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Purpose: This study aims to investigate the role of dynamic capabilities (DCs) in the financial performance of the small and medium enterprises (SMEs) during COVID-19. Furthermore, it took chief executive officer gender to moderate the relationship between DCs and financial performance. Design/methodology/approach: This study used the survey approach to collect the data. Regression analysis was used on 563 responses to test the proposed hypotheses. Findings: The results showed that DCs have a significant positive effect on the SME's operating level and revenues. Moreover, it also moderates the DCs and financial performance during COVID-19. The results revealed that firms that used the DCs during COVID-19 not only survive during the COVID-19 tough time but also enjoyed a decent revenue level. Originality/value: To the best of the authors' knowledge, this study is the first study to establish the link between DCs and SMEs operating and revenue level during the COVID-19 pandemic in Pakistani settings. © 2022, Emerald Publishing Limited.
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Purpose This study aims to investigate the role of dynamic capabilities (DCs) in the financial performance of the small and medium enterprises (SMEs) during COVID-19. Furthermore, it took chief executive officer gender to moderate the relationship between DCs and financial performance. Design/methodology/approach This study used the survey approach to collect the data. Regression analysis was used on 563 responses to test the proposed hypotheses. Findings The results showed that DCs have a significant positive effect on the SME's operating level and revenues. Moreover, it also moderates the DCs and financial performance during COVID-19. The results revealed that firms that used the DCs during COVID-19 not only survive during the COVID-19 tough time but also enjoyed a decent revenue level. Originality/value To the best of the authors' knowledge, this study is the first study to establish the link between DCs and SMEs operating and revenue level during the COVID-19 pandemic in Pakistani settings.
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The COVID-19 pandemic is indeed a once-in-a-lifetime emergency. While it seems that the end is nigh, there is also a renewed talk of the looming fourth wave spurred by the mutated Delta, Delta+, and other variants. The pandemic has made several fault lines visible in almost all societies. These include but are not limited to the tentativeness of our knowledge (especially science), the precariousness of our health systems, and failings of the educational systems, particularly citizenship education. While the COVID-19 pandemic will be long remembered as the health crisis of our times;we contend that the pandemic is also an educational crisis. Results of neoliberal neglect of citizenship-related education are now apparent in form of unethical, unjust, racist, and socially irresponsible attitudes and behaviors of individuals, collectives, and states. At the individual level, these are obvious in the irresponsible behaviors that endanger the lives of fellow citizens. At the community level, the failure of citizenship education is evident in vaccination inequality both within and between societies. Finally, at the level of international community, one can see vaccination nationalism, and politically and economically motivated vaccination diplomacy as markers of unethical citizenship.
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COVID-19 has caused major changes in every aspect of human endeavor, including efforts in the higher education sector. A sudden shift from face-to-face and blended settings to a completely online delivery mode has introduced changes to conventional teaching methods, and made learning rely heavily on technology and the Internet. Hence, students' online engagement with these tools has become even more important for their academic success. Therefore, there is a need to investigate the effects of various indicators of students' online presence on their academic performance. This paper explores the effectiveness of online presence in Higher Education Institutes, where COVID-19 has shifted the deliveries to online mode. The chosen indicator is frequency that will be adequately used to quantify the effectiveness of online presence on student performance in online mathematics courses. Statistical methods are used to measure the correlation and association between students' online presence indicators and their performance. As such, it would allow to build models to predict future outcomes or occurrences and student performances, with a major focus on mathematics and statistics courses. The results show that there is an increase in student online interaction in courses during COVID-19 era;however, it is consistent with the Online Measureable Presence Model (OMPM) model where frequency was the dominant indicator of student performance.
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The COVID-19 pandemic has necessitated rapid top-down dissemination of reliable and actionable information. This presents unique challenges in engaging low-literate communities that live in poverty and lack access to the Internet. We describe the design and deployment of a voice-based social media platform, accessible over simple phones, for actively engaging such communities in Pakistan with reliable COVID information. We developed three strategies to overcome users' hesitation, mistrust, and skepticism in engaging with COVID content. Users were: (1) encouraged to listen to reliable COVID advisory, (2) incentivized to share authentic content with others, and (3) prompted to critically think about COVID-related information behaviors. Using a mixed-methods evaluation, we show that users approached with all three strategies had a significantly higher engagement with COVID content compared to others. We discuss how new designs of social media can enable users to engage with and propagate authentic information. © 2022 ACM.
