ABSTRACT
Background: Public health officers (PHOs) are the frontline health workforce against the Coronavirus disease 2019 (COVID-19) and therefore need high immunity for protection. The application of the capability, opportunity, motiva-tion, and behavior (COM-B) model aimed to 1) explore the level of COM-B for prevention and control of COVID-19, and 2) analyze the association between factors and behaviors for prevention and control of COVID-19 among PHOs at primary care units (PCUs) of seven provinces in southern Thailand. Methods: The study design performed an analytical cross-sectional study using information from primary care units from July to September 2021. Data collection used multi-stage sampling techniques to construct the online questionnaire based on the relationship of the COM-B model. Data analysis used descriptive statistics, and Chi-squared and Fisher's exact tests to find out the association among factors.Results: The overall COM-B scores of the 203 PHOs were high, but the motivation was low. Almost all characteristics were associated with behavior. Work experience was significantly associated with capability, opportunity, and behavior (P < 0.05). The relationships between capability and behavior, and opportunity and motivation were statistically sig-nificant (P < 0.05 and P < 0.001 respectively). Conclusions: This is the first report applying the COM-B model to explore behavior changes relating to the COVID-19 vaccination among PHOs at PCUs. The association between factors and individual behavior of health providers can be applied to design interventions for promoting effective preventive and controlling behavior after the COVID-19 vaccination.
ABSTRACT
The world has witnessed the outbreak of the Covid-19 epidemic. Mainstream and social media are playing an important role in Covid-19 pandemic prevention. This research explores awareness, communication channels and effectiveness of communication in the Covid-19 pandemic in rural areas of Thua Thien Hue province, Central Vietnam. Primary information was collected from 181 respondents, who are farmers, non-farmers and students. Secondary information was collected from reports and statistical data. Television, word of mouth and local loudspeakers are the main channels of mainstream media while social media mentions the role of Facebook and Zalo to transfer Covid-19 pandemic information. Mainstream media is still the main channel of farmers and old people while non-farmers and young people tend to access information through social media. Communication has significantly contributed to improving awareness and action of rural people in the Covid-19 epidemic prevention. © 2022 World Association for Triple helix and Future strategy studies. All rights reserved.
ABSTRACT
Covid-19 is a global disaster that needs computing power to analyze, predict and interpret. So far, there have been several models doing the job. With a huge amount of daily data, deep learning models can be trained to achieve highly accurate forecasts but theirmechanism lacks explainability. Epidemiological models, e.g. SIR, on the other hand, can provide insightful analyses, but they require appropriate parameter values, which might be complicated in certain locations. The fourth wave of the pandemic in Ho Chi Minh City (HCMC), Vietnam in 2021, brought valuable lessons along with accurate and specific data. Hence, we introduce an explainableAI model, known as BeCaked(+), to predict and analyze the pandemic situation efficiently from the collected data. BeCaked(+) combined deep learning and epidemiological models enhanced by specific parameters related to the policies endorsed by the government. Such a combination makes BeCaked(+) so accurate and a tool that provides information for policymakers to respond appropriately. One take a try BeCaked(+) at http://www.cse.hcmut.edu.vn/BeCaked.
ABSTRACT
By the end of March 2020, the COVID-19 epidemic were widespread in Thailand led to the closures of schools and universities. The home quarantine, social distancing, and other restrictions are likely to have psychological impact on students. This study aimed to estimate the prevalence of depression, anxiety, and stress and determine predictors among undergraduate public health students. A cross-sectional study was conducted at the School of Public Health-Walailak University. Research subjects included 463 students selected by a 2-stage random sampling. A research tool was a self-administered standardized 21-item depression, anxiety, and stress scale. An ordinal logistic regression analysis was executed to determine the predictors of depression, anxiety, and stress. The results showed that the prevalence of depression, anxiety, and stress was 24.8, 31, and 18.8%, respectively. Significant predictors of depression and anxiety were educational environment (OR = 0.35, 95% CI: 0.13-0.6;OR = 0.3, 95% CI: 0.2-0.7), and tense feelings during learning (OR = 3.57, 95% CI: 1.9-6.6;OR = 0.5, 95% CI: 0.2-0.8). Relationship with boy/girlfriends was considered a predictor of depression and stress (OR = 2.5, 95% CI: 1.2-4.9;OR = 0.5,95% CI: 0.2-0.9). Marital status of parent (OR = 15.5, 95% CI: 1.2-192.3) and family provision for learning (OR= 3.1, 95% CI: 1.3-7.6) were predictors of depression, and specialized majors (OR = 0.2, 95% CI: 0.1-0.7) was a predictor of stress. Noticeable worries about “My physical health”, “Health systems being overloaded”, and “Restricted liberty of movement” are also considered as predictors of depression, anxiety, and stress. The findings are needed to develop a good education environment and counseling services for cases having emotional problems. © 2022, Walailak University. All rights reserved.