Investigating the Impact of Organisational Culture and Leadership on Knowledge Sharing Behavioural Intention Among Employees in Organisations in the United Arab Emirates

This study seeks to obtain a better understanding of the factors influencing employees’ knowledge sharing behavioural intentions within the Emirati organisational context. While the literature provides some examples of studies on the subject in Western countries and Asia, there has been a lack of research around the topic in the Middle East, Gulf Cooperation Council (GCC) countries and the United Arab Emirates (UAE). Some organisations have placed a lot of emphasis on innovation and technology and forgotten what (ultimately) makes their business really successful – the human factor. The study followed a mixed methodology approach; the quantitative method was the primary approach and qualitative methods were employed as a complementary technique to deepen the understanding of some of the quantitative data results. The theoretical foundation of this thesis is based on the theory of reasoned action (TRA) and the theory of planned behaviour (TPB). These theories are widely used in social psychology to explain many human behaviours. The model therefore is developed based on the latest evolution of the TRA and TPB framework as well as additional factors highlighted in the literature. Eleven variables were tested to examine their impact on the intention to share knowledge in an organisational context. Primary data were obtained from a questionnaire administered to three large government organisations in the UAE: of 1073 questionnaires, 881 were usable. A total of 21 (including the pilot interviews) semi-structured interviews were carried out in the same three organisations with organisational executives, KM managers and KM practitioners. Structural equation modelling was used to test the three study models. The results show that both inclusive leadership’s and knowledge leadership’s influence on organisational culture dimensions (participation, trust, agreement, team orientation, and openness) were highly significant. Interestingly, and contrary to expectations, the quantitative data show that neither participation nor team orientation had a significant impact on attitude toward knowledge sharing. Also, the results show that inclusive leadership has a positive an impact on attitude toward knowledge sharing whereas knowledge leadership was found to have a negative influence. In addition, all TRA constructs were significant for all three models. The results offer various insights into knowledge sharing behavioural intentions in organisations in the UAE. Policy makers, executive leaders and KM managers will be able to utilise the results and the practical implications of this study to create intervention programs to enhance knowledge sharing intentions and practices in organisations. The thesis provides an alternative view to the more common technological focus, moving it more onto human related factors. It is important for organisations to acknowledge the importance of both leadership and organisational culture on knowledge sharing behavioural intentions among employees. Like anything else that keeps evolving, organisational culture and leadership too evolves and therefore, organisations need to look for the best organisational culture and leadership style that will keep them on top of the market

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods

An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis

Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism

Establishing a marine monitoring programme to assess antibiotic resistance: a case study from the Gulf Cooperation Council (GCC) region

The World Health Organization considers antimicrobial resistance as one of the most pressing global issues which poses a fundamental threat to human health, development, and security. Due to demographic and environmental factors, the marine environment of the Gulf Cooperation Council (GCC) region may be particularly susceptible to the threat of antimicrobial resistance. However, there is currently little information on the presence of AMR in the GCC marine environment to inform the design of appropriate targeted surveillance activities. The objective of this study was to develop, implement and conduct a rapid regional baseline monitoring survey of the presence of AMR in the GCC marine environment, through the analysis of seawater collected from high-risk areas across four GCC states: (Bahrain, Oman, Kuwait, and the United Arab Emirates). 560 Escherichia coli strains were analysed as part of this monitoring programme between December 2018 and May 2019. Multi-drug resistance (resistance to three or more structural classes of antimicrobials) was observed in 32.5% of tested isolates. High levels of reduced susceptibility to ampicillin (29.6%), nalidixic acid (27.9%), tetracycline (27.5%), sulfamethoxazole (22.5%) and trimethoprim (22.5%) were observed. Reduced susceptibility to the high priority critically important antimicrobials: azithromycin (9.3%), ceftazidime (12.7%), cefotaxime (12.7%), ciprofloxacin (44.6%), gentamicin (2.7%) and tigecycline (0.5%), was also noted. A subset of 173 isolates was whole genome sequenced, and high carriage rates of qnrS1 (60/173) and bla CTX-M-15 (45/173) were observed, correlating with reduced susceptibility to the fluoroquinolones and third generation cephalosporins, respectively. This study is important because of the resistance patterns observed, the demonstrated utility in applying genomic-based approaches to routine microbiological monitoring, and the overall establishment of a transnational AMR surveillance framework focussed on coastal and marine environments

Antibody Response to SARS-CoV-2: A Cohort Study in Qatar's Primary Care Settings.

