Knowledge, Attitude and Practice (KAP) Assessment on LifeStyle, Public Health Measures Practiced by Society and Their Impact to Contain COVID-19 Transmission Among Afar Community in Selected Parts of Afar Region, Ethiopia

Adugna, Biyansa; Merera, Olana; Negash, Wossen; Abdulkadir, Oumer; Yimer, Yasin Mohammed

doi:10.2147/idr.s349019

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Besides, this study demonstrated that educational level, which is generally considered related to knowledge, is an influential factor in the intention. Furthermore, knowledge has been extensively thought of as a critical component in the fight against COVID-19, 37 as individuals with high levels of knowledge are more likely to have protective motivation. 38,39 This study was the first investigation into the acceptance of RAST among the Chinese population.…”

Section: Discussionmentioning

confidence: 99%

The acceptance of SARS‐CoV‐2 rapid antigen self‐testing: A cross‐sectional study in China

Yuan

Li³

et al. 2022

Journal of Medical Virology

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Compared with the nucleic acid amplification test (NATT), the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) rapid antigen self‐testing (RAST) has advantages in speed and convenience. However, little is known about people's acceptance and influencing factors for SARS‐CoV‐2 RAST. A cross‐sectional study was conducted from April 21 to 30, 2022 in China. The χ2 test and multivariate logistic regressions were used to identify the influencing factors. The structural equation model was used to test the extended protective motivation theory (PMT) model hypotheses. Among the total of 5107 participants, 62.5% were willing to accept the SARS‐CoV‐2 RAST. There were significant differences in acceptance among different residences (p < 0.001), educational level (p < 0.001), occupation (p < 0.001), monthly income (p < 0.001), travel frequency (p < 0.05), and feelings about NATT (p < 0.001). Response efficacy (β = 0.05; p = 0.025) and self‐efficacy (β = 0.84; p < 0.001) had a positive effect, while response cost showed a negative effect (β = −0.07; p < 0.001). The public's major concerns about SARS‐CoV‐2 RAST are its reliability, testing method, price, and authority. Overall, a moderate intention to use SARS‐CoV‐2 RAST was found among the Chinese population. The extended PMT can be used for the prediction of intention to accept the RAST. We need to take measures to increase people's acceptance of SARS‐CoV‐2 RAST.

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Section: Discussionmentioning

confidence: 99%

The acceptance of SARS‐CoV‐2 rapid antigen self‐testing: A cross‐sectional study in China

Yuan

Li³

et al. 2022

Journal of Medical Virology

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“…This will prevent citizens from being misled by misinformation. Additionally, another study conducted in Ethiopia showed that public information is primarily obtained through television and radio (72.6%) ( 54 ). During such tough times, it is still necessary to assist the underprivileged population who lack access to information on official online platforms due to the lack of basic infrastructure.…”

Section: Discussionmentioning

confidence: 99%

Impact of the eHealth literacy, knowledge and attitudes on COVID-19 prevention behavior among residents in the second year of the COVID-19 pandemic: A cross-sectional study in Anhui Province, China

Yuan

Li²,

Zhang³

et al. 2022

Front. Public Health

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ObjectiveThe pandemic of COVID-19 continues to challenge people's health worldwide. In the second year of the pandemic, adherence to COVID-19 preventive behavior is key to continuing efforts to overcome the epidemic. This study aims to assess the COVID-19-related knowledge, attitude, and prevention behavior (KAP) and electronic health literacy (eHealth literacy) among Anhui residents in China.MethodsFrom January 30 to March 27, 2021, the cross-sectional study was performed among Anhui residents in China, including 16 cities. An online survey was adopted to assess KAP regarding COVID-19, and eHealth, involving a total of 2,122 citizens. Following informed consent, residents were recruited by convenience sampling. Frequencies and proportions were calculated. Additionally, Mann–Whitney U tests were used to analyze the variables. Independent predictors of preventive behavior of COVID-19 were ascertained using a multivariable logistic regression model.ResultResidents demonstrated good knowledge, positive attitudes, acceptable practices, and good eHealth literacy. Online news and WeChat are the main health information resources. Citizens who had good knowledge, a positive attitude, good eHealth, and did not participate in the online lectures or training COVID-19 were more likely to take preventive measures. Those with poor health, who were male, did not have family members working in health care facilities, and did not work in a face-to-face environment were less likely to take precautions. Compared with a master's degree and above, participants with middle school education level and below took preventive behavior sometimes. Residents who browse the COVID-19 webpage <15 min weekly seldom took preventive actions.ConclusionThe study showed that in the second year of the COVID-19 pandemic, Chinese residents had adequate knowledge of COVID-19, positive attitudes, appropriate preventive practices, and basic eHealth literacy. To prevent the rebound of the COVID-19 epidemic, the government and health agencies should inform citizens concerning which information channels or websites to use and assist the underprivileged population who lacks basic infrastructure. In addition, increasing the level of knowledge and attitude, enhancing eHealth literacy and the Health Belief Model (HBM), and implementing the Health Code were seen as ways to reinforce adherence to preventive behavior. Targeting men, implementing public awareness campaigns, community engagement strategies, and health education programs are recommended.

