Vaccinated or not? Survey on attitude toward 'approach-avoidance conflict' under uncertainty

Knowledge and Attitudes of Physicians and Nurses in Turkey Regarding Human Papillomavirus Vaccination of Their Children

AIM

This study aimed to determine the knowledge and attitudes of physicians and nurses as parents about human papillomavirus (HPV) vaccination and their views on vaccination in children.

MATERIALS AND METHODS

This cross-sectional study included 72 physicians and 128 nurses who had children. Data were collected using questionnaires prepared by researchers. Descriptive statistical analysis and chi-square tests were used for data analysis.

RESULTS

In this study, 84.7% of physicians and 70.3% of the nurses knew that HPV is a cancer factor, and two-thirds of the healthcare professionals believed that the HPV vaccine is protective. Moreover, 62.5% of physicians and 74.2% of nurses reported that they did not intend to vaccinate their children. The reasons for vaccination hesitancy of healthcare professionals was believing it was unnecessary, thinking it was expensive, having insufficient knowledge about vaccine, thinking it may have side effects, and not trusting the vaccine. In this sample, 70.8% of physicians and 53.9% of nurses stated that they could have their children vaccinated only if the HPV vaccines were in the national vaccination schedule.

DISCUSSION

Further studies should be conducted to include the HPV vaccine in the childhood national vaccination program to reduce vaccine hesitancy.

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

Factors associated with intention to take COVID-19 vaccine among the university students in Bangladesh

Purpose

This study examined the Bangladeshi university students' intention to take coronavirus disease 2019 (COVID-19) vaccines by assessing the Health Belief Model and Theory of Planned Behavior.

Materials and Methods

University students were queried on their intention to take COVID-19 vaccines. The sample used in this study (n=310) was obtained through an online survey among university students (age, 18-25 years old) from August 7 to September 18, 2021.

Results

Although over 90% of respondents showed their willingness to take the COVID-19 vaccine, around 37.3% of respondents still desired to wait and see to take the vaccine. The most frequently cited reasons for vaccines hesitancy were concerns over side effects (62.4%). There is still an unmet need for adequate information on COVID-19 vaccines (81.4%). Results of the binary logistic regression model showed that students from private universities (odds ratio [OR], 0.19; 95% confidence interval [CI], 0.04-0.97), respondents who concerned about the vaccine safety (OR, 0.07; 95% CI, 0.01-0.44) and side effects of vaccine (OR, 0.21; 95% CI, 0.05-0.89) were less willing to take COVID-19 vaccine. On the other hand, desire to wait to take COVID-19 vaccines was associated with marital status (OR, 7.76; 95% CI, 1.50-40.27); COVID-19 preventive behavior, including use of facemask (OR, 0.25; 95% CI, 0.09-0.70) and maintain social distance (OR,1.75; 95% CI, 1.00-3.07); COVID-19 infection (OR, 0.50; 95% CI, 0.26-0.99); provide more information on vaccines (OR, 2.32; 95% CI, 1.06-5.09); the perceived side effects (OR, 2.82; 95% CI, 1.54-5.17); and effectiveness of COVID-19 vaccines (OR, 2.41; 95% CI, 1.16-5.01).

Conclusion

Public health managers should provide adequate information on COVID-19 vaccines to address the concerns about the safety and side effects of the vaccines.

Opt-out policy and its improvements promote COVID-19 vaccinations

Rationale

Vaccination uptake is a major strategy to prevent infection with SARS-CoV-2 and curb the transmission of COVID-19. However, many people remain unwilling to receive the COVID-19 vaccine.

Objective

Using default nudges, the present study examines (a) whether opt-out policy and its improvements could increase intention and attitude to get vaccinated and (b) whether these default effects differ across diverse risk-perception groups.

Method

An online experiment with 1926 unvaccinated Chinese respondents was conducted in February 2021. We measured willingness to be vaccinated after informing opt-in policy, standard opt-out policy, and its five improvements (opt-out education, opt-out opportunity, opt-out social norm, opt-out feedback, and opt-out opportunity). Risk perception of the pandemic of COVID-19 and vaccination were also measured.

Results

(a) Opt-out policy and its improvement (except the opt-out transparency) increased intentions to be vaccinated. Policies with a vaccination default did not weaken people's attitude toward policy and policymakers compared with the opt-in policy, but participants in the transparent improvement group reported lower freedom of choice than those in the opt-out group. (b) Further latent profile analysis revealed four classes underlying risk perception: risk exaggerators, risk deniers, disease-specific risk perceivers, and vaccine-specific risk perceivers. But there was no conclusive evidence that the effect of risk perception differs as a function of defaults.

Conclusions

These findings provide new psychological evidence for formulating more targeted vaccination policies and highlight the importance of risk perception to understand vaccination intentions.