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Journal of Health Research ; 37(5):280-288, 2023.
Article in English | Web of Science | ID: covidwho-2310130


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.

5th International Conference on Data Science and Information Technology, DSIT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2161387


Combatting misinformation is an important part of the global effort to fight against COVID-19. In this paper, we first present a large-scale, publicly available dataset named COVMIS for research on COVID-19 misinformation. COVMIS was constructed to support the misinformation identification approach that mimics the act of fact checking by human for truth labelling. COVMIS is collected from November 2019 to March 2021, this dataset contains 14, 384 claims (statements), 134, 320 related articles, and many features associated with the claims such as claimants, news sources, dates, truth labels (true, partly true or false) and justifications for the truth labels. Each claim is associated with a set of related articles that were collected from reputable sources and serve as the ground truth to assess the validity of the claim. We provide statistics and a detailed analysis of the dataset, and discuss a variety of its potential use cases. Using COVMIS, we then obtained new experimental results illustrating methods that can be used to significantly improve the performance of the fact checking approach for misinformation identification. © 2022 IEEE.