The Impact of COVID-19 Lockdowns on Air Quality: A Systematic Review Study

Background: The purpose of this article was to review the published literature and evaluate the association between air quality/air pollution and the lockdown/stay-at-home orders during COVID-19 pandemic. Our goal is to identify the various environmental factors, such as urban and rural air quality, which were affected by the lockdown during the coronavirus disease (COVID-19) pandemic. Methods: We searched PubMed (2000–2021) for eligible articles using the following: (1) Aerosol[Title/Abstract], AND (2) air quality[Title/Abstract] OR air pollution[Title/Abstract] AND (3) COVID-19[Title/Abstract]. A total of 39 articles were identified through the search conducted in PubMed. We first screened the title and the abstract of those 39 articles for eligibility. A total of 24 articles did not meet the eligibility criteria and were excluded based on the title and the abstract review. The 15 remaining articles were assessed in full text for eligibility and data extraction. After a full-text review, 3 articles were excluded. Finally, a total of 12 selected articles were confirmed for data extraction. Results: Among the 12 studies, 5 articles focused on the effect of the air pollution, fine particulate matter, and air pollutants of COVID-19 pandemic’s lockdown, while 1 article targeted the relationship between the weather/air quality and COVID-19 death rate during lockdown. In addition, 5 papers focused on the association between the environmental factors, air pollution and air quality and COVID-19 mortality rate. Finally, 1 research study paper aimed to study the COVID-19 positivity rate and the effect of air quality during the stay-at-home order or the lockdown which was occurred in March 2020. It is important to note that it has been found that an increase in the average PM2.5 concentration was correlated with a relative increase in the COVID-19 test positivity rate. This explains the increase in the number of COVID cases during the period of the wildfire smoke from August to October 2020 (1). Conclusion: The findings indicate that the COVID-19 lockdown has significant impact on the air quality across the world. The lockdown significantly reduces the air pollutants such as NO2, CO, O3 and Particulate Matter PM2.5 and PM10. This reduction led to a much healthier and safer outdoor air and hence improved the air quality during the lockdown/stay-at-home orders. More research is needed to validate that the air pollutants (NO2, CO, O3, PM2.5 and PM10) have a significant impact on the COVID-19 mortality and fatality rates

The Impact of Hospital Demographic Factors on Total Quality Management Implementation: A Case Study of UAE Hospitals

Aim:  Maintaining service quality and value using quality and management tools is crucial in any organization. In essence, improving service quality boosts both efficiency of organizations and consumer pleasure. The deployment of quality development programs such as Total Quality Management (TQM) is one technique that businesses may employ to deliver exceptional customer service. The health sector, in particular, is one of the industries that require TQM adoption due to its complexity and the need for constant service improvement. TQM helps to improve service quality in health facilities through advanced clinical and administrative procedures. This research comprehensively assesses TQM levels and the impact of hospital demographics on its implementation process in hospitals in the United Arab Emirates (UAE).   Methods:  The study used a quantitative research strategy based on a survey study design. Questionnaires were used to gather primary data from respondents deployed a self-administered technique. 1850 questionnaires were delivered to the hospital's senior staff based on their number in each hospital. Of the 1850 questionnaires distributed, 1238 usable questionnaires were analyzed, yielding a response rate of 66.9%. The study used a binary logistic regression model to determine if hospital demographics affected TQM implementation. The study data were examined and analysed using version 25.0 of the SPSS software.   Results: The results show that most of the health facilities with an overall TQM between 4.12 and 4.82 were utilized, governmental, accredited and utilized and large hospitals, while the hospitals with a mean between 2.91 and 3.45 were small, unaccredited private, and non-specialised. Thus, large hospitals have a higher TQM utilization rate than small hospitals. In addition, the findings of the t-test revealed that a high TQM is represented by means of 4.68, 4.67, 4.43, and 4.12 for accredited, utilized, governmental and large hospitals. The binary regression analysis also reveals similar results: large, governmental, utilized and accredited hospitals have greater chances of TQM adoption than other categories of hospitals (Exp (B): 1.2; 95%CI: 1.001 – 1.421, P< .05); (Exp (B): 1.3; 95%CI: 1.012 – 1.721, P< .05); (Exp (B): 1.5; 95%CI: 1.127 – 2.051, P< .01); and (Exp   (B): 1.5; 95%CI: 1.102 – 2.012, P< .05); correspondingly. Another observation from the results is that hospitals that implemented technological tools had a greater chance of successfully executing the TQM program than hospitals that did not utilize advanced technologies due to the limited availability of resources (Exp (B): 1.7; 95%CI: 1.332 – 2.187, P< .01). Conclusion: Even though health facilities need to adopt TQM, its implementation depends on the hospital size and demographics that significantly influence the adoption of TQM programs. However, this study will help bridge the current gap on the usage of TQM in the health context by examine the influence of demographic factors on adopting TQM in hospitals. Hence, provide adequate information to help the UAE hospital administrators appropriately execute the TQM program in the hospitals and enhance the efficacy of their operations. &nbsp

