• The strength of the future nursing workforce depends on a continuous flow of new registered nurses from the nursing education pipeline. Delays in the education sector will lead to failures into the future. Investment in nursing education and jobs is needed to improve retention of the current nursing workforce and address the global nurse shortage. KEY MESSAGES Nursing education and the emerging nursing workforce in COVID-19 pandemic

The International Council of Nurses.

Immunopharmacological perspective on zinc in SARS-CoV-2 infection.

Saudi universities have suspended the traditional face-to-face classes and moved to online learning to ensure the continuation of the educational process during the COVID-19 pandemic. The speed at which the move to online learning has happened was unprecedented and staggering. Academics, had to improvise and try different e-learning solutions in less-than-ideal circumstances. No matter how powerful and easy-to-use e-learning solutions might be, many academics understandably found the transition to e-learning stressful. This research project looks at the transition experience to online learning from the viewpoint of academics in Saudi universities. It investigates how the COVID-19 pandemic has affected the adoption of e-learning solutions and how this forced experience would affect the long-term adoption of e-learning solutions. The study employed an online survey administered to academics from all the 24 public universities. The results were quantitatively analysed by using structural equation modelling (SEM). It was found that attitudes toward e-learning, self-efficacy and perceived reliability have significant positive effects on behavioural intention to adopt e-learning. It was also found that COVID-19 has positively affected the long-term adoption of e-learning. Therefore, it is recommended that universities take immediate actions and capitalise on the e-learning experience that academics have gained during COVID-19 pandemic.

The impact of the COVID-19 pandemic on the adoption of e-learning among academics in Saudi Arabia

Aims and objectives To investigate nurses' intention in accepting COVID-19 vaccination and the factors affecting their decision. Background COVID-19 vaccination has started in most European countries with healthcare personnel being the first group receiving the vaccine shots. Their attitude towards vaccination is of paramount significant as their role in the frontline could help in the awareness of general population. Methods A study was conducted in Albania, Cyprus, Greece, Spain and Kosovo with the use of an online questionnaire. The Fear of COVID-19 Scale was used. The STROBE checklist was followed for this cross-sectional study. Results Study population consisted of 1135 nurses. Mean age of the participants was 38.3 years, while most of them were female gender (84.7%) and married (53.1%). Acceptance of a safe and effective COVID-19 vaccine was higher among Greek (79.2%) and Spanish (71.6%) nurses, followed by Cypriot (54%), Albanian (46.3%) and Kosovo (46.2%) nurses. Key factors for willingness to get vaccinated were male gender, living in a country with a high mortality rate in comparison with low mortality, being not infected with COVID-19, having high level of knowledge about COVID-19 vaccines and having been vaccinated for influenza in the last 2 years. Moreover, trusting the government and doctors regarding the information about the COVID-19 and having high level of fear about this virus were key factors for willingness to get vaccinated. Conclusion Vaccination of healthcare personnel is a crucial issue not only for their own safety but also for their patients'. Healthcare acceptance to get vaccinated can work as a role model for general population. Relevance to clinical practice Gender, country, mortality rate, trust in government and health professionals and the level of fear were key factors that should be managed in clinical practice.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jocn.15980

Predictors of nurses' intention to accept COVID-19 vaccination: A cross-sectional study in five European countries.

Background This study has two aims. First, determine the fit of the fear model to COVID-19, anxiety, and post-traumatic stress in the general population and health-care workers. Second, determine which model best explains the relationship between depression and the triad of fear, anxiety, and post-traumatic stress in both groups. Method A cross-sectional study was conducted using self-reported questionnaires for anxiety, fear of COVID-19, depression, and post-traumatic stress. Information was collected from adults living in Lima, the capital and the most populous city in Peru. The explanatory models were evaluated using a structural equation model. Results A total of 830 participants were included, including general population (n = 640) and health-care workers (n = 190). A high overall prevalence of depressive symptoms (16%), anxiety (11.7%), and post-traumatic stress (14.9%) were identified. A higher prevalence of depressive, anxious, or stress symptoms was identified in the general population (28.6%) compared to health-care workers (17.9%). The triad model of fear of COVID-19, anxiety, and stress presented adequate goodness-of-fit indices for both groups. A model was identified that manages to explain depressive symptoms in more than 70% of the general population and health-care workers, based on the variables of the triad (CFI = 0.94; TLI = 0.94; RMSEA = 0.06; SRMR = 0.06). In the general population post-traumatic stress mediated the relationship between anxiety and depression (β = 0.12; 95%CI = 0.06 to 0.18) which was significant, but the indirect effect of post-traumatic stress was not significant in health care workers (β = 0.03; 95%CI = - 0.11 to 0.19). Limitations The prevalence estimates relied on self-reported information. Other variables of interest, such as intolerance to uncertainty or income level, could not be evaluated. Conclusions Our study proposes and tests one model that explains more than 70% of depressive symptoms. This explanatory model can be used in health contexts and populations to determine how emotional factors can affect depressive symptoms.

