通过基于图形的教育工具提高对宏观体的理解:随机对照试验 (MATE)
Kristen A Cagino1, Myra Kurjee1, Emily Hyde1
1Department of Obstetrics, Gynecology and Reproductive Sciences, McGovern Medical School at UTHealth Houston, Houston, Texas.
American journal of perinatology
|March 25, 2025
概括
一个基于图形的教育工具 (GBET) 显著提高了孕妇对巨症风险因素和并发症的知识. 然而,该工具并没有显著改善对宏观的管理选项的了解.
科学领域:
- 产科和妇科 产科和妇科
- 医疗教育 医学教育
- 公共卫生 公共卫生
背景情况:
- 巨症是一种过度胎儿生长的疾病,对母亲和婴儿都有重大风险.
- 孕妇中普遍存在关于宏观症的风险因素,并发症和管理选择的知识差距.
- 有效的患者教育工具对于改善理解和潜在地减轻与宏观症相关的不良结果至关重要.
研究的目的:
- 评估基于图形的教育工具 (GBET) 在提高孕妇对宏观体的知识方面的有效性.
- 为了比较接受GBET和接受常规护理的参与者之间所获得的知识.
- 评估与宏观的风险因素/并发症 (RF/C) 和管理选项 (MO) 相关的知识.
主要方法:
- 一项随机对照试验 (NCT06281301) 针对符合特定妊娠年龄和健康标准的孕妇进行.
- 参与者被随机分配,接受GBET或常规护理.
- 通过使用17个问题问卷评估知识,重点是RF/C (11个问题) 和MO (6个问题) 对于macrosomia.
主要成果:
- 与常规护理组相比,GBET组的整体知识得分明显高 (70%对64%,p < 0.001).
- 在GBET组中,风险因素和并发症 (RF/C) 的知识得分显著提高 (72%对63%,p=0.001).
- 两组之间管理选项 (MO) 的知识得分没有显著差异 (65%对68%,p = 0.084).
结论:
- 这种以图形为基础的教育工具有效地提高了孕妇对宏观症风险因素和并发症的了解.
- 在这个研究群体中,GBET并没有显著提高对宏症管理选择的理解.
- 需要进行进一步的研究,以探索增强的知识是否会转化为改善宏观的临床结果.
更多相关视频
12:29Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
Published on: May 3, 2017
9.7K
06:49A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development
Published on: October 29, 2019
6.6K
相关概念视频
02:01Group Design
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
01:13Multiple Comparison Tests
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
01:09Comparing Experimental Results: Student's t-Test
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...