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Background: Prehospital emergency staff usually encounter patients in situations that can affect the mental health of the medical staff and cause symptoms of depression, anxiety, and stress. This study aimed to determine depression, anxiety, and stress in prehospital emergency personnel during the COVID-19 epidemic in Ardabil City, Iran, 2020. Materials and Methods: A descriptive cross-sectional study was conducted from March 2020 to April 2020 with the participation of 138 working staff in the prehospital emergency department of Ardabil City. The samples were selected by the census method. Necessary information was collected with a two-part questionnaire: a demographic questionnaire and the DASS-21 standard questionnaire. DASS-21 is a 21-item questionnaire that consists of three subscales of 7 questions: depression, anxiety, and stress. The obtained data were analyzed using descriptive statistics, including mean and standard deviation, and inferential analysis, including analysis of variance, independent t test, and multiple regression using SPSS software v. 22 statistical software. Results: The results showed that 45.7% of the staff had moderate depression, 44.9% moderate anxiety, and 77.5% normal stress. There was a significant relationship between work experience and stress level (P=0.03). There were significant associations between age with depression (P=0.04), anxiety (P=0.00) and stress (P=0.01). There was also a significant relationship between gender and variables of stress (P=0.00) and anxiety (P=0.01). Multiple regression results showed that gender and education variables are predictors of anxiety and stress, and age and education variables are predictors of depression (P<0.05). Conclusion: More than half of the staff had moderate to severe depression and anxiety. Considering that prehospital emergency personnel has a vital role in improving and promoting people's health in the community, eliminating the underlying factors that cause emotional reactions in them is considered a health priority. © 2022, Negah Institute for Scientific Communication. All rights reserved.
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Background: Hospitals, as the most important medical institutions, must be adequately prepared before accidents to cope with emergencies and provide rapid response to disasters. This study aimed to determine the preparedness of hospitals in Ardabil Province during the COVID-19 pandemic. Materials and Methods: The present study is a cross-sectional study, and the study population included all hospitals in Ardabil Province, Iran (17 hospitals in total). The data collection tool was a standard checklist of 92 questions for COVID-19 Crisis Preparedness issued by the Iran Ministry of Health and Medical Education. The obtained data were analyzed using descriptive and inferential statistics (the Pearson correlation analysis) in SPSS software v. 22. Results: The mean relative score of preparation for the COVID-19 pandemic was 80.27% among the hospitals in the province. In the studied hospitals, the relative scores of preparedness were as follows: leadership and coordination, 92.64%, resource management, 94.36%;information management, 79.90%;communications, 77.94%;human resources, 77.69%;surge-capacity, 86.55%;rapid identification, 57.18%;diagnosis, 71.32%;isolation and patient management, 81.09%;and infection prevention and control, 84.05%. Conclusion: The level of hospital preparedness in most dimensions (9 out of 10 dimensions) was good, and only in the area of rapid identification was at a moderate level. However, further evaluation is needed at different stages of an epidemic. © 2022, Negah Institute for Scientific Communication. All rights reserved.
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This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.
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Introduction: In this research project we aim to assess the feasibility and practicability of using a robot to perform ultrasound. Especially during the current COVID-19 pandemic, robotic ultrasound when developed successfully can help us to perform ultrasound imaging of infectious patients while minimising the risk to our sonographers. Furthermore, robotic ultrasound system can reduce the musculoskeletal burden of sonographers while potentially obtaining better more even ultrasound pictures. Method: We are recruiting 60 patients to perform focused scans of the abdomen. This includes: liver, kidneys, ascites, gallbladder. Our study robot is uniquely a haptically enhanced robot, meaning it provides the sonographer (person who performs ultrasound) with force feedbacks throughout the scan making it easier to operate and safer. The research study will be conducted over a period of 3 months starting in February 2022. Results will be included at the time of presentation as we have only started the study. Conclusion: An innovative research project in which we reduce sonographer/ patient interaction in the cases of infections such as the COVID-19 pandemic.
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Following the discovery of the first instances of COVID-19 in nations and the subsequent announcement of a "pandemic" by WHO, worldwide efforts to identify efficient methods to combat COVID-19 began. One of the most effective solutions is to carry out widespread vaccination against the virus. Despite this, some members of the community refuse to be vaccinated. The present paper reviews the potential causes and factors correlating with people's hesitation to receive COVID-19 vaccines. This article is a narrative review paper. We searched PubMed, Scopus, and Web of Science databases using COVID-19, Vaccine, Acceptance, and Hesitancy keywords. Qualitative content analysis was performed and associated predictors with public vaccination acceptance were identified. According to the study, hesitation in receiving COVID-19 vaccines, regardless of the countries, is significant among females, lower ages, lower education level, doubt about efficacy, and concerns about the safety of the vaccines, history of not receiving vaccines, especially the influenza vaccine, distrust of regional or national health officials, low level of health literacy and lack of information, fear of side effects and other complications, doubt of pharmaceutical companies and fear of lobbying, presence of chronic underlying diseases and comorbidities, lower socioeconomic status and racial or religious minorities. According to the results, several factors can influence individuals' uncertainty about COVID-19 vaccines. Given the importance of vaccinating the majority of the community to achieve mass immunity, healthcare systems should consider the vaccine acceptance rate to be a vital and substantial factor.
