Exploring the Merit of Simulation-Based Education: A Systematic Review and Meta-Analysis

荟萃分析 系统回顾 计算机科学 政治学 医学 梅德林 内科学 法学
作者
Jose Foppiani,Kryštof Staněk,Angelica Hernandez Alvarez,Allan Weidman,Lauren Valentine,Il‐Hoan Oh,Khaled Albakri,Umar Choudry,Carolyn R. Rogers-Vizena,Samuel J. Lin
出处
期刊:Journal of Plastic Reconstructive and Aesthetic Surgery [Elsevier]
标识
DOI:10.1016/j.bjps.2024.01.021
摘要

Background The drive to improve surgical proficiency through advanced simulation-based training has gained momentum. This meta-analysis systematically evaluates evidence regarding the impact of plastic surgery-related simulation on residents‘ performance. Methods A systematic review of PubMed, Web of Science, and Cochrane Library was performed following PRISMA protocol. An inverse-variance random-effects model to combine study estimates was utilized to account for between-study variability. Objective Structured Assessment of Technical Skills (OSATS) scores and subjective confidence scores were used to assess the impact of the simulation with positive changes from baseline indicating better outcomes. Results Eighteen studies pooling 367 trainees who participated in various simulations were included. Completion of simulation training was associated with significant improvement in subjective confidence scores with a mean increase of 1.44 units (95% CI: 0.93 to 1.94, P < 0.001) and in OSATS scores, with a mean increase of 1.24 units (95% CI: 0.87 to 1.62, P < 0.001), both on a 1-to-5 scale. Participants reported high satisfaction scores (mean = 4.76 units, 95% CI = 4.61 to 4.91, P = 0.006), also on a 1-to-5 scale. Conclusion Participation in surgical simulation markedly improves both objective and subjective scoring metrics for surgical trainees. A variety of simulation devices are available for honing surgical skills, with the potential for more advancements to come. Because the evidence demonstrates their effectiveness, incorporating simulation into training should be a priority for the field of plastic surgery.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
李爱国应助limerencevie采纳,获得10
1秒前
研友_Z30Kz8完成签到,获得积分10
1秒前
KK完成签到,获得积分10
2秒前
卜之玉完成签到,获得积分10
3秒前
开庆完成签到,获得积分10
4秒前
灵巧的奇迹完成签到,获得积分10
4秒前
4秒前
5秒前
拼搏的青雪完成签到,获得积分10
5秒前
pan完成签到 ,获得积分10
5秒前
zkl发布了新的文献求助20
6秒前
勾勾1991完成签到,获得积分10
7秒前
李师傅完成签到 ,获得积分10
7秒前
7秒前
悦动发布了新的文献求助10
8秒前
8秒前
9秒前
是一整个圆完成签到,获得积分10
9秒前
Hannah完成签到,获得积分10
9秒前
61forsci完成签到,获得积分10
11秒前
天涯完成签到 ,获得积分10
11秒前
Scidog完成签到,获得积分10
11秒前
11秒前
11秒前
11秒前
12秒前
LOVER发布了新的文献求助10
12秒前
12秒前
自然的茉莉完成签到,获得积分10
12秒前
13秒前
13秒前
siri发布了新的文献求助10
14秒前
开心的迎海完成签到,获得积分20
14秒前
xzy发布了新的文献求助10
15秒前
香菜完成签到 ,获得积分10
15秒前
帽帽完成签到 ,获得积分20
15秒前
ruilong完成签到,获得积分10
15秒前
16秒前
Jorna发布了新的文献求助10
17秒前
ccm应助洁净的星星采纳,获得10
17秒前
高分求助中
Sustainable Land Management: Strategies to Cope with the Marginalisation of Agriculture 1000
Corrosion and Oxygen Control 600
Yaws' Handbook of Antoine coefficients for vapor pressure 500
Python Programming for Linguistics and Digital Humanities: Applications for Text-Focused Fields 500
重庆市新能源汽车产业大数据招商指南(两链两图两池两库两平台两清单两报告) 400
Division and square root. Digit-recurrence algorithms and implementations 400
行動データの計算論モデリング 強化学習モデルを例として 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2551889
求助须知:如何正确求助?哪些是违规求助? 2177786
关于积分的说明 5611342
捐赠科研通 1898686
什么是DOI,文献DOI怎么找? 948013
版权声明 565542
科研通“疑难数据库(出版商)”最低求助积分说明 504276