MOOC-based learning for human resource development in organizations during the post-pandemic and war crisis: a study from a developing country perspective

Purpose: Human resources (HR) management has encountered unforeseen obstacles and issues in recruiting, retaining, training and developing workforces under the "new normal” due to pandemic circumstances followed by the Russo–Ukrainian War and global economic turmoil. As the world is now well-equipped with technological advancements and internet-based connectivity, many pandemic disruptions have been avoided through rapid adaptation of technological systems. Despite the constructive outcomes of this contemporary approach to learning and development (L&D), this study explores the further depths of massive open online courses (MOOC) platform adoption in human resource development initiatives during pandemic times. Design/methodology/approach: A qualitative research approach was adopted to understand the employee and HR perspective on the changes in L&D approaches in organizations. To gather the primary data, respondents were divided into two clusters;different sets of questionnaires were developed for interview sessions. Findings: Results suggest that employee L&D was much more improvised with distance or online learning, including organizational e-learning systems and MOOC platforms. To accomplish their HR development goals, organizations went through significant transformations during the Coronavirus pandemic;organizational attempts to initiate online training and MOOC-based learning fostered positive results in employee capacity development, process improvement, employee engagement and motivation. Originality/value: This research will assist organizations in developing interactive training methods as an effective replacement for traditional training. Additionally, it will assist readers, practitioners and HR specialists in understanding how MOOCs are changing the L&D ecosystem. © 2022, Shamsul Huq Bin Shahriar, Silvia Akter, Nayeema Sultana, Sayed Arafat and Md. Mahfuzur Rahman Khan.

Remote Work and Changes in Organizational HR Practices During Corona Pandemic: A Study from Bangladesh

This study aims to offer insightful knowledge on organizational members’ real-life experience of working in a ‘new normal’ environment and explores changes in organizational HR practices and the future of work culture during this pandemic. Applying the qualitative methodology through implementing an in-depth interview technique, this study revealed subjective insights on pandemic impacts within diverse organizations and their coping strategies, that is, remote work practices and technological adaptations. The study found out that HR functionalities powered by different online tools and remote work or flexible roster duties are ensuring employee betterment and organizational productivity at the same time. Pandemic countermeasure oriented or transformed HR practices like online training and e-recruitment are keeping the workforce steady in this distressing time, but the ‘new normal’ lifestyle and evolved work environment, practices are putting much stress on and changing the dimension of work policies like employee well-being, compensation, leave, and so on, through isolation, quarantine and strict health guideline type issues. © 2022 Management Development Institute.

Relationship of Blood Group Type and SARS-CoV-2 Infection: Experience in A Peripheral Military Hospital

Introduction: Clinical studies have shown that advanced age and chronic diseases increase the risk of infection. Different studies have found blood groups are also a determining factor of severity. However, in Bangladesh it is yet to have any clinical pattern in this respect. The aim of this study is to find out whether there exists a relationship between the blood groups of the patients and risk of SARS-CoV-2 infection. Methods: The study is a retrospective cohort study. It included all hospitalized RT-PCR confirmed COVID-19 patients in Combined Military Hospital (CMH) Barishal. We included both genders and people who are older than 14 years of age. The study was conducted during the period between 4th June and 3rd August, 2020. In this period total 556 individuals reported and tested for SARS-CoV-2 where COVID +ve patients are served as patient group and COVID -ve patients are served as the controls. Results: Our sample consists of 185 patients;we found a percentage distribution of 34.6%, 39.5%, 5.4%, and 20.54% for blood groups A, B, AB and O, respectively. Blood group A was statistically significantly more frequent among those infected with COVID-19 compared to controls (34.6% vs. 21.83%, p-value 0.045 (<0.05);OR: 1.894).On the other hand, the frequency of blood group O was significantly lower in the COVID-19 patients, compared to the control group (20.54% vs 32.88%, p-value0.049 (<0.05);OR: 0.528). Although there was a higher percentage distribution of the B blood group among COVID-19 patients as compared to the control groups, this difference did not reach statistical significance. Also, there was no significant difference in the risk of COVID-19 in blood group AB with a distribution of 6.2% in control group, and5.4% in COVID-19 patients. Conclusion: The results of the present study suggest that the blood group A might have a role in increased susceptibility to the COVID-19 infection and the blood group O might be somewhat protective. [ABSTRACT FROM AUTHOR] Copyright of Journal of Bangladesh College of Physicians & Surgeons is the property of Bangladesh College of Physicians & Surgeons and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic Detection of COVID-19 Disease in Chest X-Ray Images using Deep Neural Networks

The worldwide spread of COVID-19 has marked a devastating impact on the global economy and public health. One of the significant steps of COVID-19 affected patient's treatment is the faster and accurate detection of the symptoms which is the motivational center of this study. In this paper, we have analyzed the performances of six artificial deep neural networks (2-D CNN, ResNet-50, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2) for COVID-19 detection from the chest X-rays. Our dataset consists of 2905 chest X-rays of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). Among the implemented neural networks, ResNet-50 demonstrated reasonable performance in classifying different cases with an overall accuracy of 96.91%. Most importantly, the model has shown a significantly good performance in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Sensitivity = 1.00, Specificity = 1.00, and F1-score = 1.00). Therefore, among the deep neural networks presented in this paper, ResNet-50 can be adapted as a reliable method for faster and accurate COVID-19 affected case detection. © 2020 IEEE.