Objective(s): To assess the level of depression and anxiety among school age children with acute lymphoblastic leukemia under chemotherapy treatment and to find out the relationship between the level of depression and anxiety among the affected children and their demographic characteristics.
Methodology: A cross-sectional study was conducted on school age children both gender having acute lymphoblastic leukemia under chemotherapy treated and their age between 6 years to 12 years. The study started from the period of September, 19th 2020 to March,1st 2021. Non-probability (Purposive) sample of (114) children with acute lymphoblastic leukemia under chemotherapy was selected in attending hospital wards, outpatient and counseling clinics. The questionnaire is designed and composed of two parts: the first part includes two sections that deal with the children’s demographic data, and information about disease to children, the second part includes hospital anxiety and depression scale. The reliability of the questionnaire is determined through a pilot study and the validity through a panel of (12) experts. The data are collected by the use of the questionnaire. The data are described statistically and analyzed through the use of descriptive and inferential statistical analysis procedures.
Results: The results of the present study indicated that level of depression and anxiety of the most children with acute lymphoblastic leukemia had moderate level, no Significance is found between the level of depression and anxiety and their demographic characteristics of children
Recommendations: Establishment of specialized centers to treat depression and anxiety for children who are treated for acute lymphoblastic leukemia
Autorías: Nuha Mohsin Dhahi, Muhammad Hamza Shihab. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.
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... Show MoreThe first successful implementation of Artificial Neural Networks (ANNs) was published a little over a decade ago. It is time to review the progress that has been made in this research area. This paper provides taxonomy for classifying Field Programmable Gate Arrays (FPGAs) implementation of ANNs. Different implementation techniques and design issues are discussed, such as obtaining a suitable activation function and numerical truncation technique trade-off, the improvement of the learning algorithm to reduce the cost of neuron and in result the total cost and the total speed of the complete ANN. Finally, the implementation of a complete very fast circuit for the pattern of English Digit Numbers NN has four layers of 70 nodes (neurons) o
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