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Background: In confronting coronavirus disease 2019 (COVID-19), informing the public through social media is one of the most important strategies. Objectives: The present study aimed to assess the knowledge level of social media users about COVID-19 and its challenges in Iran. Methods: This mixed-methods study was conducted in 2020. In the quantitative phase, the knowledge level of 299 social media users about COVID-19 was assessed in Khorramabad, Iran, using a questionnaire. Data were analyzed using the SPSS software ver-sion 23. The mean score of knowledge was measured utilizing the t-test and analysis of variance. In the qualitative phase, data collection was completed through semi-structured interviews. Purposive sampling was used, and ten faculty members and experts were interviewed. The content analysis method was used to analyze data using the MAXQDA10 software. Results: We observed that the mean knowledge score was 73.73 out of 100. Knowledge score had a significant relationship with educational level, field, and profession. Challenges of raising the knowledge of social media users included five themes: The nature of the disease, challenges related to users, stewardship challenges, the nature of social media, and problems related to domestic messengers. Conclusions: Proper and active management of social media, along with the decisive and effective presence of health system authorities in social media, can make this platform the most important source to the public for knowledge raising during the outbreaks of communicable diseases.
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Purpose: This study aims to identify health workforce challenges at Iranian hospitals during the COVID-19 pandemic. Design/methodology/approach: This was a conventional content analysis study conducted in 2020. The population consisted of the managers (heads of hospitals, managers and matrons) and staff (nurses, physicians, etc.) of eligible hospitals. The participants were selected using purposive sampling, and data saturation was achieved after 28 interviews. The data were analyzed in MAXQDA10. Findings: In total, 28 interviews were conducted with 10 women and 18 men. The challenges of hospital human resources were categorized into five main themes and 15 sub-themes. The main themes were the shortage of human resources, burnout, the need to acquire new knowledge and skills, the employees’ health and safety and the reward system. Originality/value: Identification of challenges faced by human resources is the first step toward preventing human force shortage and psychological problems in the personnel. Implementing the recommendations of the present study would assist the proper management of hospitals’ human resources. © 2022, Emerald Publishing Limited.
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In this paper, we analyze the covid-19 data set in two ways, The first one depends on the calculation of correlation coefficient via classical mathematical representation. And the second way of analysis depends on modern technique which is associated with copula function concepts and its relationship to measures of association. Afterwards, we compare the obtained results to decide far which is better in an analysis of the examined dataset.
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Background: This study aimed to evaluate the demographic features in patients with Coronavirus Disease-2019 (COVID-19) and to determine the relationship between Computerized Tomography (CT) and mortality. Objectives: This study aims to evaluate the characteristics for diagnosis and severity of involvement in primary imaging, their adaptation to the course of the disease, and their relationship with mortality. Methods: This retrospective study was conducted on the medical records of 212 patients with suspected COVID-19 admitted to the teaching hospitals of Shiraz University of Medical Sciences from February 20, 2009 to August 2020. The patients’ CT images were also assessed and the frequency of abnormalities was determined. Results: The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test was positive in 204 patients (99%). Consolidation was observed in all the 206 patients. The highest degree of lung involvement (90%) was observed in 69 patients (33.5%). Atoll sign was also diagnosed in 121 cases (58.7%). Besides, crazy-paving reticular lines, subpleural sparing, and bronchial distortion were observed in 129 (62.6%), 88 (42.7%), and 124 patients (60.2%), respectively. In addition, multi-segment was detected in the CT scan results of 194 cases (94.2%), which was higher compared to the single segment seen in 12 patients (5.8%). Conclusion: CT scan is a relatively sensitive technique for diagnosing COVID-19. The study results revealed a significant relationship between CT scan and death. The disease severity was also accurate using this method.
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Over the past year, COVID-19 has become a global pandemic and people across the globe have suffered a lot from this pandemic. The rate of transmitting the coronavirus in people is very quick. A rapid diagnosis can potentially help governments in identifying the pattern of transmission. There are some tests available but those tests take a long time to give the report. So, in this work, we have proposed a model that will distinguish between normal people, COVID affected people, and pneumonia affected people with the help of an X-ray. X-ray images are considered because taking an X-ray image is very little time-consuming. In this work, we have trained the X-ray images with a novel Deep Learning approach with SpinalNet architecture, and that distinguishes normal people, COVID affected people, and pneumonia affected people. After training the model we have achieved a very good accuracy for the SpinalNet architecture that is 96.12% while the traditional model provides 95.50% accuracy. We present precision, recall, and Fl scores of COVID and Pneumonia classes. We also present our results and codes with execution details. This paper contains the link to Kaggle notebooks with execution details. The applied Spinalnet transfer learning code is available in our GitHub repository: https://github.com/dipuk0506/SpinalNet
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Background and Aim: Patient safety is one of the basic principles of health care and the investigation of patient safety culture is a step towards providing safe conditions for patient care. The aim of this study was to evaluate the patient safety culture in nurses working in the care wards of patients with COVID-19 in Imam Khomeini Hospital in Ardabil, Iran.
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Purpose: The purpose of the study is to investigate the perceived stigma among residents of Sanandaj, west of Iran, following COVID-19 pandemic. Design/methodology/approach: This is a cross-sectional study conducted from March to April 2020. The sample consisted of 1,000 participants who live in Sanandaj. The data collection tool was a self-report electronic questionnaire. ANOVA and T-test were used to analyze the data. Findings: The mean perceived stigma for COVID-19 was 5.50±2.24 (IQR: 3.75–6.87) out of 10-point scale. The highest point was seen for perceived external stigma (6.73±2.49, IQR: 5–8.75) followed by disclosure stigma (4.95±3.92, IQR: 0–10). Interestingly, self-employers were more concerned about disclosing their illness than those with governmental jobs (25±3.93 vs. 4.31±4.14, P<0.05), and also had an overall higher stigma score;5.72±2.23 vs. 5.19±2.37, P<0.05). Originality/value: COVID-19 stigma is high among Iranians and more common among men, youngsters and self-employers. © 2020, Emerald Publishing Limited.