不良产后事件发生在美国所有交付中的约9%。1
Nurse educators who train novices in obstetrics know it’s critical for your program to prepare learners to keep mother and baby safe when delivery complications arise. As simple as this may sound, it can be a challenge to make sure that your learners are exiting the program fully prepared for these low-frequency, high-risk events. In order to ensure learners are optimally prepared for practice,实现依赖有效学习方法的框架的能力是关键。
In 1984, David Kolb developed his experiential learning theory. It asserts that learners must progress through four stages of a learning cycle, with each stage involving different experiences, in order to effectively learn2Kolb's theory strongly suggests thatno single teaching method will stand on its own。For a maternal nursing program that covers everything from basic concepts to complicated delivery procedures and team dynamics, this can be especially true. Instead, using a range of complementary teaching methods allows learners to connect the dots between areas of learning, building on their constructive knowledge as they progress through the program.
在一个纪律和母亲护理的复杂和生活中,高保真仿真培训通常被视为它为学习经历带来的现实主义。除了动手经验的价值之外,它还提供了在团队合作和沟通技能方面的培训,可以帮助最大限度地减少对新母亲和婴儿的伤害。专注于劳动和交付的一项研究得出结论认为,基于模拟的团队培训在最大限度地减少通过沟通和团队合作缺陷的主要原因而导致的负面结果方面发挥着关键作用3.。
For learners to get the most out of high-fidelity simulation training, they should be as confident as possible before they enter the simulation lab. Incorporating virtual simulation is one way to reinforce classroom knowledge and boost learner preparedness. Virtual simulation offers learners an interactive learning experience. It allows learners to work with realistic virtual patients, virtual simulation helps learners develop key reasoning, prioritization, and decision-making skills – all of which can be helpful assets to carry with them into the simulation lab.
下面,我们探索如何将虚拟仿真ed as a bridge between classroom learning and high-fidelity simulation. And if you’re already using virtual simulation, you’ll find out how you can use it as a powerful tool to prepare learners for more complex learning methods like high-fidelity simulation.