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Preventive vaccination against SARS-CoV-2 infection is currently receiving close attention in the Russian Federation. Improving public confidence in immunisation with new vaccines largely depends on a guarantee of the absence of side effects caused by contamination. A high risk of contamination is inherent to biological products, including coronavirus prevention vaccines, due to their properties and the nature of raw materials used. This risk adds to the need for using effective contaminant detection approaches. The aim of the study was to evaluate the possibility to improve sterility testing of preventive vaccines against SARS-CoV-2 infection. This article presents an analysis of the procedures proposed by pharmaceutical developers for sterility testing of ten Russian vaccines approved in the country for COVID-19 prevention. The authors considered specific characteristics of these vaccines, including their physical and chemical properties, the presence of antimicrobial components, and other critical factors affecting the correctness of the experimental setup. The results suggest that it is possible to improve sterility testing. According to the authors, the main directions for its improvement are the proposal to develop an alternative procedure based on compendial method 2 (OFS.1.2.4.0003.15, Ph. Rus. XIV), as well as the use of a universal culture medium. If used for refining the established procedures and developing new ones, the authors' recommendations will improve the reliability and applicability of sterility testing during both manufacturing and pre-approval regulatory assessment of updated coronavirus vaccines for subsequent release to the market. The proposed approaches can be applied to testing other medicinal products for sterility.Copyright © 2023 National Electronic-Information Consortium (NEICON). All rights reserved.
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Travel behavior is important in many fields, such as urban management and disaster management. Since the breakout of COVID-19, many people have changed their preference in travel, which is called travel behavior pattern, to respond to the impact of COVID-19. Understanding when, how, and why people change their travel behavior patterns is significant for antiepidemic and estimating the impact of COVID-19 on human society. However, most current studies ignore that travel behavior is multi-dimensions, and it can be a barrier to understanding travel behavior change. To fill up the vacuum of current research, we used an online Bayesian change detection method to detect individual travel behavior pattern change from big mobile trajectory data. For the low data quality problem caused by various and uneven, we design a novel Monte Carlo data grading framework to assess data quality and filter useable data and thus avoid unreliable results. The analysis result shows Tokyo experienced 6 phases of travel behavior change since 2020, and the change was driven by policies to some extent, especially in the frequency dimension and spatial dimension. Also, the correlation analysis indicates the correlation between four travel behavior dimension dimensions, and the infection number provides us with knowledge about how people will make a change in their travel behavior in the COVID-19 period. © 2023 Elsevier Inc. All rights reserved.
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Background: The outbreak of the COVID-19 pandemic, the constant transformation of the SARS-COV-2 virus form, exposure to substantial psychosocial stress, environmental change, and isolation have led to the inference that the overall population's mental health could be affected, resulting in an increase in cases of psychosis. Objective(s): We initiated a systematic review to determine the impact of the SARS-COV-2 virus and its long-term effects-in both symptomatic and asymptomatic cases-on people with or without psychosis. We envisioned that this would give us an insight into effective clinical intervention methods for patients with psychosis during and after the pandemic. Method(s): We selected fifteen papers that met our inclusion criteria, i.e., those that considered participants with or without psychiatric illness and exposed to SARS-COV-2 infection, for this review and were retrieved via Google, Google Scholar, MEDLINE, PubMed, and PsychINFO Database. Key Gap: There is a dearth of research in understanding how COVID-19 affects people with or without a prior personal history of psychosis. Result(s): The systematic review summary provides insight into the state of knowledge. Insights from the systematic review have also been reviewed from the salutogenesis model's perspec-tive. There is moderate evidence of new-onset psychosis during the COVID-19 pandemic in which some antipsychotics treated the psychotic symptoms of patients while treating for COVID-19. Suggestions and recommendations are made for preventive and promotive public health strategies. Conclusion(s): The Salutogenesis model and Positive Psychology Interventions (PPI) provide another preventive and promotive public health management approach.Copyright © 2023 Bentham Science Publishers.
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This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.
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In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.
