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Introduction: Vaccination is an important strategy to prevent or reduce hospitalizations and mortality caused by COVID-19 infection. However, some people with chronic diseases are hesitant to get the COVID-19 vaccination. Objective: This study aimed to assess the acceptance of COVID-19 vaccination and associated factors among people with chronic diseases. Methods: A cross-sectional online survey was conducted between May and August 2021. A sample of 457 Thai adults living with one or more chronic diseases was drawn from a larger online survey. Results: Participants were 19 to 89 years old. The three most commonly reported chronic diseases were hypertension, diabetes, and obesity. The acceptance rate of COVID-19 vaccination was 89.1%. Forty-six percent of respondents had received the COVID-19 vaccination, and 43.1% intended to get the vaccine. Reasons for vaccine hesitancy/refusal included concerns about adverse side effects from the vaccines including long-term effects that might complicate their disease condition. Multiple logistic regression analyses revealed that having a bachelor's degree or higher [aOR 4.40; 95% CI: 2.12-9.14], being employed [aOR 2.11; 95% CI: 1.03-4.39], and having positive attitudes [aOR 2.36; 95% CI: 1.69-3.29] and negative attitudes [aOR 0.38; 95% CI: 0.27-0.55] predicted acceptance of the COVID-19 vaccination. Vaccine literacy was significantly associated with acceptance of COVID-19 vaccination in binary logistic regression analyses, but it was not retained in the multiple logistic regression model. Conclusion: Vaccine literacy and attitudes influence acceptance of COVID-19 vaccination in people with chronic diseases.
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Nurses are healthcare workers at high risk of contracting COVID-19 and are prioritized for COVID-19 vaccination. This study aimed to explore COVID-19 vaccine acceptance, vaccine literacy, and attitudes toward COVID-19 vaccines, and determine factors associated with COVID-19 vaccine acceptance among nurses in Thailand. A cross-sectional survey was conducted using online questionnaires between May and June 2021. A total of 510 nurses were recruited during the pandemic's third wave in Thailand. Data were analyzed using descriptive and inferential statistics. Ten percent (n = 51) of the participants were men, all of whom showed COVID-19 vaccine acceptance. Of the 459 female nurses, 94.8% (n = 435) accepted COVID-19 vaccination and 91.3% (n = 419) reported previous influenza vaccination. In multiple logistic regression models, previous influenza vaccination, interactive-critical vaccine literacy, and attitudes toward COVID-19 vaccines were significant predictors of COVID-19 vaccine acceptance among female nurses in Thailand. Those who had previously received influenza vaccination were more likely to accept COVID-19 vaccination. Higher scores for interactive-critical vaccine literacy and positive attitudes toward the COVID-19 vaccine increased the odds of accepting vaccination, while negative attitudes were associated with decreased vaccine acceptance. Vaccine literacy, together with attitudes toward the COVID-19 vaccine, had a strong positive effect on increasing vaccination acceptance and reducing vaccine hesitancy. The results suggest that policymakers should consider both attitudes and vaccine literacy when establishing prospective strategies for promoting vaccine acceptance among nurses beyond the COVID-19 pandemic.
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BACKGROUND: RamaCovid is a mobile health (mHealth) education system that provides the Thai population with information about COVID-19 and self-risk assessment. RamaCovid has a chatbot system that provides automatic conversations (available 24 hours per day) and a live chat function that allows users to directly communicate with health professionals (available 4 hours per day in the evening). The system consists of (1) COVID-19 vaccine information, (2) self-care after vaccination, (3) frequently asked questions, (4) self-risk assessment, (5) hospital finding, (6) contact number finding, and (7) live chat with a health professional. OBJECTIVE: This study investigates the use of and satisfaction with the RamaCovid system. METHODS: Overall, 400 people were recruited via RamaCovid by broadcasting an infographic about the study. Questionnaires collected demographic data, users' experiences of RamaCovid, and the use of and satisfaction with the system. The questions were answered using a 5-point Likert scale. Descriptive statistics were used to describe the participant characteristics and their use of and satisfaction with the RamaCovid system. The Mann-Whitney U test was performed to examine the difference in use and satisfaction between the adult and older adult groups. RESULTS: The participants showed high use of and satisfaction with the RamaCovid system. They used the information to take care of themselves and their family, and they gained information about their COVID-19 risk. The users were satisfied with the system because the information was easy to understand, trustworthy, and up to date. However, the older adult group had lower use of and satisfaction with the system compared to the adult group. CONCLUSIONS: RamaCovid is an example of the successful implementation of mHealth education. It was an alternative way to work with the call center during the COVID-19 pandemic and increased access to health information and health care services. Providing ongoing updated information, improving the attractiveness of the media information, and the age group difference are important issues for further system development.
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We use Fawcett and DeSanto-Madeya's framework to critique Fisher and Fisher's information-motivation-behavioral skills model and determine its usefulness and applicability for nursing. Our analysis and evaluation show that the model is a parsimonious and useful situation-specific theory for guiding nursing research and practice because it shows good fit with the nursing context, has social and theoretical significance, and exhibits empirical and pragmatic adequacy. More consistency in using terms referring to each concept would help to improve the model's internal consistency. The model's testability could also be improved by measuring health outcomes (eg, viral load or CD4 count) in future research.