Objective: To find out the prevalence of anxiety and depression among Iraqi repatriated prisoners of Iran-Iraq war
(IRPOWs), and the relationship with some variables.
Methodology: A descriptive study was carried out from Oct. 18th, 2009 through Jan. 10th, 2010. A Snowball
sampling as a non-probability sampling technique was used to recruit 92 repatriates who had visited Ministry of
Human Rights. An instrument was constructed for this purpose. The constructed instrument consisted of six
demographic characteristics, and fourteen items to measure the level of anxiety and depression in prisoners of
war (POWs). Data were collected with using the constructed instrument and the process of the interview as means
for data collection. Data were analyzed through the application of descriptive statistical analysis, which are;
percentages, frequencies and inferential statistic analysis (Pearson correlation coefficient).
Results: The study revealed that the majority of IRPOWs have some levels of; anxiety (62.0%) with the levels of:
mild (28.3%), moderate (25.0%), and severe (8.7%); and depression (63.0%) with the levels of: mild (27.2%),
moderate (29.3%), and severe (6.5%). The findings also indicated that there is no significant relationship between
anxiety and depression relative to; current age, age at capture, duration of captivity, marital status, and level of
education.
Recommendations: The study recommends that it is very important to establish special mental health services
centres within the primary health care centres deal with those repatriates for counselling and in order to diagnose
and treat them and further studies in this field with follow-up studies for the POWs
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... Show MoreForecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti
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