Objective: To assess the clinical learning environment and clinical training for students' in maternal and child
health nursing.
Methodology: A descriptive study was conducted on non probability sample (purposive) of (175) students' in
Nursing College/ University of Baghdad for the period of June 19th to July 18th 2013. A questionnaire was used as a
tool of data collection to fulfill with objective of the study and consisted of three parts, including demographic,
clinical learning environment and clinical training for students' in maternal and child health nursing. Descriptive
statistical analyses were used to analyze the data.
Results: The results of the study revealed that the 65.1% of student at age which ranged between (19-23) years
and 56% were male student, 66.9% were third year nursing students, and 59.4% were morning study. The study
revealed that there were high mean score response among study sample except item (11) the response was (No)
in which as (The learning environment in the hospital with a homogeneous environment college) at student's
attitude's regarding clinical learning environment. And the study revealed that there were high mean score
response among study sample at the clinical training.
Recommendations: The study recommended to need to conduct other the researches to evaluate the actual
clinical learning environment for nurse's skills and practices performance in the hospital. And to determine factors
influence student's during clinical learning environment and clinical training
This study specifically contributes to the urgent need for novel methods in Training of Trainers (ToT) programs which can be more effective and efficient through incorporation of AI tools. By exploring scenarios in which AI could be used to dramatically advance trainer preparation, knowledge-sharing, and skill-building across sectors, the research aims to understand the possibility. This study uses a mixed-methods approach, it surveys 500 trainers and conducts in-depth interviews with a further 50 ToT program directors across diverse industries to evaluate the impact of AI-enhanced ToT programs. The results showcase that the use of AI has a substantial positive effect on trainer performance and program outcomes. AI-enhanced ToT programs, fo
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thi
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