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PurposeThere is an increasing interest in the supply chain's digitalization, yet the topic is still in the preliminary stages of academic research. The academic literature has no consensus and is still limited to research assessing the supply chain's digitalization of organizations. This study aims to explore the supply chain digitalization drivers to understand the emerging phenomena. More specifically, the authors devised from the literature the most common factors in assessing the readiness in scaling supply chain digitalization.Design/methodology/approachThis study followed a five-phased systematic literature review (SLR) methodology in this research: designing, analyzing, conducting, writing and assessing the quality of the review. The SLR is beneficial for justifying future research regardless of the complex process that requires dealing with high-level databases, information filtering and relevancies of the content. Through analysis of 347 titles and s and 40 full papers, the authors showed and discussed the supply chain digitalization: transformation factors.FindingsThe results generated three main themes: technology, people and processes. The study also generated ten subthemes/primary drivers for assessing the readiness for supply chain digitalization in organizations: IT infrastructure, cybersecurity systems, digitalization reskilling and upskilling, digitalization culture, top management support, digitalization and innovation strategy, integrated supply chain, digital innovation management, big data management and data analytics and government regulations. The importance of each factor was discussed, and future research agenda was presented.Research limitations/implicationsWhile the key drivers of the supply chain digitalization were identified, there is still a need to study the statistical correlation to confirm the interrelationships among factors. This study is also limited by the articles available in the databases and content extraction.Practical implicationsThis study supports decision-makers in understanding the critical drivers in digitalizing the supply chain. Once these factors are studied and comprehended, managers and decision-makers could better anticipate and allocate the proper resources to embark on the digitalization journey and make informed decisions.Originality/valueThe digitalization of the supply chain is more critical nowadays due to the global disruptions caused by the Coronavirus (COVID-19) pandemic and the surge of organizations moving toward the digital economy. There is a gap between the digital transformation pilot studies and implementation. The themes and factors unearthed in this study will serve as a foundation and guidelines for further theoretical research and practical implications.
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INTRODUCTION: The healthcare system is critical to the country's overall growth, which involves the healthy development of individuals, families, and society everywhere. This systematic review focuses on providing an overall assessment of the quality of healthcare delivery during COVID-19. METHODOLOGY: The literature search was conducted from March 2020 till April 2023 utilising the databases "PubMed," "Google Scholar," and "Embase." A total of nine articles were included. Descriptive statistics was performed using Microsoft Excel. PROSPERO registration ID- CRD42022356285. RESULTS: According to the geographic location of the studies included, four studies were conducted in Asia [Malaysia(n = 1); India (Madhya Pradesh) (n = 1); Saudi Arabia(n = 1); Indonesia (Surabaya) (n = 1)], three in Europe [U.K. (n = 1); Poland (n = 1); Albania (n = 1)] and two in Africa [Ethiopia(n = 1); Tunisia (n = 1)]. Overall patient satisfaction was found highest among studies conducted in Saudi Arabia (98.1%) followed by India (Madhya Pradesh) (90.6%) and the U.K. (90%). CONCLUSION: This review concluded five different aspects of patients satisfaction level i.e. reliability, responsiveness, assurance, empathy, and tangibility. It was found that the empathy aspect had the greatest value of the five factors, i.e., 3.52 followed by Assurance with a value of 3.51.
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COVID-19 , Humans , Reproducibility of Results , Asia , Patient Satisfaction , EthiopiaABSTRACT
BACKGROUND: European legislation defines as "near-patient testing" (NPT) what is popularly and in other legislations specified as "point-of-care testing" (POCT). Systems intended for NPT/POCT use must be characterized by independence from operator activities during the analytic procedure. However, tools for evaluating this are lacking. We hypothesized that the variability of measurement results obtained from identical samples with a larger number of identical devices by different operators, expressed as the method-specific reproducibility of measurement results reported in External Quality Assessment (EQA) schemes, is an indicator for this characteristic. MATERIALS AND METHODS: Legal frameworks in the EU, the USA and Australia were evaluated about their requirements for NPT/POCT. EQA reproducibility of seven SARS-CoV-2-NAAT systems, all but one designated as "POCT", was calculated from variabilities in Ct values obtained from the respective device types in three different EQA schemes for virus genome detection. RESULTS: A matrix for characterizing test systems based on their technical complexity and the required operator competence was derived from requirements of the European In Vitro Diagnostic Regulation (IVDR) 2017/746. Good EQA reproducibility of the measurement results of the test systems investigated implies that different users in different locations have no recognizable influence on their measurement results. CONCLUSION: The fundamental suitability of test systems for NPT/POCT use according to IVDR can be easily verified using the evaluation matrix presented. EQA reproducibility is a specific characteristic indicating independence from operator activities of NPT/POCT assays. EQA reproducibility of other systems than those investigated here remains to be determined.
