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Background/Aims: The COVID-19 pandemic accelerated the implementation of telehealth and virtual care services. Clinicians must be comfortable using this technology in order for it to be developed effectively and implemented consistently. This study evaluated the influence of various factors, including those theorised in the technology acceptance model, on physicians' intention to use teleconsultations in their clinical practice in Chennai, India. Methods: A snowball sampling method was used to distribute an online survey to physicians in Chennai, India. The survey measured respondents' intention to use teleconsultations (dependent variable), along seven independent variables relating to this technology (perceived usefulness, perceived ease of use, physicians' attitudes, social influences, facilitating conditions, perceived compatibility with the clinical area and trust). A total of 165 responses were collected. Results were analysed using descriptive and correlational statistics, along with multiple linear regression. Results: All seven independent variables were found to be significantly associated with the dependent variable (P<0.01). Multiple linear regression analysis indicated that the independent variables accounted for 67.8% of the variance in respondents' intention to use teleconsultations. Conclusions: Physicians' intention to use teleconsultations is complex and multi-faceted. Although the factors theorised by the technology acceptance model were significantly associated with intention to use telemedicine, other factors were also found to be important, including social influences, external facilitating factors, perceived compatibility with the clinical area and personal trust in technology.
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Objective: This research was carried out as a descriptive and cross-sectional study between January and April 2021 in order to determine the relationship between COVID-19 fear and COVID-19 hygiene behaviors in adults. Materials and Methods: The sample of the research consisted of 1876 individuals who accepted to participate in the online survey with the snowball sampling method and completed the research completely. Research data were obtained with an introductory questionnaire, COVID-19 Fear Scale and COVID-19 Hygiene Behavior Scale. In the analysis of the data, number-percentage calculations, mean, standard deviation, independent t test and ANOVA, Pearson Correlation and Linear regression tests were used. Results: The mean score of the individuals on the COVID-19 Fear Scale was found to be 19.51±.89 and the total mean score of the COVID-19 Hygiene Behaviors Scale was found to be 105.35±18.61. The COVID-19 fear level of the participants;It was determined that gender, education status, working status, place of residence, socio-economic status, individual with chronic disease at home, anxiety due to COVID-19, change in hygiene behavior, thinking that hygiene will prevent COVID-19 explained 21.4%. Variables of age, gender, education status, employment status, marital status, family type, presence of children, place of residence, socioeconomic status, individuals with chronic diseases at home, anxiety due to corona, change in hygiene behavior, thinking that hygiene will prevent corona, COVID-19 Hygiene The level of their behavior;It was determined that 14% explained. Conclusion: As the COVID-19 fear levels of individuals increase, the COVID-19 hygiene behaviors also increas. Amaç: Bu araştırma, yetişkinlerde COVID-19 korkusu ile COVID-19 hijyen davranışları arasındaki ilişkiyi belirlemek amacıyla Ocak-Nisan 2021 tarihleri arasında tanımlayıcı ve kesitsel olarak yapılmıştır. Gereç ve Yöntem: Araştırma verileri, "Veri Toplama Formu”, "COVID-19 Korkusu Ölçeği” ve "COVID-19 Hijyen Davranış Ölçeği” ile elde edilmiştir. Verilerin çözümlenmesinde sayı-yüzde hesaplamaları, ortalama, standart sapma, bağımsız t testi ve ANOVA, Pearson Korelasyon ve Lineer regresyon testleri kullanılmıştır. Bulgular: Bireylerin COVID-19 Korku Ölçeği puan ortalaması 19.51±6.89, COVID19 Hijyen Davranışları Ölçeği toplam puan ortalaması 105.35±18.61 olarak bulundu. Katılımcıların COVID-19 korku düzeyinin;cinsiyet, öğrenim durumu, çalışma durumu, yaşadığı yer, sosyoekonomik durum, evde kronik hastalığı olan birey, COVID-19 nedeniyle endişe, hijyen davranışı değişikliği, hijyenin COVID-19'u engelleyeceğini düşünme değişkenlerinin %21.4'ünü açıkladığı saptanmıştır. Yaş, cinsiyet, öğrenim durumu, çalışma durumu, medeni durum, aile tipi, çocuk varlığı, yaşadığı yer, sosyoekonomik durum, evde kronik hastalığı olan birey, korona nedeniyle endişe, hijyen davranışı değişikliği, hijyenin koronayı engelleyeceğini düşünme değişkenlerinin, COVID-19 Hijyen Davranışları düzeyinin;%14'ünü açıkladığı belirlenmiştir. Sonuç: Bireylerin COVID-19 korku düzeyleri arttıkça, COVID-19 hijyen davranışları da artmaktadır.
