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How does the belief that vaccination will end the COVID-19 pandemic relate to vaccination intent?

In a recent study published in the Emerging Infectious Diseases journal, researchers explored intent and belief in coronavirus disease 2019 (COVID-19) vaccination in the Netherlands. 

Study: COVID-19 Vaccination Intent and Belief that Vaccination Will End the Pandemic. Image Credit: Melinda Nagy/Shutterstock

The COVID-19 vaccines have played an indispensable role in curbing the infections and mortality caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the efficacy of the vaccines is majorly dependent on the high and equal distribution of vaccine uptake in a population. 

About the study

In the present study, the researchers employed mental models to explore the beliefs underlying and intentions toward COVID-19 vaccination.

The team conducted a survey between 12 March and 22 March 2021 when 1.5 million out of 17.5 million Netherlands residents were either partly or fully vaccinated against SARS-CoV-2 infections. The online survey was sent to 6810 individuals aged 18 years and above. The sample chosen for the survey was deemed as representative of the general Dutch population according to demographic characteristics.

Vaccination intention was assessed as follows. All the participants who were not vaccinated despite receiving an invitation for COVID-19 vaccination were asked ‘Do you want to get vaccinated against the coronavirus?’ while the respondents who reported that they did not receive an invitation for COVID-19 vaccination were questioned ‘If you are invited for a COVID-19 vaccination, do you then want to get vaccinated?’ The participants were required to answer the questions on a 5-point Likert scale wherein the points indicated 1. Certainly not; 2. Probably not; 3. Don’t know; 4 Probably yes; 5. Certainly yes.  

The beliefs pertaining to COVID-19 and vaccination were assessed by identifying major elements present in the mental models underlying the vaccination intentions. The beliefs were analyzed with the question: “We would like to know what you think about the coronavirus/vaccination against the coronavirus. For each statement, indicate to what extent it aligns with what you think. I think ….” This question was followed by a total of 25 statements which were scored based on the 5-point Likert scale which could be classified into seven elements of mental modes of a person: (1) beliefs related to COVID-19 risk to oneself and one’s loved ones, (2) safety of COVID-19 vaccination, (3) effectiveness of vaccination, (4) social benefits related to the vaccination, (5) alternatives to vaccination, (6) social norms associated with vaccination behavior, and (7) accessibility of vaccination.

The team also assessed the extent to which the beliefs were responsible for the variation in vaccination intentions and identified the specific beliefs that determined vaccination intentions. This was achieved by performing a regression analysis using Random Forest (RF), a machine learning method that facilitated regression and classification according to an ensemble of decision trees.

A total of four types of outputs were taken into account after the RF analysis: (1) variable importance ranking (VIR) which ranked control and independent variables, (2) particle dependence which indicated the extent and the direction of association between the dependent and independent variables, (3) cumulative variance explained which represented the variance after the addition of an independent variable to the VIR, and (4) total variance explained. 


The study results showed that 62.5% of the unvaccinated participants answered that they would certainly receive COVID-19 vaccination and 17.8% reported that they would probably want to receive a COVID-19 vaccination. However, 7.1% of the participants reported that they did not know yet if they would get vaccinated, 5.9% would probably not want to receive the vaccine, while 6.8% revealed that they certainly would not get vaccinated against COVID-19. The team noted that the average response with respect to vaccination intention was 4.2.

Statistical analysis showed that all 25 beliefs were substantially associated with vaccination intentions. Correlations between COVID-19 vaccination and the related beliefs regarding COVID-19 had moderate to strong associations between different risk perception beliefs related to COVID-19. Moreover, the team observed strong associations between COVID-19 vaccination and beliefs related to the safety of COVID-19 vaccination.

Furthermore, 27.7% of the participants indicated that they did not believe that the adverse effects associated with the COVID-19 vaccination were well-researched while 28.3% opined that the COVID-19 vaccines were developed too quickly. With respect to vaccine effectiveness, while the participants believed that the vaccines would effectively protect them against COVID-19, they were unsure if the vaccine would be effective only for a short duration.

Overall, the study findings showed that beliefs associated with COVID-19 explained the wide variation in COVID-19 vaccination intentions.  

Journal reference:
  • de Vries M, Claassen L, Lambooij M, Leung KY, Boersma K, Timen A. (2022). COVID-19 vaccination intent and belief that vaccination will end the pandemic. Emerging Infectious Diseasesdoi:

Posted in: Medical Science News | Medical Research News | Disease/Infection News

Tags: Coronavirus, Coronavirus Disease COVID-19, covid-19, Efficacy, Infectious Diseases, Machine Learning, Mortality, Respiratory, SARS, SARS-CoV-2, Severe Acute Respiratory, Severe Acute Respiratory Syndrome, Syndrome, Vaccine

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Written by

Bhavana Kunkalikar

Bhavana Kunkalikar is a medical writer based in Goa, India. Her academic background is in Pharmaceutical sciences and she holds a Bachelor's degree in Pharmacy. Her educational background allowed her to foster an interest in anatomical and physiological sciences. Her college project work based on ‘The manifestations and causes of sickle cell anemia’ formed the stepping stone to a life-long fascination with human pathophysiology.

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