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COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Reproducibility of Results , COVID-19/diagnosis , Point-of-Care Systems , Nucleic Acid Amplification TechniquesABSTRACT
Preventive vaccination against SARS-CoV-2 infection is currently receiving close attention in the Russian Federation. Improving public confidence in immunisation with new vaccines largely depends on a guarantee of the absence of side effects caused by contamination. A high risk of contamination is inherent to biological products, including coronavirus prevention vaccines, due to their properties and the nature of raw materials used. This risk adds to the need for using effective contaminant detection approaches. The aim of the study was to evaluate the possibility to improve sterility testing of preventive vaccines against SARS-CoV-2 infection. This article presents an analysis of the procedures proposed by pharmaceutical developers for sterility testing of ten Russian vaccines approved in the country for COVID-19 prevention. The authors considered specific characteristics of these vaccines, including their physical and chemical properties, the presence of antimicrobial components, and other critical factors affecting the correctness of the experimental setup. The results suggest that it is possible to improve sterility testing. According to the authors, the main directions for its improvement are the proposal to develop an alternative procedure based on compendial method 2 (OFS.1.2.4.0003.15, Ph. Rus. XIV), as well as the use of a universal culture medium. If used for refining the established procedures and developing new ones, the authors' recommendations will improve the reliability and applicability of sterility testing during both manufacturing and pre-approval regulatory assessment of updated coronavirus vaccines for subsequent release to the market. The proposed approaches can be applied to testing other medicinal products for sterility.Copyright © 2023 National Electronic-Information Consortium (NEICON). All rights reserved.
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Objective: To systematically evaluate the diagnostic value of nucleic acid test in sputum for COVID-19 and to determine the suitable population for sputum specimens. Method(s): PubMed, CNKI, Scopus, Web of Science, medRxiv and bioRxiv databases were searched for the diagnostic value of sputum nucleic acid test for COVID-19 from December 2019 to April 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias with QUADAS-2 in the included studies. We used sensitivity, specificity, AUC and DOR to evaluate the diagnostic value of sputum specimens. Result(s): A total of 25 studies were included, including 10,731 subjects. Meta-analysis results showed that: The combined sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), and area under operating characteristic curve (AUC) of sputum nucleic acid for the diagnosis of COVID-19 were 89.2% (95% CI, 86.6-91.4), 97.5% (95% CI, 97.2-97.8), 41.4 (95% CI, 11.7-145.9), 0.9474 (95% CI, 0.8964-0.9846). The results of subgroup analysis showed that the Asian group's DOR was 36.835 (95% CI, 10.83-134.570), and the Non-Asian group's DOR was 66.294 (95% CI, 0.719-6109.09). The DOR was 27.207 (95% CI, 2.860-258.780) in the OPS group and 44.165 (95% CI, 4.828-403.970) in the NPS group. DOR of mild patients was 84.255 (95% CI, 9.975-711.690), the DOR of the severe group was 14.216 (95% CI, 3.527-57.142) and was 19.464 (95% CI, 0.724-522.920) in the cured group. Conclusion(s): Current evidence shows that sputum nucleic acid test is of high diagnostic value for COVID-19. Study area and severity of disease are the influencing factors for the diagnostic accuracy of the sputum nucleic acid test. Due to the limitations on the number and quality of the included studies, the above conclusions need to be verified by more high-quality studies.Copyright © 2023, E-Century Publishing Corporation. All rights reserved.
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Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.
