Objective(s): To assess nurses' practices for neurological unconscious patients in intensive care units.
Methodology: A descriptive study was conducted that included (50) nurse who are working in intensive care
units in hospitals and departments of the nervous system in (4) hospitals (neuroscience hospital, teaching
neurosurgical hospital, surgical specialist hospital, and sheck zaied hospital) in Baghdad city from March, 30th
,
2009 to July, 30th 2009 for the purpose of assessing their skills towards unconscious patients. A purposive "nonprobability
sample" was selected that consisted of (50) nurse who are working in intensive care units. A
questionnaire format and observational checklist were used which consist of (2) parts, the first part includes
demographic information of the sample and the second part includes tools which consists of the observation
(7) parts distributed to (64) items. Reliability and validity of questionnaire and observational checklist was
estimated through a pilot study and a panel of expert. The data were analyzed by using descriptive statistical
measures which included frequencies, percentages, and standard deviation, as well as the use of inferential
statistical measures which include the correlation coefficient and coefficient of probability.
Results: The results revealed the inadequacy of skills of nurses towards unconscious patients' care in the field
of airway care, observation and record level of consciousness, hygiene, stimulate the senses and care of urinary
and digestive tract and the adequacy of skills of nurses in the field of measuring and recording vital signs,
nutrition, and the environment.
Recommendations: Based on the results of research, the study recommends initiating training courses in the
field of intensive care and care of for unconscious patients as well as to design a special mini-booklet for the
purpose of care for unconscious patients.
Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date ve
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