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Background The second wave of the COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. Methods The current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluated statistically significant risk factors, generated new combined variables, and then supply these risk factors to the deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients (≥18 years) admitted to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID19 on RT-PCR, had incomplete or missing health records. The first phase selection of the risk factors was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase, we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, and others. Results A total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after the exclusion criteria, and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30;95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51;95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24;95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24;95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21;95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79;95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%;positive predictive value, 50.00%;negative predictive value, 91.05%;and area under the curve of the receiver-operator curve of 88.5. Conclusion Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly, and with high accuracy.
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Background: Coronavirus disease 2019 (COVID-19) was declared as a pandemic by the World Health Organization (WHO) on March 11, 2020. Apart from respiratory findings, cardiac involvement has been highlighted by some studies. COVID-19 is increasing rapidly among young adults. The present study was designed to evaluate left ventricular function using speckle-tracking echocardiography in young adult COVID-19 patients who underwent home recovery. Methods: The study assessed 40 patients aged between 18 and 39 years who recovered at home from COVID-19 and 20 healthy control subjects. All the participants underwent evaluations of left and right ventricular function via the conventional and global longitudinal strain (GLS) technique measured by speckle-tracking echocardiography. Results: Heart rate was significantly higher in the post–COVID-19 group (P=0.024). The patients were assessed for a mean period of 38.8 days (standard deviation=10.9 d) after negative COVID-19 testing. In the post–COVID-19 group, 19 patients (47%) reported ongoing dyspnea: 13 had dyspnea during ordinary daily activities and 6 had dyspnea during less-than-ordinary daily activities. Nine patients (23%) had chest pain, 8 (20%) had palpitations, 22 (55%) had fatigue, and 4 (10%) had joint pain. The left ventricular GLS value for the post–COVID-19 group was significantly impaired compared with the control group (P=0.006). Conclusions: Among our young adult patients, who had recovered from COVID-19 at home, left ventricular GLS was affected, which may indicate the subclinical impairment of left ventricular systolic function.
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OBJECTIVE: Regular physical activity is essential for lifelong optimal health. Contrarily, physical inactivity is linked with risk for many chronic diseases. This study was conducted to evaluate the physical activity levels and factors associated with physical inactivity among a multi-ethnic population of young men living in Saudi Arabia. METHODS: This is a cross-sectional study involving 3,600 young men (20-35 years) living in Riyadh, Saudi Arabia. Sociodemographic and physical activity data were collected from subjects by face-to-face interviews. Physical activity characteristics were evaluated by using the Global Physical Activity Questionnaire. Weight and height were measured following standardized methods, then body mass index was calculated. RESULTS: Physical inactivity was reported among 24.9% of study subjects. The lowest and highest rates of physical inactivity were reported among subjects from the Philippines (14.0%) and Saudi Arabia (41.5%), respectively. There is a high variation in daily minutes spent on physical activities related to work, transport, recreation, vigorous and moderate-intensity physical activities and sedentary behaviors among study participants based on their nationalities. Nationality, increasing age, longer residency period in Saudi Arabia, living within a family household, having a high education level, earning a high monthly income, and increasing body mass index were significantly associated with a higher risk of physical inactivity among the study participants. CONCLUSION: Physical inactivity prevalence is relatively high among a multi-ethnic population of young men living in Saudi Arabia. The findings confirmed notable disparities in the physical activity characteristics among participants from different countries living in Saudi Arabia.
