University of Jordan

About: University of Jordan is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Health care. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.

Prevalence and Predictors of Depression, Anxiety, and Stress among Youth at the Time of COVID-19: An Online Cross-Sectional Multicountry Study

Abstract: Depression and anxiety are prevalent mental illnesses among young people. Crisis like the Coronavirus Disease 2019 (COVID-19) pandemic may increase the current prevalence of these illnesses. A cross-sectional, descriptive design was used to (1) explore the prevalence of depression, anxiety, and stress among youth and (2) identify to what extent certain variables related to COVID-19 could predict depression, anxiety, and stress (DAS) among young people in six different countries. Participants were requested to complete an online survey including demographics and the DAS scale. A total of 1,057 participants from Oman (n = 155), Saudi Arabia (n = 121), Jordan (n = 332), Iraq (n = 117), United Arab Emirates (n = 147), and Egypt (n = 182) completed the study. The total prevalence of depression, anxiety, and stress was 57%, 40.5%, and 38.1%, respectively, with no significant differences between countries. Significant predictors of stress, anxiety, and depression were being female, being in contact with a friend and/or a family member with mental illness, being quarantined for 14 days, and using the internet. In conclusion, COVID-19 is an epidemiological crisis that is casting a shadow on youths' DAS. The restrictions and prolonged lockdowns imposed by COVID-19 are negatively impacting their level of DAS. Healthcare organisations, in collaboration with various sectors, are recommended to apply psychological first aid and design appropriate educational programmes to improve the mental health of youth.

Global Burden of Childhood Epilepsy, Intellectual Disability, and Sensory Impairments

Abstract: BACKGROUND: Estimates of children and adolescents with disabilities worldwide are needed to inform global intervention under the disability-inclusive provisions of the Sustainable Development Goals. We sought to update the most widely reported estimate of 93 million children METHODS: We analyzed Global Burden of Disease Study 2017 data on the prevalence of childhood epilepsy, intellectual disability, and vision or hearing loss and on years lived with disability (YLD) derived from systematic reviews, health surveys, hospital and claims databases, cohort studies, and disease-specific registries. Point estimates of the prevalence and YLD and the 95% uncertainty intervals (UIs) around the estimates were assessed. RESULTS: Globally, 291.2 million (11.2%) of the 2.6 billion children and adolescents (95% UI: 249.9–335.4 million) were estimated to have 1 of the 4 specified disabilities in 2017. The prevalence of these disabilities increased with age from 6.1% among children aged CONCLUSIONS: The number of children and adolescents with these 4 disabilities is far higher than the 2004 estimate, increases from infancy to adolescence, and accounts for a substantial proportion of all-cause YLD.

Salp swarm algorithm: Theory, literature review, and application in extreme learning machines

Abstract: Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability.