Objective: This study aims to assess the level of nurse's knowledge regarding toxoplasmosis management
in pregnant women.
Methodology: A descriptive analytic study was carried out from January 2012 to March 2012. A sample of
(70)nurses who provide prenatal care to pregnant women at primary health care centers of AL-Adala,ALHindia,AL-Askary,AL-Jamea,AL-Ansar
and AL-Salam in AL-Najaf city. The questionnaire was self-completed
and included questions on sociodemographic characteristics and toxoplasmosis aspects.
Results: The findings of the study indicated that (44.3%) of nurses have moderate level of knowledge.
(32.9%) of nurses was with age ranging from 31-36 years. (74.3%) were male. (52.9%) were secondary
graduate,(31.4) were institute graduates, regarding the training sessions the majority of nurses
(84.3%)have no training sessions, (34.3%) of nurses who were included in the study have duration of
experience ranging from 7-13 years. Highly significant relation between nurse‘s level of knowledge and
their age and years of experience (0.009, 0.024) respectively
Recommendations: The study recommended that: it is necessary to join all nurses who work in prenatal
care units in workshop training, continuing education sessions regarding toxoplasmosis aspects and other
mother –child issues to improve health awareness. Another study should be conducted to investigate the
association between the prevalence of toxoplasmosis and affectivity of prenatal care in preventing
toxoplasmosis.
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Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.
This was a cross‐sectional study based on electronic survey data that was collected in Iraq during December first‐19th, 2020. The electronic surve
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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