Objectives of the study: The study aims to assess satisfy of the coronary artery patients for the care product from
the nurse and physician and to find out the relationship between patient satisfaction with the social and the
clinical characteristics of the patients.
Methodology: A descriptive design study conducted using the evaluation approach for the duration of June 3
rd
2012 to January 31, 2013. Non-probability sample of (60) patients who were visiting or admitted (inpatient or
outpatient) to the teaching hospital in Baquba with the diagnosis of coronary artery disease. A questionnaire have
been built and develop by the researcher based on review of literature and previous research, the form included of
(3) parts: Part I demographic characteristics such as age, gender, education level, marital status, occupation,
monthly income, the second part of clinical data and the third part assessing the satisfaction of patient about the
quality of the physicians care, the quality of nurses care and satisfaction in general.
(12).
Reliability was identified
through the use of (test and retest).The validity was identified by offering experts with competence. The data were
analyzed using descriptive statistics and inferential statistical analysis approach
Results: The results of the study showed that the highest percentage (53.3%) was males under age groups (60-69)
and (36.7%) of the sample were unable to read and write. The study showed that (75.0%) of the sample were
married, (41.7%) of the sample were female and housewife (61.7%) of the respondents live in the city, and (91.7%)
of the sample had one source of income and (61.7%) of the sample had not sufficiently a monthly income. The
results of the study shows that the patients with coronary artery disease were not satisfy with the care provided
by the nurse, the physician and general care in most of the sub domains.
Recommendations: The study recommended there is a need to writing standard education program for the nurse
and the physician in cardiac care unit to improve the care provided to the patient and establishment cardiac center
rehabilitation in all governorate to produce the service to the patient
Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to
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