儿童外科教学方法实践及探讨

目的探讨儿童外科的教学特点及教学方法,提高儿外科医生的总体执业水平,促进儿童外科学的发展。方法通过分析四川大学华西医院多年儿童外科疾病的临床教学实践经验,总结出适合医学生、初级儿童外科专科医生的实用的教学方法及经验。结果结合儿童外科特有的疾病特点所总结出的故事式讲述教学、典型病例教学、漫画形式学习、比较学习法及多媒体素材教学五种方式是非常适合儿童外科教学的方法。能够加深记忆,提高兴趣,轻松快捷,事半功倍的掌握相关医学专业知识。结论独特有趣的儿童外科教学方法可提高医学生及儿童外科初级从业者的学习兴趣、专业知识和执业水平。     

实体化创伤中心建设提升严重创伤救治能力

为明确实体化创伤中心建设对提升严重创伤救治能力的作用,笔者以兴义市创伤中心实体化建设后所取得的初步成效,从院内多学科团队建设及核心能力维护、创伤救治流程优化、创伤时间节点质量控制与持续改进等方面进行介绍,为实体化创伤中心建设提供借鉴和参考。     

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

翻转课堂教学法在消化内科临床带教的应用评价

目的 探讨翻转课堂教学法在消化内科临床带教的教学效果.方法 选取2018年10月—2019年10月在消化内科进行临床见习的护生48人(全部为女生),按照实习先后顺序,前24人作为试验组,给予翻转课堂教学法教学;后24人作为对照组,采用传统的带教模式.讨论两组护生在理论和操作成绩和对带教满意度方面有无差异.两组学生在试验开始之前的成绩相比较,差异无统计学意义(P>0.05).结果 试验组在理论、操作、教学满意度等方面的成绩均高于对照组,P<0.05.结论 翻转课堂教学法在消化内科的临床带教中有较好的教学效果,可以提高学生的学习成绩和教学满意度.     

新型管理模式在护理实习教学管理中的应用探讨

目的探讨在进行护理实习教学管理期间新型管理模式应用可行性。方法选择我院2018年1月-2019年2月102例实习护生作为试验对象;数字奇偶法分组后探究每组教学管理模式;对照组(51名):选择传统护理教学管理模式展开;试验组(51名):选择新型护理教学管理模式展开;比较两组护实习护生行为规范合格率、平均业务学习出勤率以及教学满意度评分结果。结果试验组实习护生规范合格率(98.04%)高于对照组(64.71%)(P<0.05);试验组实习护生平均业务学习出勤率(98.04%)高于对照组(62.75%)(P<0.05);试验组实习护生各项教学满意度评分均高于对照组(P<0.05)。结论医院实习护生在接受新型护理实习教学管理后,对于规范合格率的提升,平均业务学习出勤率的提升以及教学满意度评分的提升,均获得显著效果,最终为医院实习护生的学习效率以及护理安全提升奠定了基础。     

PBL与思维导图相结合教学模式在临床教学中的应用

目的探讨PBL与思维导图相结合教学模式在临床教学中的应用。方法本文研究对象为在我院见习的临床本科生,为大理大学2015级一个班级,共55名学生,数据收集时间为2019年1月;按照授课方式的差异将学生分为实验班与对照班,实验班27名学生,对照班28名学生;对照班实施常规教学法,实验班实施PBL与思维导图相结合教学法。结果实验班教学结束测试得分为(85.60±3.46)分,对照班教学结束测试得分为(77.48±3.12)分,差异具有统计学意义(P<0.05);实验班教学方式满意度评分为(90.35±4.12)分,对照班教学方式满意度评分为(80.25±4.05)分,差异具有统计学意义(P<0.05)。结论在临床教学中采用PBL与思维导图相结合教学模式,能够提高学生的专业知识掌握水平与对教学模式的认可度。     

在生物化学教学中培养医学生的科研创新能力

在教学过程中潜移默化培养医学生的科研创新能力，是实践新时代医学教育改革新理念的有益探索。生物化学是医学生的重要专业基础课程之一，探讨在生物化学教学过程中通过多种教学方法培养学生对科研的兴趣，同时，结合科研新进展，开拓学生的视野，培养学生的创新思维，提高医学生科研创新能力的思路。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.