Objective(s): To Evaluate Diabetes self –management among patients in Baghdad City and to compare
between these patients self-management relative to the type of the disease.
Methodology: A descriptive design was conducted in Baghdad city, started from November 16th 2017 to the
end of May 17 th 2018 in order to evaluate Diabetes self-management. Purposive (non-probability) sample,
which was consisted of (120) patients who were diagnosed with D.M. The sample is comprised of (60) patient
with diabetes type I and (60) patient with diabetes type II. It is consisted of (60) male and (60) female. A
questionnaire is constructed for the purpose of the study. It is composed of (42) items. Reliability and validity of
the questionnaire is determined through pilot study. Data are collected through the use of the questionnaire and
the structured interview as means of data collection. Data are analyzed through the application of descriptive
statistical data analysis approach of frequency, percentage, mean, range and total scores and inferential
statistical data analysis approach of t-test.
Results: The results show that more than a half have a poor level of diabetes self-management (n = 64; 53.3%)
and a lesser proportion who have a good level of such management.
Recommendations: Further study can be conducted on subjects with a large sample size. An education
program can be done to improve patient knowledge about the diabetes self-management. Better attention can be
directed for patients who were students, married and low income ones
Good governance of service quality through the adoption of sustainable energy the study of A1- Karkh historic center of in Baghdad city
Nonlinear time series analysis is one of the most complex problems ; especially the nonlinear autoregressive with exogenous variable (NARX) .Then ; the problem of model identification and the correct orders determination considered the most important problem in the analysis of time series . In this paper , we proposed splines estimation method for model identification , then we used three criterions for the correct orders determination. Where ; proposed method used to estimate the additive splines for model identification , And the rank determination depends on the additive property to avoid the problem of curse dimensionally . The proposed method is one of the nonparametric methods , and the simulation results give a
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