Abstract：
This umbrella review aimed to consolidate the evidence base on the impact of high-fidelity simulation on knowledge and performance among undergraduate nursing students.
Umbrella review with meta-analyses of pooled effect sizes, followed by an additional meta-analysis of primary studies from the included systematic reviews, excluding overlapping results.
Systematic searches were performed up to August 2023 in PubMed, Embase, and Cochrane Library. We included reviews that compared high-fidelity simulation against other learning strategies.
The risk of bias was assessed for each included systematic review (ROBIS tool) and primary study (RoB 2 or ROBINS-I as appropriate). Random-effect meta-analyses of meta-analyses were performed to estimate the pooled effects of high-fidelity simulation on knowledge and performance. Further random-effect meta-analyses of primary studies were conducted, with overlapping studies excluded (12 %). Subgroup analyses were performed to provide a more comprehensive analysis of the findings. Trim-and-fill analyses were conducted to adjust for potential publication bias.
Six systematic reviews were included and encompassed 133 primary studies (2767 and 3231 participants concerning performance and knowledge, respectively). The adjusted pooled effects for knowledge (SMD = 0.877, 95 % CI: 0.182 to 1.572) and performance (SMD = 0.738, 95 % CI: 0.466 to 1.010) closely aligned with those obtained from meta-analyzing the primary studies for knowledge (SMD = 0.980) and performance (SMD = 0.540), both showing high statistical heterogeneity. Traditional lectures represented the more common comparison. The subgroup analysis revealed significant differences in effect sizes across geographic locations, topics, types of control, and how interventions were reported.
The results provide robust evidence supporting the integration of high-fidelity simulation into undergraduate nursing programs to enhance students\' knowledge and performance. The high reported heterogeneity may be attributed to variations in study contexts or methodologies. Future research should explore the optimal use of high-fidelity simulation in different educational and cultural contexts.
Umbrella review with meta-analyses of pooled effect sizes, followed by an additional meta-analysis of primary studies from the included systematic reviews, excluding overlapping results.
Systematic searches were performed up to August 2023 in PubMed, Embase, and Cochrane Library. We included reviews that compared high-fidelity simulation against other learning strategies.
The risk of bias was assessed for each included systematic review (ROBIS tool) and primary study (RoB 2 or ROBINS-I as appropriate). Random-effect meta-analyses of meta-analyses were performed to estimate the pooled effects of high-fidelity simulation on knowledge and performance. Further random-effect meta-analyses of primary studies were conducted, with overlapping studies excluded (12 %). Subgroup analyses were performed to provide a more comprehensive analysis of the findings. Trim-and-fill analyses were conducted to adjust for potential publication bias.
Six systematic reviews were included and encompassed 133 primary studies (2767 and 3231 participants concerning performance and knowledge, respectively). The adjusted pooled effects for knowledge (SMD = 0.877, 95 % CI: 0.182 to 1.572) and performance (SMD = 0.738, 95 % CI: 0.466 to 1.010) closely aligned with those obtained from meta-analyzing the primary studies for knowledge (SMD = 0.980) and performance (SMD = 0.540), both showing high statistical heterogeneity. Traditional lectures represented the more common comparison. The subgroup analysis revealed significant differences in effect sizes across geographic locations, topics, types of control, and how interventions were reported.
The results provide robust evidence supporting the integration of high-fidelity simulation into undergraduate nursing programs to enhance students\' knowledge and performance. The high reported heterogeneity may be attributed to variations in study contexts or methodologies. Future research should explore the optimal use of high-fidelity simulation in different educational and cultural contexts.
摘要：
目的：本综述旨在巩固有关高保真模拟对护理本科生知识和绩效影响的证据基础。
方法：雨伞综述，对合并效应大小进行荟萃分析，随后对纳入的系统评价中的主要研究进行了额外的荟萃分析，排除重叠的结果。
方法：截至2023年8月，在PubMed进行了系统搜索，Embase,科克伦图书馆我们纳入了将高保真模拟与其他学习策略进行比较的评论。
方法：评估每个纳入的系统评价（ROBIS工具）和主要研究（适当的RoB2或ROBINS-I）的偏倚风险。进行荟萃分析的随机效应荟萃分析，以评估高保真模拟对知识和性能的综合影响。对主要研究进行了进一步的随机效应荟萃分析，排除重叠研究（12%）。进行亚组分析以提供更全面的结果分析。进行了修剪和填充分析，以调整潜在的发表偏倚。
结果：纳入了六项系统评价，涵盖了133项主要研究（2767和3231名参与者涉及绩效和知识，分别)。调整后的知识（SMD=0.877，95％CI：0.182至1.572）和性能（SMD=0.738，95％CI：0.466至1.010）的合并效应与通过荟萃分析主要研究获得的知识（SMD=0.980）和性能（SMD=0.540），两者都表现出高度的统计异质性。传统的讲座代表了更常见的比较。亚组分析显示，不同地理位置的效果大小存在显着差异，主题,控制类型,以及如何报告干预措施。
结论：结果提供了有力的证据，支持将高保真模拟整合到本科护理课程中以提高学生的知识和表现。报告的高度异质性可能归因于研究背景或方法的变化。未来的研究应该探索高保真模拟在不同教育和文化背景下的最佳使用。
方法：雨伞综述，对合并效应大小进行荟萃分析，随后对纳入的系统评价中的主要研究进行了额外的荟萃分析，排除重叠的结果。
方法：截至2023年8月，在PubMed进行了系统搜索，Embase,科克伦图书馆我们纳入了将高保真模拟与其他学习策略进行比较的评论。
方法：评估每个纳入的系统评价（ROBIS工具）和主要研究（适当的RoB2或ROBINS-I）的偏倚风险。进行荟萃分析的随机效应荟萃分析，以评估高保真模拟对知识和性能的综合影响。对主要研究进行了进一步的随机效应荟萃分析，排除重叠研究（12%）。进行亚组分析以提供更全面的结果分析。进行了修剪和填充分析，以调整潜在的发表偏倚。
结果：纳入了六项系统评价，涵盖了133项主要研究（2767和3231名参与者涉及绩效和知识，分别)。调整后的知识（SMD=0.877，95％CI：0.182至1.572）和性能（SMD=0.738，95％CI：0.466至1.010）的合并效应与通过荟萃分析主要研究获得的知识（SMD=0.980）和性能（SMD=0.540），两者都表现出高度的统计异质性。传统的讲座代表了更常见的比较。亚组分析显示，不同地理位置的效果大小存在显着差异，主题,控制类型,以及如何报告干预措施。
结论：结果提供了有力的证据，支持将高保真模拟整合到本科护理课程中以提高学生的知识和表现。报告的高度异质性可能归因于研究背景或方法的变化。未来的研究应该探索高保真模拟在不同教育和文化背景下的最佳使用。
