Your search
Results 3 resources
-
No existing review has synthesized key questions about acculturation experiences among international migrant workers. This review aimed to explore (1) What are global migrant workers’ experiences with acculturation and acculturative stress? (2) What are acculturative stress coping strategies used by migrant workers? And (3) how effective are these strategies for migrant workers in assisting their acculturation in the host countries? Peer-reviewed and gray literature, without time limitation, were searched in six databases and included if the study: focused on acculturative stress and coping strategies; was conducted with international migrant workers; was published in English; and was empirical. Eleven studies met the inclusion criteria. Three-layered themes of acculturation process and acculturative stress were identified as: individual layer; work-related layer; and social layer. Three key coping strategies were identified: emotion-focused; problem-focused; and appraisal-focused. These coping strategies were used flexibly to increase coping effectiveness and evidence emerged that a particular type of acculturative stress might be solved more effectively by a specific coping strategy. Migrant workers faced numerous challenges in their acculturative process. Understanding this process and their coping strategies could be used in developing research and interventions to improve the well-being of migrant workers.
-
COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.
-
Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.
Explore
Academic Units
Resource type
- Book (1)
- Book Section (1)
- Journal Article (1)