Background: Frontline healthcare providers are consistently exposed to potentially traumatic events while assisting patients with COVID-19. Post-traumatic growth happens when a person can transform trauma and use adversity in one’s advantage. In response to limited studies on positive outcomes that may occur from the pandemic; this study aimed to elucidate the positive impact of coping with COVID-19 outbreak on mental health, such as PTG.Methodology: The study comprised a sample of 691 healthcare providers 59% female, including physicians and nurses, working in (...) public health facilities in Kosovo, with an average age of 41.6 years. They were asked to complete a questionnaire with four parts: Socio-demographic, GHQ-28, COPE and PTGI. A deterioration of mental health with somatic symptoms leading to the escalation due to COVID-19 outbreak was found.Results: Female healthcare providers reported more clinical symptoms as well as higher coping skills scores than men. The domains in which positive changes were most frequently observed were Relating to Others, New Possibilities and Personal Strength. There was no significant direct effect of mental health on PTG in the mediation model, though a significant indirect effect was observed for coping skills.Conclusion: The results suggest that levels of mental health exacerbation do not play a conclusive role in determining levels of PTG, as long coping mechanisms are in place. The development and implementation of interventions to minimize COVID-19-related mental health consequences, by fostering PTG among healthcare providers could be highly beneficial in pandemic response work. (shrink)

Knowing that language is often used as a classifier of human intelligence and the development of systems that understand human language remains a challenge all the time (Kryeziu & Shehu, 2022). Natural Language Processing is a very active field of study, where transformers have a key role. Transformers function based on neural networks and they are increasingly showing promising results. One of the first major contributions to transfer learning in Natural Language Processing was the use of pre-trained word embeddings (...) in 2010 (Joseph, Lev, & Yoshua, 2010). Pre-trained models like ELMo (Matthew, et al., 2018) and BERT (Delvin, et al., 2019) are trained on large corpora of unlabeled text and as a result learning from text representations has achieved good performance on many of the underlying tasks on datasets from different domains. Pre-training in the language model has proven that there has been an improvement in some aspects of natural language processing, based on the paper (Dai & Le, 2015). In present paper, we will pre-train BERT on the task of Masked Language Modeling (MLM) with the Albanian language dataset (alb_dataset) that we have created for this purpose (Kryeziu et al., 2022). We will compare two approaches: training of BERT using the available OSCAR dataset and using our alb_dataset that we have collected. The paper shows some discrepancies during training, especially while evaluating the performance of the model. (shrink)

Many governments in the world have created e-government initiatives including developed and developing countries. In order to better understand e-government evolution, different maturity models have been developed by many authors. In this paper the most cited e-government maturity models are analyzed using the meta-synthesis approach. As a result, five stages of e-government maturity are identified. The comparative results show the supported stages by each e-government initiative as important elements in the decision making process. This paper is attempting to show that (...) although there are many models for measuring e-government maturity, they all converge on one common model. The contribution of this paper is in simplifying work for researchers when choosing the right maturity model. (shrink)

Enterprise Resource Planning systems are used by universities to handle the academic services and business processes while providing enhanced experience and services to students. This study begins with a background review of ERPs in higher education institutions, the impact on the business processes through optimization and the importance of critical success factors for easier implementation. Secondly, Academic Planning, a core part of the student module of ERPs for higher education, is analyzed in this paper from the prism of data integration, (...) business process workflow, and process optimization. The issues that arise with development of a module are addressed through a case study at SEE-University. The data and business process workflows are based on an actual study by real implementation at this institution. The findings from this study will serve other universities who are in the process of implementation of an ERP to ease their development process and improve the efficiency of the services provided. Main contribution of this study is that it reduces the gap in literature and practice for issues and solutions that arise with the development of a new system, especially in higher education institutions, which in turn are very scarce in nature. (shrink)