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The variation in ambient air pollution hampers indoor air quality (IAQ), and even the short-term variation is very hazardous for the exposed population. Technological interventions including sensors, smartphones and other gadgets are implemented to build smart environments. However, these interventions are still not fully explored in developing countries like India. The COVID-19 pandemic has made it very important to keep a tab on the air we breathe in as those already suffering from respiratory troubles are prone to fall victim to the deadly disease. In such a scenario, even a rise in pollution for a short duration is dangerous to the exposed pollution. Such short-term exposure facilitated by the meteorological creates a disaster for environmental health. The short-term rise in the concentration of pollutants makes things worse for the exposed people, even indoors. It is therefore critical to come up with a concrete solution to predict the IAQ instantly and warn the exposed population which can be only achieved by technological interventions and futuristic Internet of Things-based computational predictions. This chapter is intended to elaborate the health hazards linked to short-term rise in pollutants, which often goes unnoticed but has a critical impact and how with the help of IoT-based applications, the short-term variation can be predicted through different strategies. Similarly, the assessment of the health impact associated with short-term exposure to air pollution is also significant, and different exposure assessment models and computational strategies are discussed in the course of the study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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International Conference on Geospatial Science for Digital Earth Observation (GSDEO 2021)The international conference on "Geospatial Science for Digital Earth Observation” (GSDEO) 2021 was successfully held on a virtual platform of Zoom on March 26th and 27th, 2021. The conference was jointly organized by the Indian Society of Remote Sensing (ISRS), Kolkata chapter, and the Department of Geography, School of Basic and Applied Sciences, Adamas University. Due to the non-predictable behaviour of the COVID-19 second wave, which imposed restrictions on organizing offline events, the GSDEO (2021) organizing committee decided to organize the conference online, instead of postponing the event.Remotely sensed data and geographic information systems have been increasingly used together for a vast range of applications, which include land use/land cover mapping, water resource management, weather forecasting, environmental monitoring, agriculture, disaster management, etc. Currently, intensive research is being carried out using remotely sensed data on the geoinformatics platform. New developments have led to dynamic advances in recent years. The objective of the international conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) was to bring the scientists, academicians, and researchers, in the field of geo-environmental sciences on a common platform to exchange ideas and their recent findings related to the latest advances and applications of geospatial science. The call for papers received an enthusiastic response from the academic community, and over 100+ participants from 50+ colleges, universities, and institutions participated in the conference. In total 50+ research papers had been presented through the virtual Zoom conference platform in GSDEO 2021.The conference witnessed the presentation of research papers from diverse applied fields of geospatial sciences, which include the application of geoinformatics in geomorphology, hydrology, urban science, land use planning, climate, and environmental studies. There were four sessions namely, TS 1: Geomorphology and Hydrology, TS 2: Urban Science, TS 3: Social Sustainability and Land Use Planning, and TS 4: Climate and Environment. Each session was further subdivided, into two parts, namely Technical Session 1-A and 1-B. Each sub-session had been designed with one keynote speech and 5 oral presentations. Oral sessions were organized in two parts and offered through live and pre-recorded components based on the preference of the presenters. The presentation session was followed by a live Q&A session. The session chairs moderated the discussions. Similarly, poster sessions were organized in three parts and offered e-poster, live, and pre-recorded components. The best presenter of each sub-session received the best paper award.Dr. Prithvish Nag, Ex-Director of NATMO & Ex Surveyor General of India delivered the inaugural speech, and Dr. P. Chakrabarti, Former Chief Scientist of the DST&B, Govt. of West Bengal delivered a special lecture after the inaugural session. Eight eminent keynote speakers, Prof. S.P. Agarwal from the Indian Institute of Remote Sensing, Prof. Ashis Kumar Paul from Vidyasagar University, Prof. Soumya Kanti Ghosh from the Indian Institute of Technology, Kharagpur, Prof. L. N. Satpati from the University of Calcutta, Prof. R.B. Singh from the University of Delhi, Dr. A.K. Raha, IFS (Retd), Prof. Gerald Mills from the University College Dublin and Prof. Sugata Hazra from Jadavpur University enriched the knowledge of participants in the field of geoinformatics by their informative lectures. The presentations and discussions widely covered the various spectrums of geoinformatics and its application in monitoring natural resources like vegetation mapping, agricultural resource monitoring, forest health assessment, water, and ocean resource management, disaster management, land resource management, water and climate studies, drought vulnerability assessment, groundwater quality monitoring, accretion mapping and the use of geospatial sci nce in studying morphological, hydrological, and other biophysical characteristics of a region etc. Application of geoinformatics in predicting urban expansion, urban climate, disaster management, healthcare accessibility, anthropogenic resource monitoring, spatial-interaction mapping, and, sustainable regional planning were well-discussed topics of the conference.List of Committees, photos are available in the pdf.