Globally, countries are rolling out Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quarantine policies and vaccination programs. Research studies are needed in helping understand the likelihood of acquired immunity to reinfection and identify priority groups for vaccination to inform them. This study aimed to assess period prevalence and longitudinal changes in antibody levels after SARS-CoV-2 infection in Qatari primary care settings. A cohort study design with 2 data collection phases was undertaken-Phase 1 (conducted in July 2020) and Phase 2 (conducted in October 2020). A stratified random sampling technique by age, gender and nationality was utilized to identify the study sample. The total sample size required for the study was estimated to be 2102. Participants were invited to an appointment where they were administered a questionnaire and provided samples for polymerase chain reaction and Immunoglobulin G immunoassay tests. A total of 943 individuals participated in both Phase 1 and Phase 2. In this cohort, seroprevalence of SARS-CoV-2 was found to be 12% (N = 113) in Phase 1 and 17.2% (N = 162) in Phase 2. Of the 113 participants who were seropositive in Phase 1, 38.1% (CI 29.5-47.2%, N = 43) had a reduction, 54.9% (CI 45.7-63.8%, N = 62) had no change, and 7.1% (CI 3.4-12.9%, N = 8) had an increase in IgG titer in Phase 2. All (N = 18) participants aged 10 to 17 years retained their antibodies. The proportion of men who retained their antibodies was slightly higher compared to women-92.5% (N = 74) and 87.9% (N = 29) respectively. Similarly, symptomatic individuals (97.8%; N = 45) had a higher antibody retention compared with asymptomatic individuals (86.4%; N = 57). This study provides preliminary information on the longitudinal changes in antibody levels after SARS-CoV-2 infection. These findings will help inform quarantine policies and vaccination programs.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by PHCC (PHCCDCR202005047).The funders had no role in the design, analysis, interpretation, or writing

Results from the United Arab Emirates 2022 report card on physical activity for children and adolescents

Objective: The United Arab Emirates (UAE) 2022 Report Card provides a systematic evaluation of the physical activity (PA) levels of children and adolescents in the UAE. Methods: The 2022 Report Card utilized data from 2017 to 2021 to inform 10 core PA indicators that were common to the Global Matrix 4.0. Results: One in five (19%) UAE school children achieved the recommended amount of moderate-to-vigorous PA (i.e. ≥60 min/d; Total Physical Activity Grade F). Less than 1% of school children used active transport to and from school (Active Transportation Grade F). One in four (26%) secondary school children achieved the recreational screen time recommendations (i.e. ≤2 h/d; Sedentary Behaviours Grade D-). A quarter of adults reported achieving the recommended PA level (i.e. ≥150 min of moderate-intensity PA per week, or equivalent) (Family and Peers Grade D-). All school children are taught physical education (PE) by a specialist with at least a bachelor\u27s degree in PE; however, the duration of weekly PE classes varied between schools (School Grade A-). The UAE Government has invested significant funds and resources into developing and implementing strategies and facilities that will increase PA across the entire population (Government Grade B+). Organised Sport and Physical Activity, Active Play, Physical Fitness, and Community and Environment indicators were graded ‘Incomplete’ (INC) due to a lack of available data. Conclusions: Overall, PA levels remain low and sedentary behaviours remain high amongst UAE children and adolescents. The UAE Government has sustained investment in further developing PA opportunities for all children and adults which should translate to increased PA and health improvements at a population level

Mobile phones as fomites for pathogenic microbes: A cross-sectional survey of perceptions and sanitization habits of health care workers in Dubai, United Arab Emirates

Backgrounds In 2022, smartphone use continues to expand with the number of smartphone subscriptions surpassing 6 billion and forecasted to grow to 7.5 billion by 2026. The necessity of these ‘high touch’ devices as essential tools in professional healthcare settings carries great risks of cross-contamination between mobile phones and hands. Current research emphasises mobile phones as fomites enhancing the risk of nosocomial disease dissemination as phone sanitisation is often overlooked. To assess and report via a large-scale E-survey the handling practices and the use of phones by healthcare workers. Methods A total of 377 healthcare workers (HCWs) participated in this study to fill in an E-survey online consisting of 14 questions (including categorical, ordinal, and numerical data). Analysis of categorical data used non-parametric techniques such as Pearson's chi-squared test. Results During an 8-h shift, 92.8% (n/N = 350/377) use their phone at work with 84.6% (n/N = 319/377) considering mobile phones as an essential tool for their job. Almost all HCWs who participated in this survey believe their mobile phones could potentially harbour microorganisms (97.1%; n/N = 366/377). Fifty-seven respondents (15.1%) indicated that they use their phones while wearing gloves and 10.3% (n/N = 39/377) have never cleaned their phones. The majority of respondents (89.3%; n/N = 337/377) agreed that contaminated mobile phones could contribute to dissemination of SARS-CoV-2. Conclusion Mobile phone use is now almost universal and indispensable in healthcare. Medical staff believe mobile phones can act as fomites with a potential risk for dissemination of microbes including SARS-COV-2. There is an urgent call for the incorporation of mobile phone sanitisation in infection prevention protocol. Studies on the use of ultraviolet-C based phone sanitation devices in health care settings are needed

Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic.

