Background: Scientific education aims to be inclusive and to improve students learning achievements, through appropriate teaching and learning. Problem Based Learning (PBL) system, a student centered method, started in the second half of the previous century and is expanding progressively, organizes learning around problems and students learn about a subject through the experience of solving these problems.Objectives:To assess the opinions of undergraduate medical students regarding learning outcomes of PBL in small group teaching and to explore their views about the role of tutors and methods of evaluation. Type of the study: A cross-sectional study.Methods: This study was conducted in Kerbala Medical Colleges among second year students. A self-administered questionnaire was prepared to evaluate the newly applied teaching system. The study analysis included simple descriptive analysis and determining association through t-test, chi square test and regression analysis and using structural equation models to determine simultaneous association between different students’ demographic characteristics and potential predictors using SPSS-20 and Amos software at a significance level of < 0.05.Results:A total of 131 undergraduate medical students participated in the study with a response rate of 94%. The majority (93%) have indicated that PBL strategy contributed effectively to their knowledge development with a similar majority (92%) considering PBL successful new teaching method. About 86% reported that would choose PBL rather than conventional method and also 86% would advise PBL for others. Similarly, high majority indicated that various PBL activities are essential. Regarding the tutors’ role in PBL, the majority (92%) indicated that this role was positive and fundamental. According to two thirds (68%) of participants PBL application in Kerbala Medical college was very good application while a higher majority described various PBL sessions as successful and positive and fundamental role of tutors was stressed by most students.Conclusions: This study highlighted the benefits of soliciting student impressions of effective small group teaching. The students’ emphasized group atmosphere and facilitation skills of tutor in learning.Key words: Problem Based Learning, Medical Education, Small Group Teaching, Team Based Learning, Kerbala Medical College
Objectives:
To evaluate mothers’ attitudes toward readiness for discharge care at home for a premature baby in Intensive Care Unit at teaching hospitals in Medical City Complex and to find out the relationship between mothers’ attitudes and their socio-demographic characteristics.
Methodology: A quasi-experimental study design was carried out through the period of 6th January 2020 to 2021 to 11th March 2021, to evaluate mother’s attitude toward discharge care plan for premature babies. The study carried out in Welfare Teaching Hospital, Nursing Home Hospital and Baghdad Teaching Hospital at Medical City Complex in Baghdad City on 30 mother of premature babies in neonatal intensive care units using the nonprobability sampling
Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
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نحو تعزيز المشاركة السياسية للطالبات الجامعيات الفلسطينيات
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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