Background and Aims: During the COVID-19 pandemic, college students can access health-related information on the Internet to improve preventative behaviors, but they often judge the merits of such information and create challenges in the community. The aim of this study was to investigate information-seeking behaviors in regard to COVID-19 among students at Kerman University of Medical Sciences (KUMS) with the help of mass and social media.Methods: The present study is a cross-sectional study, which was conducted using an online researcher-made questionnaire. An invitation to participate in the study was sent to 500 students at KUMS, of which 203 were selected according to the inclusion criteria and completed the questionnaire. Descriptive statistics were used to analyze the data.Results: COVID-19 news was mostly obtained through social media platforms such as WhatsApp, Telegram, Instagram, radio, and television, as well as online publications and news agencies. Social media platforms such as WhatsApp, Telegram, Instagram, and satellite networks such as BBC contained the most rumors about COVID-19. Some of the most common misconceptions regarding COVID-19 were as follows: "COVID-19 is the deadliest disease in the world," "COVID-19 is a biological attack," and "COVID-19 disappears as the air temperature rises." In addition, most of the virtual training provided through mass media focused on "refraining from visiting holy places and crowded locations such as markets,""observing personal hygiene and refraining from touching the eyes, nose, and mouth with infected hands," and "the role of quarantine in reducing the incidence of COVID-19." Conclusion: Our findings demonstrated that during the pandemic, students used social media platforms the most to obtain health-related information and these media have a significant impact on their willingness to engage in preventative behaviors and take the COVID-19 risk seriously.
Background The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
Introduction: Epidemic diseases have always caused considerable physical and financial casualties for governments. By the end of the year 2019, the Covid19 pandemic emerged for the first time in China and rapidly infected the globe. As information technology plays a significant role in the current healthcare system, the aim of the present study was to conduct a systematic review to determine the role of electronic health in the Covid19 crisis.Material and Methods: This review was carried out on articles published from December 2019 until March 17th 2020 by searching keywords and their equivalents in "MeSH" in PubMed, Web of Science, and Scopus databases and Google search engine.Results: In total, from 72 found articles, 28 were recognized based on their research topic. After imposing inclusion and exclusion criteria, eventually 6 original articles and 8 reports were selected for further analysis. Results showed that reviewed articles had mentioned the effective role of IT in diagnosing Corona patients, addressing the spread of the disease, providing sufficient education for the public to prevent the disease, and recognizing high-risk areas. Telemedicine, machine learning algorithms, deep learning, Augmented intelligence, neural networks, Global positioning system, and geographical information system have been the most widely used technologies.Conclusions: It was shown that defeating the Covid19 is impossible without the help of technology. Experiences with the effectivity of using electronic health in controlling and monitoring the prevalence of Covid19 can be used to deal with other pandemic diseases in the future as well; and to avoid possible casualties and economic regressions while rapidly providing solutions for similar critical situations.
Background Anxiety disorder is more common in women than men. To some extent, it can be attributed to childbirth and factors related to pregnancy in women. Therefore, it is necessary for mothers to use valid and reliable scale to assess perinatal anxiety, such as the perinatal anxiety screening scale (PASS). The purpose of this study was to investigate the validity and reliability of the PASS in Persian language. Methods The PASS was translated into Persian (PASS-IR). Generally, 224 women antenatal and 125 postnatal answered the questions of PASS, EPDS-10, BAI and DASS-21 questionnaires. The data was collected in the health centers of Kerman by random sampling method. Finally, content validity, factor analysis, internal consistency and test-retest reliability were evaluated. Results The mean age of the participants was 32.89 years (range between 18 and 45 and SD = 6.23). More than half of the participating were at risk of severe anxiety (53.5%). Content Validity Index (CVI) and Content Validity Ratio (CVR) were 0.80 and 0.87. PASS-IR subscales include social anxiety and specific fears, general anxiety and adjustment, acute anxiety and trauma, and perfectionism and control. PASS-IR was significantly correlated with EPDS-10 (rho = 0.42), BAI (rho = 0.53), DASS-21 with three concepts of depression, anxiety and stress (rho = 0.51, rho = 0.49 and rho = 0.49), and adverse life events (rho = 0.30). Conclusion The results of this study show that PASS-IR has good validity and reliability. Therefore, it can be used to screen for anxiety disorder among Iranian women in the perinatal stage.
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