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The nursing staff is more likely to experience drastic stress, which can compromise their physical and mental health and affect the quality of their work. This study aimed to assess the attachment type and affective temperament in connection to the psychological effects of the COVID-19 pandemic on psychiatric hospital nurses. We conducted a cross-sectional online survey utilizing a convenience sample population of 100 nurses who served on the front lines during the COVID-19 outbreak. The key findings showed that most nurses reported moderate attachment anxiety, moderate affective temperament, and mild psychological distress. It is crucial to offer nurses psychological support during COVID-19 through various channels.
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Background The coronavirus disease 2019 (COVID-19) pandemic has extensively affected the public physical and mental health, especially the medical staff working in the COVID-19 wards of hospitals. Objective This study aims to evaluate the relationship between post-traumatic stress disorder (PTSD), anxiety sensitivity (AS), and resilience among hospital medical staff in Rasht, during the COVID-19 pandemic. Methods This is an analytical cross-sectional study. The study population consists of all medical staff working in the COVID-19 wards of Poursina and Razi hospitals in Rasht, Iran. Of these, 94 participated who were selected using a convenience sampling method. Weathers et al.'s post-traumatic stress disorder checklist, Reiss et al.'s AS index, and Connor-Davidson resilience scale were used for data collection. The data were analyzed using Pearson correlation test and multiple linear regression analysis. Results There was a significant negative correlation between PTSD and resilience (r = -0.405, P = 0.001). and a significant positive correlation between PTSD and AS (r=0.633, P=0.001). The results of multiple analyses showed that resilience (B=-0.208, P=0.004) and AS (B=0.574, P=0.001) could significantly explain the PTSD in medical staff. Conclusion Resilience can be an important protective factor against PTSD in hospital medical staff during the COVID-19 pandemic. The medial staff with AS may experience the symptoms of PTSD more.
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The paper is devoted to the analysis of the spread of the COVID-19 pandemic in Ukraine based on finding the correlation between search terms in Google search engine and laboratory-confirmed cases. Statistics were obtained from open sources. The analysis was performed on matrices based on the Pearson correlation coefficient. To do this, we analyzed 25 typical search phrases, and after grouping them-7 remained. The data were reduced to the same discreteness. Correlation matrices were calculated for each wave of the pandemic and for altogether. As a result, the correlation between search phrases and laboratory-confirmed cases was observed only in the second and third waves of the pandemic. Moreover, in the first wave, the preconditions for its occurrence were found;in the second-Pearson's correlation coefficient was 0.74, and in the third wave, it decreased to 0.57. Other correlations that are specific to each pandemic wave are also analyzed. Additionally, it was proved that polynomials of the 6th degree most effectively restore lost data. © 2022 IEEE.
