Schizophrenic patients who are at great risk of relapse are characterized by non-compliance,
denial of illness and need for treatment and no contact with family. So, the prevention of relapse
and readmission to hospital are crucial in mental health practice.
The present study is a descriptive-analytical study that was carried out from November 2nd
2006 through the end of 20 of April 2008.
Objectives: To assess the associated factors with the risk of relapse in schizophrenic patients at
psychiatric hospitals in Baghdad city.
Methodology: A purposive "non-probability" sample of (50) schizophrenic patient who hasd
relapsed was involved in the present study. Data were collected through the use of the constructed
questionnaire and the process of the interview as means for data collection.
The questionnaire was constructed by the researcher to achieve the objectives of the study,
which consisted of two parts; the first one is concerned with the demographic and clinical
characteristics; the second part includes the risk factors of relapse and contains (68) item that
describe the factors related to the family support, factors related to the family hardship and factor
related to the treatment compliance.
Data were analyzed through the application of descriptive statistical analysis (frequency,
percentage, mean and mean of scores and the relative sufficiency and inferential statistic (Multiple
Logistic Regression).
Results: The findings of the study indicated that there was a high family support for schizophrenic
patients, high family hardship, moderate level of non-compliance to the treatment and more than
half of the patients had (30%) chance of relapse related to the recent exposure to recent life events.
The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as 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 long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h
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