Evaluating an Interdisciplinary EEG Initiative on In-Training Examination EEG-Related Item Scores for Anesthesiology Residents.

JOURNAL OF CLINICAL NEUROPHYSIOLOGY(2019)

引用 6|浏览9
暂无评分
摘要
Purpose: Clinical neurophysiology is an evolving area of medicine with clinical applications in intensive care unit and intraoperative settings, where EEG is used. An interdisciplinary module was implemented over 7 years in one institution to strengthen anesthesiology residents' EEG education. This study researched the module's outcome by evaluating participants' specific performance on EEG-related questions (keywords) through independent testing, i.e., the in-training examinations (ITEs). Methods: Residency program ITE performance reports from 2002 to 2014 were searched for EEG keyword items. The ITE uses images for assessment. Analysis of variance was used to evaluate differences in the composite performance (mean percent correct on EEG-related keywords) of anesthesiology trainees from their clinical anesthesia year 1 (CA-1) to their clinical anesthesia year 3 (CA-3) who received the education module and compared with those who did not receive the training module, as well as compared with the national average for the corresponding training level. Results: Residents who received the education module (mean percent correct = 83.3%, 95% CI: 74.0-92.7) performed significantly better than residents within the same program who did not receive the module (P = 0.04; mean difference = 22.0%, 95% CI: 1.0-43.0), as well as national residents on the same keywords (P = 0.01; mean difference = 23.4%, 95% CI: 3.9-42.9). Differences between residents who did not receive the module and national residents (matched for same keywords) were not statistically significant (P = 0.983, mean difference = 5.2%, 95% CI: 217.3 to 27.7). Conclusions: The multidisciplinary education module was effective for the EEG-specific topics as measured by the national ITE examination performance that resulted in sustained learning over time.
更多
查看译文
关键词
Electroencephalography,Educational measurement,Graduate medical education,Long-term outcome,Interdisciplinary education
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要