Objective(s): To evaluate nurses' knowledge toward pain management of leukemic child in oncology wards
how were receiving chemotherapy.
Methodology: A descriptive study was conducted in two hospitals on (40) nurses, who provided care for the
children with leukemia in oncology wards (2) hospitals (Children Welfare Teaching Hospital and Child’s Central
Teaching Hospital) in Baghdad city from October 2010 up to the 27th of October 2011 for the purpose of
evaluating their knowledge towards pain management for leukemic child. A purposive "non-probability
sample" was selected that consisted of (40) nurse who are working in oncology wards. A questionnaire format
was used which consist of (2) parts, the first part includes demographic information of the sample and the
second part consists of structured Items concerning nurses' knowledge toward pain management for leukemic
child which includes (3) main sections and comprised of (89) Items. Reliability and validity of questionnaire 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 chi- square test.
Results: The results revealed inadequate nurse's knowledge about pain management for leukemic child under
chemotherapy, two third (75 %) of the sample has poor knowledge toward pharmacological and non
pharmacological pain management methods.
Recommendations: Based on the results of research, the study recommends initiating training courses in pain
management and developing pain assessment tools for assessing child's pain and evaluating the level of pain
management procedures
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This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different
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