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Objective: This study was conducted to evaluate the views and attitudes of undergraduate nursing students regarding the distance education process during the COVID-19 pandemic period. Materials and Methods: The research was conducted between 16-25 July 2020 as a descriptive study. The research sample group consists of 434 nursing students. In the collection of data;"Personal Information Form" and "Online Learning Attitude Scale (OLAS)" were used. In the analysis of data;numbers, percentage distributions, mean and standard deviation values were calculated, and independent sample t test, oneway analysis of variance and Pearson correlation analysis were used for comparisons. Results: The mean score of the students participating in the research was 54.81±16.63, and it was found that the majority of the students were satisfied with distance education (75.6%) during the COVID-19 pandemic, received timely feedback about exams and homework in distance education (81.5%), and did not experience connection problems in distance education (81.5%). Among the students participating in the research, it was determined that the fourth grade students had a higher mean score of OLAS compared to the first grade, while those who preferred face-to-face education had a lower mean score of OLAS than those who preferred other education methods. It was determined that there was a positive correlation between the mean score of OLAS and age (r=0.153, p<0.05). At the same time, a significant negative correlation was found between the academic average of the students and the mean score of the OLAS (r=-0.159, p<0.05). Conclusion: Even though nursing students show an indecisive attitude towards online learning, it is seen that they want the hybrid education system. In further studies, it is recommended to evaluate students' views on both education systems together with their education outcomes.
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COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.
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Background-Covid-19 Pandemic has proved the Nurse's crucial role in health care delivery system and providing nursing care to critically ill patients. It is a challenge for nurses as they need to be astute, competent, compassionate and critical thinker when they have to take care of patients on mechanical ventilator. Aim-To assess knowledge and practices regarding care of patients on mechanical ventilator among nursing personnel before and after administration ofNursing Care Bundle (NCB) in experimental and comparison group. Material and method. A Quasi Experimental non Equivalent comparison group pretest post test design used in thus study. 65 nursing personnels (30 experimental and 35 comparison groups) were selected from hospitals of North India using convenience sampling technique. NCB was administered in experimental group. Structured knowledge questionairre, Structured Observation Checklist for practices was used to collect data before and after intervention. Results-The mean post test knowledge and practices scores of nursing personnel in experimental and comparison groups were (21.6 ± 3.84, 30.83 ± 4.51) and (17.54 ± 2.76, 19.54 ± 4.17) respectively. There was significant difference between mean pre test and post test knowledge and practices scores (p=0.00).There was statistically no significant correlation between post test knowledge and practices score [r=0.16 (0.39)] among nursing personnel in experimental group at the level of significance 0.05.There was significant association of selected variable in area of gender (0.02) in experimental and education (0.02) in comparison group with pre test knowledge scores , also there was a significant association of selected variable in area of gender in experimental (0.03) and present area of working (0.03) in comparison group with pre test practices score. Conclusion-Nursing Care Bundle was effective in improving knowledge and practices of nursing personnel.
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People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers' physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers' physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers' PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO2. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers' physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys' physical fitness prediction, and 99.26% for girls' physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers' physical fitness levels by their running PPG recordings.
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Running , Wearable Electronic Devices , Adolescent , Artificial Intelligence , Female , Humans , Male , Photoplethysmography/methods , Physical FitnessABSTRACT
River Damodar (India) is one of the most significant tropical large rivers and this river is carrying predominantly industrial effluents, urban sewage, and non-degradable chemical agricultural fertilizers. Several industries, cities, and townships directly depend on this important river throughout the year. It is highly essential to evaluate its surface water quality, characteristics, and improvement status during the COVID-19 lockdown and unlock phases. The major objectives of the present study are to analyse changing nature of heavy metals (Zn, Cd, Pb, Ni, Cr, and Fe) and microbial load (TVC, TC, and FC) of river water and to identify heavy metals impact on water quality and human health in pre, during, and after unlocking of COVID-19 lockdown. Here, a total of 33 water samples have been collected in the pre-lockdown, lockdown, and unlock phases. The results showed that decreasing trend of the microbial load was found in the lockdown phase. Heavy metal pollution index (HPI) illustrated that all samples are highly polluted (HPI > 150) during the pre-lockdown phase, while during the lockdown phase; HPI showed that around 54.54% of samples have been positively changed (low pollution level). During the unlock phase, 45.45% of samples were again amplified to the high pollution level. Pearson's correlation coefficient and hierarchical cluster analysis indicated strong relation among heavy metals with faecal coliform at a 0.05% level of significance. Noncarcinogenic hazard index (HI) shows the higher possibility of health risk (HI > 1) particularly for children in all the phases and during the lockdown phase, 36.36% of samples showed no possible health risk for adults (HI < 1). However, HI of dermal contact showed no possible health risk for children and adults in the assessment periods. This applied research can definitely assist planners and administrators in making effective solutions regarding public health.