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Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Reisinezhad,

Fakhrahmad

2023

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PurposeQuestionnaire studies of knowledge, attitude and practice (KAP) are effective research in the field of health, which have many shortcomings. The purpose of this research is to propose an automatic questionnaire-free method based on deep learning techniques to address the shortcomings of common methods. Next, the aim of this research is to use the proposed method with public comments on Twitter to get the gaps in KAP of people regarding COVID-19.Design/methodology/approachIn this paper, two models are proposed to achieve the mentioned purposes, the first one for attitude and the other for people’s knowledge and practice. First, the authors collect some tweets from Twitter and label them. After that, the authors preprocess the collected textual data. Then, the text representation vector for each tweet is extracted using BERT-BiGRU or XLNet-GRU. Finally, for the knowledge and practice problem, a multi-label classifier with 16 classes representing health guidelines is proposed. Also, for the attitude problem, a multi-class classifier with three classes (positive, negative and neutral) is proposed.FindingsLabeling quality has a direct relationship with the performance of the final model, the authors calculated the inter-rater reliability using the Krippendorf alpha coefficient, which shows the reliability of the assessment in both problems. In the problem of knowledge and practice, 87% and in the problem of people’s attitude, 95% agreement was reached. The high agreement obtained indicates the reliability of the dataset and warrants the assessment. The proposed models in both problems were evaluated with some metrics, which shows that both proposed models perform better than the common methods. Our analyses for KAP are more efficient than questionnaire methods. Our method has solved many shortcomings of questionnaires, the most important of which is increasing the speed of evaluation, increasing the studied population and receiving reliable opinions to get accurate results.Research limitations/implicationsOur research is based on social network datasets. This data cannot provide the possibility to discover the public information of users definitively. Addressing this limitation can have a lot of complexity and little certainty, so in this research, the authors presented our final analysis independent of the public information of users.Practical implicationsCombining recurrent neural networks with methods based on the attention mechanism improves the performance of the model and solves the need for large training data. Also, using these methods is effective in the process of improving the implementation of KAP research and eliminating its shortcomings. These results can be used in other text processing tasks and cause their improvement. The results of the analysis on the attitude, practice and knowledge of people regarding the health guidelines lead to the effective planning and implementation of health decisions and interventions and required training by health institutions. The results of this research show the effective relationship between attitude, practice and knowledge. People are better at following health guidelines than being aware of COVID-19. Despite many tensions during the epidemic, most people still discuss the issue with a positive attitude.Originality/valueTo the best of our knowledge, so far, no text processing-based method has been proposed to perform KAP research. Also, our method benefits from the most valuable data of today’s era (i.e. social networks), which is the expression of people’s experiences, facts and free opinions. Therefore, our final analysis provides more realistic results.

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Knowledge, Attitude and Practice (KAP) Assessment on LifeStyle, Public Health Measures Practiced by Society and Their Impact to Contain COVID-19 Transmission Among Afar Community in Selected Parts of Afar Region, Ethiopia

Cited by 3 publications

References 13 publications

The acceptance of SARS‐CoV‐2 rapid antigen self‐testing: A cross‐sectional study in China

The acceptance of SARS‐CoV‐2 rapid antigen self‐testing: A cross‐sectional study in China

Impact of the eHealth literacy, knowledge and attitudes on COVID-19 prevention behavior among residents in the second year of the COVID-19 pandemic: A cross-sectional study in Anhui Province, China

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

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