The overarching objective of this study was to provide the descriptive epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar by addressing specific research questions through a series of national epidemiologic studies. Sources of data were the centralized and standardized national databases for SARS-CoV-2 infection. By July 10, 2020, 397,577 individuals had been tested for SARS-CoV-2 using polymerase-chain-reaction (PCR), of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI 27.8-28.1%). As of July 5, case severity rate, based on World Health Organization (WHO) severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Age was by far the strongest predictor of severe, critical, or fatal infection. PCR positivity of nasopharyngeal/oropharyngeal swabs in a national community survey (May 6-7) including 1,307 participants was 14.9% (95% CI 11.5-19.0%); 58.5% of those testing positive were asymptomatic. Across 448 ad-hoc testing campaigns in workplaces and residential areas including 26,715 individuals, pooled mean PCR positivity was 15.6% (95% CI 13.7-17.7%). SARS-CoV-2 antibody prevalence was 24.0% (95% CI 23.3-24.6%) in 32,970 residual clinical blood specimens. Antibody prevalence was only 47.3% (95% CI 46.2-48.5%) in those who had at least one PCR positive result, but 91.3% (95% CI 89.5-92.9%) among those who were PCR positive > 3 weeks before serology testing. Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to growing immunity levels in the population

SARS-CoV-2 seroprevalence in the urban population of Qatar: An analysis of antibody testing on a sample of 112,941 individuals

ABSTRACTBackgroundQatar has experienced a large SARS-CoV-2 epidemic. Our first objective was to assess the proportion of the urban population that has been infected with SARS-CoV-2, by measuring the prevalence of detectable antibodies. Our second objective was to identify predictors for infection and for having higher antibody titers.MethodsResidual blood specimens from individuals receiving routine and other clinical care between May 12-September 9, 2020 were tested for anti-SARS-CoV-2 antibodies. Associations with seropositivity and higher antibody titers were identified through regression analyses. Probability weights were applied in deriving the epidemiological measures.ResultsWe tested 112,941 individuals (∼10% of Qatar’s urban population), of whom 51.6% were men and 66.0% were 20-49 years of age. Seropositivity was 13.3% (95% CI: 13.1-13.6%) and was significantly associated with sex, age, nationality, clinical-care type, and testing date. The proportion with higher antibody titers varied by age, nationality, clinical-care type, and testing date. There was a strong correlation between higher antibody titers and seroprevalence in each nationality, with a Pearson correlation coefficient of 0.85 (95% CI: 0.47-0.96), suggesting that higher antibody titers may indicate repeated exposure to the virus. The percentage of antibody-positive persons with prior PCR-confirmed diagnosis was 47.1% (95% CI: 46.1-48.2%), severity rate was 3.9% (95% CI: 3.7-4.2%), criticality rate was 1.3% (95% CI: 1.1-1.4%), and fatality rate was 0.3% (95% CI: 0.2-0.3%).ConclusionsFewer than two in every 10 individuals in Qatar’s urban population had detectable antibodies against SARS-CoV-2 between May 12-September 9, 2020, suggesting that this population is still far from the herd immunity threshold and at risk from a subsequent epidemic wave.</jats:sec

Herd Immunity against Severe Acute Respiratory Syndrome Coronavirus 2 Infection in 10 Communities, Qatar.

We investigated what proportion of the population acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whether the herd immunity threshold has been reached in 10 communities in Qatar. The study included 4,970 participants during June 21-September 9, 2020. Antibodies against SARS-CoV-2 were detected by using an electrochemiluminescence immunoassay. Seropositivity ranged from 54.9% (95% CI 50.2%-59.4%) to 83.8% (95% CI 79.1%-87.7%) across communities and showed a pooled mean of 66.1% (95% CI 61.5%-70.6%). A range of other epidemiologic measures indicated that active infection is rare, with limited if any sustainable infection transmission for clusters to occur. Only 5 infections were ever severe and 1 was critical in these young communities; infection severity rate of 0.2% (95% CI 0.1%-0.4%). Specific communities in Qatar have or nearly reached herd immunity for SARS-CoV-2 infection: 65%-70% of the population has been infected