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COVID-19 , Metals, Heavy , Water Pollutants, Chemical , Adult , Child , China , Communicable Disease Control , Environmental Monitoring , Humans , Metals, Heavy/analysis , Risk Assessment , Rivers , Water Pollutants, Chemical/analysisABSTRACT
Complex modeling has received significant attention in recent years and is increasingly used to explain statistical phenomena with increasing and decreasing fluctuations, such as the similarity or difference of spike protein charge patterns of coronaviruses. Different from the existing covariance or correlation coefficient methods in traditional integer dimension construction, this study proposes a simplified novel fractional dimension derivation with the exact Excel tool algorithm. It involves the fractional center moment extension to covariance, which results in a complex covariance coefficient that is better than the Pearson correlation coefficient, in the sense that the nonlinearity relationship can be further depicted. The spike protein sequences of coronaviruses were obtained from the GenBank and GISAID databases, including the coronaviruses from pangolin, bat, canine, swine (three variants), feline, tiger, SARS-CoV-1, MERS, and SARS-CoV-2 (including the strains from Wuhan, Beijing, New York, German, and the UK variant B.1.1.7) which were used as the representative examples in this study. By examining the values above and below the average/mean based on the positive and negative charge patterns of the amino acid residues of the spike proteins from coronaviruses, the proposed algorithm provides deep insights into the nonlinear evolving trends of spike proteins for understanding the viral evolution and identifying the protein characteristics associated with viral fatality. The calculation results demonstrate that the complex covariance coefficient analyzed by this algorithm is capable of distinguishing the subtle nonlinear differences in the spike protein charge patterns with reference to Wuhan strain SARS-CoV-2, which the Pearson correlation coefficient may overlook. Our analysis reveals the unique convergent (positive correlative) to divergent (negative correlative) domain center positions of each virus. The convergent or conserved region may be critical to the viral stability or viability; while the divergent region is highly variable between coronaviruses, suggesting high frequency of mutations in this region. The analyses show that the conserved center region of SARS-CoV-1 spike protein is located at amino acid residues 900, but shifted to the amino acid residues 700 in MERS spike protein, and then to amino acid residues 600 in SARS-COV-2 spike protein, indicating the evolution of the coronaviruses. Interestingly, the conserved center region of the spike protein in SARS-COV-2 variant B.1.1.7 shifted back to amino acid residues 700, suggesting this variant is more virulent than the original SARS-COV-2 strain. Another important characteristic our study reveals is that the distance between the divergent mean and the maximal divergent point in each of the viruses (MERS > SARS-CoV-1 > SARS-CoV-2) is proportional to viral fatality rate. This algorithm may help to understand and analyze the evolving trends and critical characteristics of SARS-COV-2 variants, other coronaviral proteins and viruses.
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ABSTRACT: This study exclusively focuses on spatial and temporal change of temperature and precipitation before and after COVID-19 lockdown and also examines the extent of their variation and the spatial relationship between them. Our main objective is to analyze the spatiotemporal changes of two climatic variables in Indian subcontinent for the period of 2015-2020. Monthly precipitation and temperature data are collected from NOAA and NASA for January to May month across the four zones (northeast, northwest, central, and peninsular zone) of India. To conduct a zone-wise statistical analysis, we have adopted statistical process control (SPC) methods like exponentially weighted moving average (EWMA) control charts, individual charts (I- Chart) to detect the shift in temperature and precipitation over the study period and Pearson correlation coefficient applied to measure the spatial association between the two variables. The findings revealed that temperature parameter has experienced a lot of positive and negative trends in the span of 6 years and detected a weak to moderate negative correlation in many parts of the country in April 2020 after 2016. This study also identified a weak negative correlation mainly in NE zone in 2020 after 2017. This research provides vital scientific contribution to the effects of monthly temperature and precipitation before and after COVID-19 pandemic lockdown.