CONCLUSIONS

This study found the PMT constructs are useful in predicting COVID-19 vaccination intention. Programs designed to increase the vaccination rate after discovering the COVID-19 vaccine can include interventions on the severity of the COVID-19, the self-efficacy of individuals receiving the vaccine, and the effectiveness of the vaccine in preventing infection.

Links

PMC Free PDF

Authors+Show Affiliations

Ansari-Moghaddam A
Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
Seraji M
Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
Sharafi Z
Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
Mohammadi M
Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
Okati-Aliabad H
Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran. dr.okati@zaums.ac.ir.

MeSH

AdultCOVID-19COVID-19 VaccinesCross-Sectional StudiesHumansIntentionIranLatent Class AnalysisMotivationSARS-CoV-2Surveys and QuestionnairesVaccination

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

34140015

Citation

Ansari-Moghaddam, Alireza, et al. "The Protection Motivation Theory for Predict Intention of COVID-19 Vaccination in Iran: a Structural Equation Modeling Approach." BMC Public Health, vol. 21, no. 1, 2021, p. 1165.
Ansari-Moghaddam A, Seraji M, Sharafi Z, et al. The protection motivation theory for predict intention of COVID-19 vaccination in Iran: a structural equation modeling approach. BMC Public Health. 2021;21(1):1165.
Ansari-Moghaddam, A., Seraji, M., Sharafi, Z., Mohammadi, M., & Okati-Aliabad, H. (2021). The protection motivation theory for predict intention of COVID-19 vaccination in Iran: a structural equation modeling approach. BMC Public Health, 21(1), 1165. https://doi.org/10.1186/s12889-021-11134-8
Ansari-Moghaddam A, et al. The Protection Motivation Theory for Predict Intention of COVID-19 Vaccination in Iran: a Structural Equation Modeling Approach. BMC Public Health. 2021 06 17;21(1):1165. PubMed PMID: 34140015.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The protection motivation theory for predict intention of COVID-19 vaccination in Iran: a structural equation modeling approach. AU - Ansari-Moghaddam,Alireza, AU - Seraji,Maryam, AU - Sharafi,Zahra, AU - Mohammadi,Mahdi, AU - Okati-Aliabad,Hassan, Y1 - 2021/06/17/ PY - 2021/02/13/received PY - 2021/05/24/accepted PY - 2021/6/18/entrez PY - 2021/6/19/pubmed PY - 2021/6/25/medline KW - COVID-19 KW - Intention KW - Iran KW - Structural equation modeling KW - Vaccination SP - 1165 EP - 1165 JF - BMC public health JO - BMC Public Health VL - 21 IS - 1 N2 - BACKGROUND: Many efforts are being made around the world to discover the vaccine against COVID-19. After discovering the vaccine, its acceptance by individuals is a fundamental issue for disease control. This study aimed to examine COVID-19 vaccination intention determinants based on the protection motivation theory (PMT). METHODS: We conducted a cross-sectional study in the Iranian adult population and surveyed 256 study participants from the first to the 30th of June 2020 with a web-based self-administered questionnaire. We used Structural Equation Modeling (SEM) to investigate the interrelationship between COVID-19 vaccination intention and perceived susceptibility, perceived severity, perceived self-efficacy, and perceived response efficacy. RESULTS: SEM showed that perceived severity to COVID-19 (β = .17, p < .001), perceived self-efficacy about receiving the COVID-19 vaccine (β = .26, p < .001), and the perceived response efficacy of the COVID-19 vaccine (β = .70, p < .001) were significant predictors of vaccination intention. PMT accounted for 61.5% of the variance in intention to COVID-19 vaccination, and perceived response efficacy was the strongest predictor of COVID-19 vaccination intention. CONCLUSIONS: This study found the PMT constructs are useful in predicting COVID-19 vaccination intention. Programs designed to increase the vaccination rate after discovering the COVID-19 vaccine can include interventions on the severity of the COVID-19, the self-efficacy of individuals receiving the vaccine, and the effectiveness of the vaccine in preventing infection. SN - 1471-2458 UR - https://www.unboundmedicine.com/medline/citation/34140015/The_protection_motivation_theory_for_predict_intention_of_COVID_19_vaccination_in_Iran:_a_structural_equation_modeling_approach_ DB - PRIME DP - Unbound Medicine ER -