Objectives: To determine the impact of an educational program on nurses’ knowledge
and practices concerning neurogenic bladder rehabilitation for spinal cord injured persons
through a follow-up approach each two months post program implementation for six
months.
Methodology: "Follow-up" longitudinal design by using time series approach of data
analysis and the application of pre-post tests approach for the study and the control
groups. The study was carried out at Ibn Al-Kuff hospital for (SCI) in Baghdad governorate
from 5th of July 2010 to 15th of October 2011. To achieve the objectives of the study, a
non-probability (purposive) sample of (60) nurses (males and females) were working in SCI
units were selected. The sample is divided equally into study and control groups. A
questionnaire format was used for data collection which consisted of (3) three parts (185)
items, including their knowledge, practices, and demographic characteristics. Instrument
validity was determined through content validity, by a panel of experts. Reliability of the
instrument was determined through the use of Pearson correlation coefficient for the testretest
approach, which is (0.92) for their knowledge and (0.88) for their practices. Analysis
of data was performed through the application of descriptive statistics (frequency,
percentage) and inferential statistics (mean of scores, relative sufficiency, Pearson
correlation coefficient, t-test and one way analysis of variance and chi- square test).
71
Iraqi National Journal of Nursing Specialties, Vol. 25 (2) 2012
Results: The results of the study indicated that the nurses in study group benefited from
the implementation of health education program, their knowledge and practices were
adequately improved and developed.
Recommendations: The study recommends that there is a need to conduct annual
examinations for nurses to evaluate their nursing care for SCI persons, with a focus on the
practical side, and not upgrading any of them if they did not pass the examination
successfully.
Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
... Show MoreAn anatomical study was carried out at the College of Agricultural Engineering Sciences, University of Baghdad, in 2017, on lupine crop (Lupinus albus) as a comparison guide of three seed weights of three lupine cultivars viz. ‘Giza-1’, ‘Giza-2’ and ‘Hamburg’. The nested design was used with four replications. The results showed that cultivars had a significant effect on stem anatomical traits. ‘Hamburg’ cultivar recorded the highest stem diameter, cortex thickness and xylem vascular diameter, while cultivar ‘Giza-1’ recorded the lowest values for the same traits as well as the highest collenchyma layer thickness, vascular bundle thickness, and xylem thickness. Cultivar ‘Giza-2’ recorded the lowest vascular b
... Show MoreIn this study, Staphylococcus aureus was found to be the causative agent of furunculosis in 64 (27.5%) out of 233 Iraqi patients presented with furunculosis. 16SrRNA gene was located in all isolates. Nevertheless, mecA and lukS-lukF genes were located in 60% and 4% of S. aureus isolates, respectively. Interestingly, the lukS-lukF carrying S. aureus isolates were mecA positive as well.
The main object of this article is to study and introduce a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are the obtained results.
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show MoreIn this study, an experimental investigation had conducted for six high strength laced reinforced concrete one-way slabs to discover the behavior of laced structural members after being exposed to fire flame (high temperature). Self-compacted concrete (SCC) had used to achieve easy casting and high strength concrete. All the adopted specimens were identical in their compressive strength of ( , geometric layout 2000 750 150 mm and reinforcement specifics except those of lacing steel content, three ratios of laced steel reinforcement of (0.0021, 0.0040 and 0.0060) were adopted. Three specimens were fired with a steady state temperature of for two hours duration and then after the specimens were cooled suddenly by spraying water. The
... Show More