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 & Medicine. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.

Stress and coping strategies among Saudi nursing students during clinical education.

Abstract: Purpose The purpose of this study was to determine the stress level and coping strategies among undergraduate Saudi female nursing students during their clinical education Design and Methods : In this study, a descriptive correlational cross-sectional design was used The Demographic Information Questionnaire, Perceived Stress Scale, and Coping Behavior Inventory were completed by 121 female Saudi undergraduate nursing students Findings The findings showed that stress from taking care of the patient subscale (M = 120, SD = 50) was ranked the highest mean among all perceived stress subscale scores The most commonly used coping strategies among nursing students in clinical practice was a problem-solving strategy (M = 158, SD = 43) Practice Implications Facilitating the use of effective coping strategies in nursing students will promote quality of care for their patients

Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection

Abstract: In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.