4101 research outputs found
Sort by
Emergency admissions' diagnoses and risk of in-hospital death according to the primary ICD-10 chapter assigned at discharge and the National Early Warning Score on admission
No full textThe relationship between diagnosis, illness severity, and mortality risk for unselected emergency admissions is poorly defined. To define primary ICD-10 diagnostic chapters at discharge, admission illness severity by the National Early Warning Score, and in-hospital mortality for all unselected emergency admissions. Retrospective, observational, cohort study of 122,259 unselected, adult emergency admissions to Salford Royal Hospital between 2014 and 2022. In-hospital mortality was 4.3% but most patients had an ICD-10 chapter associated with a lower risk of death. 60% of in-hospital deaths were in four chapters, infections, circulatory and respiratory diseases, or neoplasms. An admission NEWS ≥3 was associated with earlier mortality and an eight-fold increased risk of in-hospital mortality. 45% of all in-hospital deaths occurred in patients with an admission NEWS <3. Mortality in emergency hospital admissions is associated with illness severity and four diagnostic chapters. NEWS should not be the only arbiter of hospital admission, as for certain diagnostic chapters the risk of death is high even if vital signs on presentation are normal
Learning from the handling of the referral of Dr Manjula Arora
No full textThe Singh and Forde review of the General Medical Council's handling of Dr Manjula Arora's referral made a number of recommendations for the General Medical Council, the wider NHS and other organisations. This article discusses how to move forward with the recommendations and deliver 21st-century regulation that is truly compassionate, fair and supportive
High risk and impact factors on construction management process − case study of COVID-19 of a hospital in Iraq
No full textThe pandemic of COVID-19 crisis suddenly appeared worldwide, and there were no proper crisis management procedures taken in advance to contain such a problem. Moreover, it has had harmful and unmeasured consequences on most life sectors, including construction, which has been severely impacted during the pandemic. This paper aims to review factors with a high-risk impact on hospitals' construction projects during the outbreak of COVID-19 in developing countries by considering the case of Iraq's construction sector. A mixed methodology of qualitative and quantitative research approaches was used. The qualitative method involved (1) a literature review and (2) semi-structured interviews to identify high-risk factors that affect construction projects during the outbreak of COVID-19. The quantitative method involved (1) collecting survey data from 205 industry professionals and experts to determine each risk factor's importance and influence. Cronbach's alpha test to check the reliability of the collected data set, and (3) a fuzzy inference system method was used to assess the impact of each factor on construction projects during the pandemic. The findings of this study indicate that the construction industry has been hit severely during the COVID-19 in Iraq. Moreover, 17 high-risk sub-factors under six main construction factors involve health and safety, risk management, management deficiency, financial, supply chain management and contractual impact hospital construction projects. Furthermore, the most significant risk factors had impacted hospital construction projects by calculating their means were (1) commitment to safety and health recommendations with a level of impact equal to 4.81; (2) risk management procedures with a level of impact equal to 4.63; (3) equipment delivery delays with a level of impact equal to 4.54; (4) worker acceptance of COVID-19 vaccination with a level of impact equal to 4.36; (5) increase of price material with a level of impact equal to 4.18; (6) lack of use of the latest technology with a level of impact equal to 4.09; (7) delay in payments with a level of impact equal to 3.90; and (8) lack of training to deal with the pandemic with a level of impact equal to 3.81. As such, this paper contributes by providing effective policies and measures to mitigate the impact of COVID-19 on hospital construction projects in Iraq
Remarkable lives - Robyn Clegg-Gibson in conversation with Robert Hurst
No full textPurpose: The purpose of this paper is to share Robyn Clegg-Gibson's story. Design/methodology/approach: Robyn wrote a biography of her experiences. Robert then asked a series of questions from the perspective of a mental health academic researcher. Findings: Robyn shared stories from her life, and how her experiences have shaped her life and herself. Research limitations/implications: Narratives such as this give us an overview of only a single person's experiences. However, they allow the person with lived experience to explore their story in depth. Practical implications: What Robyn has written is very emotional. Her story will give readers an insight into her life and experiences. Social implications: There is so much to learn from a story like Robyn's. In particular, from her experiences of police processes after a crime. Originality/value: To the best of the authors' knowledge, this is the first time that Robyn has chosen to publish her unique story in the written form. The value of Robyn sharing her story is apparent upon reading it
Editorial: Supporting children with child welfare concerns in Africa: Involving formal and informal systems
No full textDeveloping emotional intelligence using simulation with pre-registration nursing students a mixed methods enquiry
No full textBackground: Nursing students need to develop Emotional Intelligence (EI) skills todeliver effective care and navigate the challenges of their profession. However, the practicalapplication of teaching EI in pre-registration nursing education and the development of keycomponents such as self-awareness, social skills, and decision-making abilities have beenneglected in existing literature. This oversight hampers students' ability to effectively apply EIin their future practice. Moreover, traditional learning methods are predominantly used, withminimal integration of simulation-based learning (SBL) techniques.Purpose: The study examines the effectiveness of teaching EI using simulation withpre-registration nursing students in an English Higher Educational Institute (HEI).Methods: A mixed methods sequential explanatory approach was selected to addressthe research question. An SBL intervention was developed and delivered for nursing studentsin the university campus. A quantitative pre-test/post-test intervention design was employed toassess the students’ EI scores. EI score. A total of 116 pre-registration nursing students fromthree academic year groups completed the Trait Emotional Intelligence Short FormQuestionnaire (TEIQue-SF). Subsequently, post-intervention focus group interviews wereconducted with both students and nursing tutors.Results: The findings from both quantitative and qualitative data demonstrated asignificant effect of SBL on nursing students' post intervention EI scores. The students in thisstudy scored a higher level of overall EI after the implementation of the simulation interventionand reported four main themes: SBL intervention enhanced awareness of their own and others’emotions, learning to control their own emotions as well as feeling more empowered totransition to engage in clinical practice. The combined findings from both the quantitative andqualitative studies generated insights of the phenomenon to develop EI among nursing students
Accuracy of water quality prediction using random forest regression and Artificial neural network
No full textWater is life, human and all activities of living things needs water for its survival. There are growing concerns about the quality of water available for human consumption and other daily human activities. The pollution of water are traceable to economic activities, industrialization, urbanization, agricultural activities and other human activities. These activities also affect human life and water quality. This concern has necessitated the studies on prediction of water quality and an efficient water monitoring system. The most common method of ensuring good quality of water is by testing them in the laboratory, this current method wastes time, not economical, wastes human power. Many studies on the use of Artificial neural network and machine learning models to predict the quality of water has been on the increase, this study creates an Artificial neural network and random forest regression machine learning model using parameters such as ph., Hardness, Conductivity, Solids, Conductivity, Turbidity, Portability and does a comparative analysis of the best fit model and it was determined that the random forest model had the best fit model with a lower mean squared error of 2.325103283606211 when compared to the Artificial neural network with a mean squared error of 3.3122911 by determining the model with the least mean squared error. This study postulates that random forest method is the best fit model for prediction of water quality when compared to the Artificial neural network
Numerical investigation of FRCM-strengthened corroded RC beams under cathodic protection
No full textFibric reinforced cementitious matrix (FRCM) composites have been used to improve the mechanical performance of reinforced concrete beams subjected to degradation in the past decades. Recently, dual-functional carbon fibres have been explored to provide both structural strengthening to RC beams and cathodic protection to reinforcement bars. This paper investigates the loading responses and structural behaviour of RC beams subjected to different levels of corrosion, protected by impressed current cathodic protection and structurally strengthened by external bonded FRCM. A numerical model is developed for the corroded RC beams under impressed current cathodic protection and structural strengthening by the FRCM composite. Upon validation against experimental results collected from the literature, the finite element model is then used for parametric study. A number of numerical results are generated to analyse the effects of key parameters, including the corrosion rate, degradation level of interfacial bonding properties due to anode acidification, and end anchorage, followed by detailed discussions. It is found that the significance of the corrosion of steel reinforcement bars significantly affects the load-carrying capacity of the beams. Increasing the corrosion rate from 0 to 40% reduces the load-carrying capacity of un-strengthened beams to 45% of the original capacity. Therefore, the cathodic protection provided by the C-FRCM plate is important to the reinforcement bars as it can avoid the cross-section area reduction of reinforcement bars and, thus, the main loading capacities of the beams. In this study, the degradation of the bonding properties at the interface of carbon fibre and the cementitious matrix due to anode acidification during impressed current cathodic protection is also considered. It is found that the bond strength of the C-FRCM plate has a slight effect on the load-carrying capacity of the beam. In addition, the application of end anchorage can significantly enhance both the load-carrying capacity and ductility of the beams. The rates of enhancement, if compared to the beams with no end anchorage, can reach up to 60%
Academic performance prediction using machine learning algorithms
No full textThe objective of the study is to use a method to predict student performance during the semesters and to compare accuracy perceptron for a dataset of student performance. In this regard, Machine Learning techniques were applied to the student performance dataset provided by the Kaggle.com website. Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision tree and K-NN algorithms were used to predict the Grade result of students as a factor of performance. The Student Performance dataset is used to forecast how well students will perform in their tests. As a result, with 94.9% accuracy, the results were predicted
Curriculum for a Certificate in Social Entrepreneurship
No full textThis curriculum guide aims to give lecturers in higher education a summary of the social business market and arm them with the knowledge and abilities needed to address the social and environmental problems our communities are currently confronting. Social businesses are effective, cutting-edge business strategies that are upending conventional business methods. Moreover, social businesses work across many industries and are motivated by the tripartite bottom line of social, environmental, and fiscal factors. Through this curriculum guidance document lecturers will discover the distinctive qualities of social businesses, their different business strategies, and their effects on people and the environment.Communities all over the globe are being transformed by social enterprises, which have surfaced as potent agents of positive social change. However, the research team for this project recognises that social business is not without difficulties and complexity. It takes resiliency, flexibility, and perseverance to overcome difficulties and succeed. The overall aim of this guide is to motivate and prepare the upcoming generation of social entrepreneurs in Ghana so they can take on these issues and build a more just and sustainable future