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Sedentary Behavior , Cross-Sectional Studies , Ethnicity , Exercise , Humans , Male , Saudi Arabia/epidemiologyABSTRACT
Objective: The objective of the study was to determine the knowledge, attitude and practices of surgical interns, residents and surgeons in the time of COVID-19. Methods: A cross-sectional study was conducted on surgical interns, trainees, registrar and consult-ants working in public and private sector tertiary care hospitals of Karachi. Non-Convenience sampling technique is used. A total of 184 patients were included. The study was performed on assessing patients' demographics, basic knowledge and awareness of COVID 19 and individuals' practices toward COVID 19 in their professional routines. Data were stored and analyzed using IBM-SPSS version 23.0. Pearson Chi Square test was used to test the association outcomes, awareness and attitude items on COVID-19 between private and public sector hospitals. P-values less than 0.05 were considered statistically significant Results: The study comprised a total of 184 participants. Mostly from public 115(63%) rest 69(37%) belonged private sector hospitals. Most respondents 122(66.3%) were aware of COVID 19 incubation, symptoms and the detection of patients at risk and modalities of transmission in operating rooms. Results showed among public vs Private sector hospitals (81.7% / 68.1%) were treating corona virus, there were (35.7% / 46.3%) screening and admitted the cases, (56.5% / 62.4) tentative numbers of COVID-19 cases reported range from 0-25, (40.9%/11.6%) surgeons were assigned additional duties for COVID-19 by hospital management found statistically significant with "p<0.01". Conclusion: It is concluded after this study that the surgeons in Pakistan are well aware of the situation, have adequate knowledge of the risk factors and symptoms of COVID-19. But statistically, probably because of limited resources and work load, there is a substantial gap in practices between public and private sector surgeons. There is also a dire need to develop local guidelines related to preventive measures in our health care systems according to our demands and supply.
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Objective: To investigate online classes experience of students to find their acceptance for non-traditional way of learning due to lockdown of educational institutes and impact of online classes on Pakistani students during COVID 19. Methodology: This cross-sectional survey was done on 228 students from different institutes of Pakistan from April to June 2020. We used 13 online questionnaires in Google form. Data were analyzed using SPSS 21. Results: Out of 228 students, 54 were males and 174 females. Age ranged from 18 to 34 years (mean 21.34±2.051). Majority (52.4%) of students were from Government institutes and 47.6% from private institutes. Chi-Square tests were applied to assess the relation of student's response with respect to gender and professional educational level. The present study reported no significant relationship (rho-0.2), (p=0.14). Conclusion: Outbreak of COVID-19 has made a significant distraction in academic activities. Although a proportion of students using online platforms were well familiar with it but majority were facing a huge challenge in online learning. (Rawal Med J 202;46:677-680).
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The Higher Education Institute (HEI) are experiencing a major paradigm shift due to recent global pandemic. A sudden shift from face-to-face (F2F) and blended modes of study to completely online mode of delivery has introduced hidden challenges to facilitators and students alike. Student's online engagement has become even more important for their academic success as F2F component is not there in most cases. Therefore, there is a need to investigate the effects of the various indicators of students' online presence towards their academic performance. This paper explores the effectiveness of online presence in HEI where Covid-19 has shifted the course deliveries to fully online mode. Previously, Online Measurable Presence Model (OMPM) was used to find students effectiveness in a blended learning environment where two indicators used were Frequency and Duration. The chosen indicator in this research is frequency, which will be adequately used to quantify the effectiveness of the online presence in two mathematics courses in the Pacific. Clustering technique is used to create clusters of Frequency and see their relation to OMPM model. Prediction is made using neural network to see the accuracy based on model. The clusters would allow to build predictive models to predict future outcomes or occurrences and student performances, with a major focus on mathematics courses. © 2020 IEEE.
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As a once in a 100 years emergency, the COVID-19 pandemic has resulted in repercussions for the economy, the polity, and the social. Also, the ongoing pandemic is as much a teaching moment as it to reflect on the lack of critical citizenship education. The fault lines of the health system have become visible in terms of infection and death rates;the fault lines of the educational system are now apparent in the behavior of the citizens who are flouting the public health guidelines and, in certain cases, actively opposing these guidelines. The main objective of this commentary is to initiate a dialogue on the social contract between the state and the subjects and to see how education and educators can respond to the challenge of the new normal. It is contended that education under the new normal cannot afford to keep educating for unbridled productivity education under the new normal. It must have welfare, human connections, ethical relationships, environmental stewardship, and social justice front and center.