Roles of medical education department: What are expectations of the faculty?

BACKGROUND & OBJECTIVE: Pakistan like many Asian countries is investing in medical education to address increased societal needs and to meet the requirement of national and international accrediting bodies. Establishing medical education departments is part of this investment. The research question was "What are the expectations of faculty from medical education department?" The objective of this study was to explore the Faculty's perception about the roles of medical education department and their suggestions for its future endeavors. METHODS: A qualitative case study design was chosen for this study. Heterogeneous group of faculty members from basic and clinical sciences departments of University College of Medicine, Lahore were invited for this study. They represented a variety of disciplines, and seniority levels. They were queried about their perception of the roles of medical education department and were encouraged to give suggestions for better functioning of department. Data was collected by audio recording through focus group interviews. Data analysis was done using NVIVO 11 software. RESULTS: Initially 55 nodes/codes emerged which were then condensed to 35 nodes. Out of these three main themes emerged. The three emergent themes were: Knowledge about the roles of medical education department.Interactions with the medical education department.Future Prospects of the medical education department. Roles of medical education department identified by the faculty were mainly related to faculty development, curriculum planning and implementation, student support, policy making for student induction, improving teaching strategies, student assessment, quality assurance and accreditation of the medical college. Faculty development not only encompassed faculty training but also provision of opportunities for research and curriculum development. Student support was found to be a neglected role and faculty members suggested it to be an important area to be looked upon by medical education departments. CONCLUSION: Institutions must ensure consultation with faculty members and should take proactive measures to sustain change, including giving ownership and team building among the faculty members.

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset.

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today's medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient's death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today's healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

COVID-19 pandemic control and administrative issues in Pakistan: How Pakistan mitigated both pandemic and administration issues?

COVID-19 is wreaking havoc all around the globe, and Pakistan bears no exception. This study explores Pakistan's response toward controlling COVID-19 Pandemic from the day the 1st case was reported, February 26, 2020, in Pakistan until August 31, 2020. It explores the administrative conflicts among federal and provincial governments and political behaviors of political parties toward the COVID-19 pandemic by referring Government Response Index. By applying the ARDL model approach, results show that since the administrative harmony had been implemented in Pakistan in July 2020, its positive impact on combating the COVID-19 situation in Pakistan and substantial improvement in recovered cases and a downward trend new confirmed and fatal cases has observed in Pakistan. The findings demonstrate that administrative efforts scattered due to internal conflicts from February to mid-July 2020 have ended, and collective aggressive policy enforcement has been mitigating the adverse impact of COVID-19 in Pakistan since July to date. However, sustainable measures and prudent policy implications are needed to combat the ongoing COVID-19 pandemic and future calamities.

Impact of COVID-19 pandemic on sustainability determinants: A global trend.

For the last six months till today, the world had had no luck in defeating COVID-19. This study examined the impact of the COVID-19 Pandemic on sustainability determinants, with the time arisen from December 27, 2019, through June 30, 2020. This study considers quantitative COVID-19 dashboard data with sustainable determinants; old age group, people exposed to air pollution, and countries with the most international travelers. Applying linear regression examines that COVID-19 behavior concerning the aging population and countries host the most international travelers, more positively significant than people exposed to PM2.5% air pollution, respectively. This study made a novel contribution by analyzing two variables' interaction; first, the aging population and the countries that host the most international travelers. Secondly, the aging population and people exposed to air pollution are vulnerable to COVID-19 globally, a novel concept comprehensively. Results show that countries with aging populations are more exposed to COVID-19, and its interaction term host the most international travelers. It also analyses that the aging population and its interaction with people exposed to air pollution are also vulnerable to COVID-19 but marginally lesser than the former. However, their behavior varies from country to country, making room for future study to analyze a more in-depth analysis. It gives a different dimension to consider other risk factors of COVID-19 by bearing in mind its unique contagious characteristics, which will help policymakers draft a sound epidemic preparedness policy to tackle the unforeseen crisis. It gives a thought of provoking to policy practitioners for the risk characteristics of COVID-19, which needs a reassessment to epidemic risk management to deal with this, and future unforeseen crisis by considering Sustainable Development Goals.