SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12528-022-09344-z.
METHODS: An online cross-sectional survey design was applied, and a convenience sampling technique was used to recruit 1039 adult people from Pakistan. Data were collected from September 08 to October 12, 2021. The questionnaire consisted of a total of 32 questions in four parts assessing socio-demographics, as well as knowledge, attitudes, and practices regarding CCHF. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS), and logistic regression analyses were performed to determine the factors associated with good knowledge, positive attitudes, and good practices.
RESULTS: Alarmingly, 51.5% of participants heard about CCHF infection before administering the survey. Among these, 20.2%, 33.3%, and 48.2% of the study participants had demonstrated good knowledge, positive attitudes, and good practices, respectively. Binary logistic regression analysis revealed that education and income status had a significant impact on knowledge and attitudes (p<0.05). Similarly, the mean attitude scores differed significantly by age, education, and income status (p<0.05).
CONCLUSIONS: The findings reflected inadequate levels of knowledge, attitudes, and practices regarding CCHF among general people in Pakistan which may regard as lower than expected. As CCHF is a highly contagious disease, it's urgent to initiate a comprehensive approach to handle the situation before it spreads further in Pakistan.
METHODS: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models.
RESULTS: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset.
CONCLUSION: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
METHODS: A cross-sectional study was conducted from 21 February 2022 to 15 November 2022. Community pharmacists working in Klang Valley were given a self-administered questionnaire. This survey instrument facilitated the collection of information about their sociodemographic attributes, training background, and knowledge and attitude concerning medical marijuana. Through rigorous analysis of the accumulated data, discernible factors correlating with the levels of knowledge and attitudes surrounding medical marijuana were identified.
RESULTS: The majority (n=149, 53.8%) of participants had low knowledge of medical marijuana. Participants with lower knowledge of medical marijuana tend to have a negative attitude toward medical marijuana. Besides that, male participants showed higher knowledge of medical marijuana than female participants. Furthermore, it was found that atheists had the most negative attitude among other religions toward medical marijuana.
CONCLUSION: Most community pharmacists in Malaysia lack sufficient knowledge about medical marijuana. This indicates that Malaysian pharmacists are not future-ready and need to equip themselves with adequate knowledge of the indications and adverse effects of medical marijuana if it is to be legalised one day. Thus, there is a need for improved training and education of pharmacists around cannabis-based medicines.