The Impact of Hospital Demographic Factors on Total Quality Management Implementation: A Case Study of UAE Hospitals

Aim:  Maintaining service quality and value using quality and management tools is crucial in any organization. In essence, improving service quality boosts both efficiency of organizations and consumer pleasure. The deployment of quality development programs such as Total Quality Management (TQM) is one technique that businesses may employ to deliver exceptional customer service. The health sector, in particular, is one of the industries that require TQM adoption due to its complexity and the need for constant service improvement. TQM helps to improve service quality in health facilities through advanced clinical and administrative procedures. This research comprehensively assesses TQM levels and the impact of hospital demographics on its implementation process in hospitals in the United Arab Emirates (UAE).   Methods:  The study used a quantitative research strategy based on a survey study design. Questionnaires were used to gather primary data from respondents deployed a self-administered technique. 1850 questionnaires were delivered to the hospital's senior staff based on their number in each hospital. Of the 1850 questionnaires distributed, 1238 usable questionnaires were analyzed, yielding a response rate of 66.9%. The study used a binary logistic regression model to determine if hospital demographics affected TQM implementation. The study data were examined and analysed using version 25.0 of the SPSS software.   Results: The results show that most of the health facilities with an overall TQM between 4.12 and 4.82 were utilized, governmental, accredited and utilized and large hospitals, while the hospitals with a mean between 2.91 and 3.45 were small, unaccredited private, and non-specialised. Thus, large hospitals have a higher TQM utilization rate than small hospitals. In addition, the findings of the t-test revealed that a high TQM is represented by means of 4.68, 4.67, 4.43, and 4.12 for accredited, utilized, governmental and large hospitals. The binary regression analysis also reveals similar results: large, governmental, utilized and accredited hospitals have greater chances of TQM adoption than other categories of hospitals (Exp (B): 1.2; 95%CI: 1.001 – 1.421, P< .05); (Exp (B): 1.3; 95%CI: 1.012 – 1.721, P< .05); (Exp (B): 1.5; 95%CI: 1.127 – 2.051, P< .01); and (Exp   (B): 1.5; 95%CI: 1.102 – 2.012, P< .05); correspondingly. Another observation from the results is that hospitals that implemented technological tools had a greater chance of successfully executing the TQM program than hospitals that did not utilize advanced technologies due to the limited availability of resources (Exp (B): 1.7; 95%CI: 1.332 – 2.187, P< .01). Conclusion: Even though health facilities need to adopt TQM, its implementation depends on the hospital size and demographics that significantly influence the adoption of TQM programs. However, this study will help bridge the current gap on the usage of TQM in the health context by examine the influence of demographic factors on adopting TQM in hospitals. Hence, provide adequate information to help the UAE hospital administrators appropriately execute the TQM program in the hospitals and enhance the efficacy of their operations.     Conflict of interest: None declare

Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach

Metaverse (MS) is a digital universe accessible through a virtual environment. It is established through the merging of virtually improved physical and digital reality. Metaverse (MS) offers enhanced immersive experiences and a more interactive learning experience for students in learning and educational settings. It is an expanded and synchronous communication setting that allows different users to share their experiences. The present study aims to evaluate students’ perception of the application of MS in the United Arab Emirates (UAE) for medical-educational purposes. In this study, 1858 university students were surveyed to examine this model. The study’s conceptual framework consisted of adoption constructs including Technology Acceptance Model (TAM), Personal innovativeness (PI), Perceived Compatibility (PCO), User Satisfaction (US), Perceived Triability (PTR), and Perceived Observability (POB). The study was unique because the model correlated technology-based features and individual-based features. The study also used hybrid analyses such as Machine Learning (ML) algorithms and Structural Equation Modelling (SEM). The present study also employs the Importance Performance Map Analysis (IPMA) to assess the importance and performance factors. The study finds US as an essential determinant of users’ intention to use the metaverse (UMS). The present study’s finding is useful for stakeholders in the educational sector in understanding the importance of each factor and in making plans based on the order of significance of each factor. The study also methodologically contributes to Information Systems (IS) literature because it is one of the few studies that have used a complementary multi-analytical approach such as ML algorithms to investigate the UMS metaverse systems

Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis

The current study is based on an integrated research model developed by combining constructs from the Technology Acceptance Model (TAM) and other features affecting smartwatch effectiveness, such as content richness and user satisfaction (SAT). TAM is used to locate factors influencing the adoption of the smartwatch (ASW). Most importantly, the current study focuses on factors influencing smartwatch acceptance and use in the medical area, facilitating and enhancing the effective role of doctors and patients. The present study's conceptual framework examines the close association between two-term TAM variables of perceived ease of use (PEU) and perceived usefulness (PU) and the constructs of user satisfaction and content richness. It also incorporates the flow theory (EXP) to measure the effectiveness of the smartwatch. The study also uses the flow theory to assess involvement and control over ASW. The study used a sample of 489 respondents from the medical field, including doctors, nurses, and patients. The study employed a hybrid analysis method combining Structural Equation Modeling (SEM) and an Artificial Neural Network (ANN) based on deep learning. The study also used Importance-Performance Map Analysis (IPMA) to determine the relevance and performance of the variables influencing ASW. Based on the ANN and IPMA analyses, user satisfaction is the most crucial predictor of intention to use a medical smartwatch. Applying the structural equation model to the sample shows that SAT, PU, PEU, and EXP significantly influence intention to use a medical smartwatch. The study also revealed that content richness is an important factor that enhances users' PU. The current study could enable healthcare provider practitioners and decision-makers to identify factors for prioritisation and to strategise their policies accordingly. Methodologically, this study indicates that a “deep ANN architecture” can determine the non-linear associations between variables in the theoretical model. Overall, the study finds that smartwatches are in high demand in the medical field and are useful in information transmission between doctors and their patients

Technology acceptance drivers for AR smart glasses in the middle east : a quantitative study

This study aims to establish Middle East users' perspectives on the major factors that impact their decision to adopt Augmented Reality AR smart glasses (ARSG). Thus, an online questionnaire was designed and sent directly to the respondents, and 584 valid data points were collected from individuals living in the Middle East. The data were analyzed using Pearson correlations and Exploratory Factor Analysis (EFA) techniques using SPSS. Eleven hypotheses were tested using Multiple Regression analysis, where seven independent variables out of eleven were confirmed to have a significant impact on the perceived adoption of ARSG. The results indicate that four of the independent variables including Pre-Market Knowledge, Image, Own privacy and Technology innovativeness show the significant impact on ARSG adoption at the 1% significant level. In addition, the results indicate that three of the social and technological factors include Perceived Ease of use, Perceived usefulness and Other's privacy show the significant effect on ARSG adoption at the 5% significant level. Among the 7 social and technological factors, the results suggest that technology innovation expresses the strongest effect on ARSG adoption with the highest coefficient value of 0.413 (b = 0.413, t = 12.881, ρ < 0.01). Moreover, user intention is significantly impacted by gender and place of living but not by education or age. The research also provides pre-market insights on users' personal types that represent who will most likely adopt the new smart glasses and that differentiate them based on their priorities. To the best of our knowledge, this is among the first works to investigate technology acceptance drivers of AR smart glasses in the Middle East

Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach

An Electronic Medical Record (EMR) has the capability of promoting knowledge and awareness regarding healthcare in both healthcare providers and patients to enhance interconnectivity within various government bodies, and quality healthcare services. This study aims at investigating aspects that predict and explain an EMR system adoption in the healthcare system in the UAE through an integrated approach of the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) using various external factors. The collection of data was through a cross-section design and survey questionnaires as the tool for data collection among 259 participants from 15 healthcare facilities in Dubai. The study further utilised the Artificial Neural Networks (ANN) algorithm and the Partial Least Squares Structural Equation Modeling (PLS-SEM) in the analysis of the data collected. The study's data proved that the intention of using an EMR system was the most influential and predictor of the actual use of the system. It was also found that TAM construct was directly influenced by anxiety, innovativeness, self-efficacy, and trust. The behavioural intention of an individual regarding EMR was also proved to positively influence the use of an EMR system. This study proves to be useful practically by providing healthcare decision-makers with a guide on factors to consider and what to avoid when implementing strategies and policies

Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim: Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE). &nbsp; Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7). &nbsp; Results: Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P&lt;0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P&lt;0.05), (β= 0.571, P&lt;0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P&lt;0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P&lt;0.001) and (β= 0.416, P&lt;0.001), respectively. &nbsp; &nbsp; Conclusion: Conclusions made during the research add to the existing literature regarding technology adoption by demonstrating how adoption properties, perceived enjoyment, and personal innovativeness influence students’ perceptions concerning innovational technologies used in education. &nbsp; Conflicts of interest: None declared

Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim:&nbsp;Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE). Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7). Results:&nbsp;Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P&lt;0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P&lt;0.05), (β= 0.571, P&lt;0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P&lt;0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P&lt;0.001) and (β= 0.416, P&lt;0.001), respectively

Factors Affecting the Uptake of COVID-19 Vaccine amongDubai Airport's Professionals

Aim:&nbsp;Comprehending the elements that influence COVID-19 vaccination acceptability and recognizing expediters for vaccination decisions are critical components of developing effective ways to increase vaccine coverage in the general population. This study aims to investigate the main factors affecting COVID-19 vaccination uptake among Dubai 'Airport's employees. In addition, it seeks to explore the main signs and symptoms that appeared on vaccinated employees after taking the COVID-19 vaccination, hence, track the vaccine's safety. Methods:&nbsp;Employees at Dubai's airport in the United Arab Emirates (UAE), mainly in Dubai, provided data. To gather data online utilising the Google Forms platform, a questionnaire was used as the main quantitative tool. As 2000 questionnaires got distributed, 1007 employees participated in the survey, yielding a 50.4% response rate. Results:&nbsp;The results show that employees overwhelmingly agree with the assertion that the factors of accessibility and affordability have a significant effect on their decision to receive the COVID-19 vaccine, followed by a trust in vaccine, knowledge, vaccine safety, advice and information, and beliefs on the vaccine. In this study, the agreement level on factors affecting the COVID-19 vaccine uptake was found significantly to be higher in females (88.6%) who were married (91.6%) and those aged over 60 years (89.2%) at P &lt;.05. In addition, the results show that 53.7% of vaccinated staff was found to have one or more side effects of the vaccine, where none of them was hospitalized after immunization. The binary logistic regression analysis in this study shows that females were two times more likely to have 'vaccine's symptoms after vaccination than males (Exp (B): 1.6; 95%CI: 1.127 - 2.351, P&lt; .01). It further reveals that participants in the age group over 50 were three times more likely to have 'vaccine's symptoms after vaccination than participants in the age group 20-29 (Exp (B): 2.9; 95%CI: 2.497-9.681, P&lt; .001). Finally, it indicates that individuals with previous SARS-CoV-2 infection were 2 times more likely to have 'vaccine's symptoms after vaccination than those without known past infection (Exp (B): 1.9; 95%CI: 1.272 - 2.542, P&lt; .01). Conclusion:&nbsp;There are several factors that playing a significant role in population’s decision to receive the COVID-19 vaccine, where the accessibility and affordability factors were found to have the greatest effect on their decision to uptake the vaccine. The current study concluded that COVID-19 vaccination is safe and that adverse effects from a vaccine are usually modest and affected by several factors such as age, gender, and COVID-19 infection history. &nbsp