/pdf/depression-post-traumatic-stress-anxiety-and-fear-of-covid-12bfk3tem4.pdf

Depression, post-traumatic stress, anxiety, and fear of COVID-19 in the general population and health-care workers: prevalence, relationship, and explicative model in Peru.

Measuring the extent of stress and fear among Registered Nurses in KSA during the COVID-19 Outbreak.

Of the most popular applications of artificial intelligence (AI), those used in the health sector are the ones that represent the largest proportion, in terms of use and expectation. An investigative systematization model is proposed in the scientific training of nursing professionals, by articulating epistemological positions from previous studies on the subject. In order to validate the model proposed, a prototype was created to present an application that could help nurses in their clinical processes, storing their experiences in a case base for future research. The prototype consisted of digitizing paediatric nursing diagnoses and inserting them into a case base in order to assess the effectiveness of the prototype in handling these cases in a structure conducive to retrieval, adaptation, indexing, and case comparison. This work presents as a result a computational tool for the health area, employing one of the artificial intelligence techniques, case-based reasoning (CBR). The small governmental nursing education institution in Bangladesh used in this study did not yet have the systemization of nursing care (NCS) and computerized support scales.

/pdf/nursing-care-systematization-with-case-based-reasoning-and-39xr88z8.pdf

Nursing Care Systematization with Case-Based Reasoning and Artificial Intelligence

Factors associated with exercise self-efficacy among people with chronic diseases

Coronaviruses 2019 (Covid-19) is a massive family of viruses that causes respiratory illnesses ranging from the common cold to the most severe conditions such as Middle East Respiratory Syndrome and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that represents the humanitarian crisis on a global scale. Currently, there are no therapeutic strategies approved by the U.S.Food and Drug Administration (FDA) to cure or prevent COVID-19. Global research efforts from around the world extrapolate the updates focusing specifically on the biphasic nature of Covid-19 that involve both innate and acquired immunity. Even though researchers around the globe are racing to invent a life-saving therapeutics and vaccines to tackle COVID-19, the only available venture is a supportive approach in rendering treatment to patients with severe and non-severe cases of COVID-19. Supplementation of several vitamins and trace elements showed the expected favorable impact on enhancing immunity in viral infection. Numerous studies prompted the value of zinc (Zn) supplementation that prevents the virus from entering cells by binding with protein in potentiating antiviral immunity, which is realized through different mechanisms, including the improvement in markers of immune function. Zinc is also an associated factor for several enzymes (needed for the activity for over 300 enzymes), transcription factors, and replication factors. Interestingly, low-level zinc results in dysfunction of all immune cells, subjects with altered zinc state have a high risk for infectious disorders, autoimmune disorders, and cancer. Several assumptions regarding immunomodulators of zinc remain unresolved. This review aimed to explore the hypothetical association of Zinc supplementation (the key immunomodulator) in association with a preventive and therapeutic role of treating patients with COVID-19.

/pdf/immunomodulatory-effects-of-zinc-as-a-supportive-strategies-52ehz5vq31.pdf

Immunomodulatory Effects of Zinc as a Supportive Strategies for COVID-19

Coronavirus-2019 disease (Covid-19) is a contagious respiratory disease that emerged in late 2019 and has been recognized by the World Health Organization (WHO) as a global pandemic in early 2020. Since then, researchers have been exploring various strategies and techniques to fight against this outbreak. The point when the pandemic appeared was also a period in which Machine Learning (ML) and Deep Learning (DL) algorithms were competing with traditional technologies, leading to significant findings in diverse domains. Consequently, many researchers employed ML/DL to speed up Covid-19 detection, prevention, and treatment. This paper reviews the state-of-the-art ML/DL tools used, thoroughly evaluating these techniques and their impact on the battle against Covid-19. This article aims to provide valuable insight to the researchers to assess the use of ML against the Covid-19 pandemic.

/pdf/a-review-on-the-use-of-machine-learning-against-the-covid-19-3fkhv2m6.pdf

Nahla A. Tayyib

Papers

Measuring the extent of stress and fear among Registered Nurses in KSA during the COVID-19 Outbreak.

Nursing Care Systematization with Case-Based Reasoning and Artificial Intelligence

Factors associated with exercise self-efficacy among people with chronic diseases

Immunomodulatory Effects of Zinc as a Supportive Strategies for COVID-19

A Review on the Use of Machine Learning Against the Covid-19 Pandemic