Objectives: The present study aims at detecting the depression among nurses who provide care for infected patients with corona virus phenomenon and to find out relationships between the depression and their demographic characteristics of age, gender, marital status, type of family, education, and years of experience of nurses in heath institutions, infection by corona virus, and their participation in training courses.
Methodology: A descriptive study is established for a period from October 10th, 2020 to April 15th, 2021. The study is conducted on a purposive (non-probability) sample of (100) nurse who are providing care for patients with COVID-19 and they are selected from the isolation wards. The instrument of the study is developed from Patients’ Health Questionnaire (PHQ) to achieve the study objectives. Content validity of the instrument is determined through panel of experts and internal consistency reliability is obtained through pilot study. Data are collected through the use of the questionnaire and analyzed through the application of descriptive and inferential statistical approaches which are applied by using SPSS version 22.
Results: The results of the present study showed that nurses who were providing care for patients with COVID-19 age group (30-39 years) )37%(, males constituent the higher percentage than female 87%, (77%) of them is married, (59%) Small family of Nurses, )64%( level of education among nurses have diploma in nursing, and they have (1-5) years of experience in heath institutions among nurses about (40%), also (61%) of nurses not sharing in epidemiological training courses, and (58%) of nurses had previous work in isolation wards, (39%) of nurses have source of information from network, duration of work in isolation wards is (83%) of nurses who are work for more than four weeks, (70%) of nurses are not infected with corona virus, (96%) of nurses are having no history of mental disorders, (54%) of nurses are not drinking alcohol and having no problem with drug abuse. By using PHQ-9, the study finds that depression among nurses is (43%).
Recommendations: Psychological care counseling and guidance are necessary to increase nurses’ vulnerability and strengthen their mental health which helps to encounter any psychological burden caused by COVID 19 pandemic.
Background : Double diabetes (DD) is the term used to describe situations in which a patient exhibits characteristics that are a combination of type 1 diabetes mellitus(T1DM) and type 2 Diabetes Mellitus (T2DM) a large epidemiological study found that 25.5% of people with T1D also had the metabolic syndrome. A new protein hormone called asprosin is predominantly released by white adipose tissue. It was initially discovered in 2016 . Asprosin is important diagnoses marker for insulin resistant in diabetes patients ,additionally is very important denotation about early diagnoses of type 2 diabetes. Objectives: The current study aims to find predictive significance of diagnosis a double diabetes by evaluating the asprosin in the blood serum of
... Show MoreBackground: A role for vitamin D deficiency in Parkinson disease (PD) has recently been suggested.
Objective:: To estimate the state of vitamin D in PD with an age-matched healthy control.
Type of the study: A case control study.
Method: The study randomly comparison of plasma 25-hydroxyvitamin D (25[OH] D) concentrations of collected samples in a clinical neurology department ward / Baghdad teaching hospital / Medical City and Parkinson disease movement disorder clinic. Participants were registered into the study from October 2015 to October 2016. We was study serum vitamin D level in 40 consecutive patients with
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Bladder dysfunction is one of the most common complications of diabetes, even exceeding nephropathy or peripheral neuropathy. Diabetic cystopathyaffects patients in both sexes, and its prevalence increases over time with diabetes; our concern is to evaluate the urodynamic findings of bladder dysfunction in diabetic patients.A cross sectional study conducted at Ghazi Al-Hariri Surgical specialized hospital during the period from the firsts of January 2018 to the end of Mar 2019, in which 118 diabetic patients (71 female and 47 male) with lower urinary tract symptoms were enrolled in the current study. The mean age (62±13) years old, 37.3% of patients presented with urgency as the main type of dysfunction. Diabetic cystopathy were fo
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Hyperprolactinemia is a common endocrine abnormality caused by physiological factors like pregnancy and lactation, drug-induced factors like antipsychotics, pituitary adenomas that secrete prolactin, or stalk compression or section that reduces dopamine inhibition. Dopamine agonists cure most prolactinomas.
To assess response to treatment in micro versus macroprolactinoma.
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
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Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob
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This research aims to present a proposed model for disclosure and documentation when performing the audit according to the joint audit method by using the questions and principles of the collective intelligence system, which leads to improving and enhancing the efficiency of the joint audit, and thus enhancing the confidence of the parties concerned in the outputs of the audit process. As the research problem can be formulated through the following question: “Does the proposed model for disclosure of the role of the collective intelligence system contribute to improving joint auditing?”
The proposed model is designed for the disclosure of joint auditing and the role
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