Background: A basic knowledge and understanding
of first aid can be invaluable for individuals to be
able to provide emergency care in the event of an
accident, possibly saving lives and minimizing
injury. Since injury is the fifth leading cause of death
and children at the age of primary school are liable
for accidents and lack of knowledge and good
judgment of their teachers may lead to dangerous
consequences when emergencies occur. Training
teachers how to deal with accidents is of obvious
importance.
Objectives: To estimate the adequacy of primary
school teachers’ knowledge of first aid concerning
external bleeding and fractures and Whether there's a
difference in knowledge in regard to years spent in
teaching or the teaching subject and to decide
whether primary school teachers need training or not.
Methods: This study is a cross-sectional Study was
done over 5 months (from 01/Dec/2010 to 01/May/
2011) in five randomly chosen primary schools in
different regions in Baghdad/ Al-Rusafa,carried out
on 100 primary school teachers without inclusion or
exclusion criteria.
Statistical analysis: Chi-Square was calculated by
MiniTab Program to estimate significance. The
significance level was set at P<0.05.
Results: Of the 100 participants, 4% had good
knowledge, 19% had fair knowledge and 77% had
poor knowledge, 26% were science teachers, 9%
were social studies teachers, 43% were literature
teachers and 22% were sport/art teachers. No
significant difference was found for their knowledge
(p value=0.41).Also participants were classified
according to the years they spent in teaching. No
significance was found for their knowledge (p
value=0.82).
Conclusions: Primary school teachers' knowledge of
first aid concerning external bleeding and fractures is
poor and certainly inadequate.No difference was
found in regard to how many years spent in teaching
and difference in teaching subjects also has no effect
on their knowledge.
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Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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