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PurposeThe aim of this research is to empirically assess the nine dimensions of the Total Quality Management (TQM) model, which have been categorized into four blocks: the top management block, the supplier block, the process management block and the customer block. The nine dimensions represent key strategic activities of company performance. A comparative analysis of companies with ISO 9001 certification and those without certification in a developing country during the COVID-19 pandemic is carried out.Design/methodology/approachA survey was administered to the management of 259 Peruvian goods companies (in the mining, repair and manufacturing sectors) during the COVID-19 pandemic. The survey consisted of 35 Likert-scale items, which were grouped into the following nine TQM dimensions: Top management (leadership), quality planning, quality audit and assessment, product design, suppliers' quality management, process control and improvement, education and training, quality circles and focus on customer satisfaction. Then, Cronbach's alpha, the Kolmogorov–Smirnov test, the Mann–Whitney U test and means were computed for each of the dimensions. This analysis made it possible to estimate significant differences between ISO 9001 certified and non-certified goods companies in terms of the dimensions.FindingsThe results showed that, for ISO 9001 certified companies, the averages for all of the dimensions were significantly different from those of non-certified companies, except for the education and training dimension. ISO 9001 certified companies scored higher than non-certified companies in the TQM dimensions. For both certified and non-certified companies, the leadership dimension had the highest average and the quality circles dimension had the lowest average.Originality/valueThis study addresses two main gaps highlighted in the research on quality management: the application of Quality Management Systems (QMS) in developing countries like Peru, and the impact of ISO 9001 on the performance of goods companies during the COVID-19 pandemic.
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Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.
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Video conferencing, which includes both video and audio content, has contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic forced millions of people to work and learn from home. Global Internet traffic of video conferencing has dramatically increased Because of this, efficient and accurate video quality tools are needed to monitor and perceptually optimize telepresence traffic streamed via Zoom, Webex, Meet, etc.. However, existing models are limited in their prediction capabilities on multi-modal, live streaming telepresence content. Here we address the significant challenges of Telepresence Video Quality Assessment (TVQA) in several ways. First, we mitigated the dearth of subjectively labeled data by collecting similar to 2k telepresence videos from different countries, on which we crowdsourced similar to 80k subjective quality labels. Using this new resource, we created a first-of-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels. Our model achieves state-of-the-art performance on both existing quality databases and our new TVQA database, at a considerably lower computational expense, making it an attractive solution for mobile and embedded systems.
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Aim. To elaborate on the relationship between work engagement, perceived organizational support, and the turnover intention of nurses by analysing some potential moderators. Background. Nurses' turnover intention is negatively impacted by their level of work engagement and perceptions of organizational support. However, it is challenging to reach a consistent conclusion. Methods. Data were acquired from six electronic databases. Each study was evaluated using the quality assessment tool for cross-sectional studies of the Agency for Healthcare Research and Quality (AHRQ). STATA 15.0 was used to analyse the data, and a random effects model was used. The groups that included two or more studies were added to the moderator analysis. Results. A total of 40 study articles involving 23,451 participants were included. The turnover intention of nurses was inversely associated with work engagement (coefficient: −0.42) and perceived organizational support (coefficient: −0.32). A substantial moderating role was played by cultural background, economic status, working years, and investigation time (P<0.05). Conclusion. Work engagement and organizational support significantly reduced turnover intention among nurses. Considering the acute shortage of nurses worldwide, nurses with lower wages, fewer working years, and lower levels of work engagement should be given more attention and support from their organizations. Implications for Nursing Management. The meta-analysis suggested that managers should give their employees a more organizational support and promote their work engagement to motivate nurses' retention intention and maintain a stable workforce with little employee turnover.
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The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.