WeChat Mini Program
Old Version Features

“立体整合、医患共教、模拟实操”教学模式在脊柱外科教学中的应用

Chinese Journal of Medical Education Research(2023)

Cited 0|Views15
Abstract
目的:探讨“立体整合、医患共教、模拟实操”教学模式在脊柱外科教学中的应用及效果。方法:选取2021年在北京协和医院外科实习的临床医学八年制学生64名作为研究对象。按照随机数字表法将其分为试验组(33名)与对照组(31名),试验组采用“立体整合、医患共教、模拟实操”的新教学模式,对照组采用常规教学方案。教学效果评价指标包括理论测试、解剖结构辨识测试及问卷调查分析。应用SPSS 22.0软件进行配对 t检验及两独立样本 t检验。 结果:试验组授课后理论测试成绩(51.25±6.99)分及解剖结构辨识成绩(37.56±1.83)分均高于对照组(42.46±6.13)分和(30.37±3.46)分,差异有统计学意义( P<0.001)。调查问卷有效回收率100%,问卷结果显示:试验组在教学吸引力、课堂专注度、学习兴趣、学习效率、解剖辨识能力、发现解决问题能力及总体教学方式满意度方面均高于对照组( P<0.05)。 结论:“立体整合、医患共教、模拟实操”教学模式可有效提高学生理论知识、学习兴趣、学习效率、实践操作和发现解决问题的能力,值得推广。
More
Translated text
Key words
Multidisciplinary integration,Doctors & patients co-teaching,3D printing,Teaching mode,Spine surgery,Clinical teaching
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined