医学
逻辑回归
拯救生命
考试(生物学)
临床实习
急诊医学
护理部
医疗急救
内科学
生物
古生物学
作者
Kenji Imai,Yuhei Hashimoto,Yumiko Ito,Keiko Sakata,Masashi Kawanami,Tomoko Nakano‐Kobayashi,Koji Hashii,Yoshihiro Yamahata,Hiroaki Kajiyama,Tomomi Kotani
出处
期刊:Journal of obstetrics and gynaecology research
[Wiley]
日期:2024-07-02
卷期号:50 (9): 1513-1521
摘要
Abstract Aims This study aimed to evaluate the long‐term results of Japan Maternal Emergency Life‐Saving (J‐MELS) simulation training on obstetric healthcare providers, over a 12‐month follow‐up period. Methods A total of 273 trainees from 17 J‐MELS Basic courses conducted between August 2021 and October 2023 were included. The trainees' responses to the pre‐ and post‐tests, questionnaires, and self‐reports on the usefulness of the J‐MELS scenarios in actual clinical settings at 1, 6, and 12 months after the training were analyzed. Multivariate logistic regression analysis was also conducted to identify the factors influencing knowledge retention. Results We found an overall improvement in clinical knowledge acquisition after J‐MELS training and a significant retention of this improvement at least until 12 months later. However, these scores gradually declined over. Trainees reported increased usefulness of J‐MELS scenarios in actual clinical practice at 1, 6, and 12 months after training, particularly in managing obstetric emergencies such as atonic postpartum hemorrhage. Knowledge retention was influenced by several specific factors, such as years of clinical experience, affiliated institutions, qualifications, and especially pre‐test scores. Conclusion Our longitudinal follow‐up study demonstrated, for the first time, the long‐term results of J‐MELS simulation training using post‐tests and self‐report data. Our findings provide valuable insight into the impact of J‐MELS simulation training on maternal emergency care. By elucidating the factors influencing knowledge retention and practical utility, the findings offer actionable recommendations for optimizing training strategies and improving maternal outcomes in actual clinical practice.
科研通智能强力驱动
Strongly Powered by AbleSci AI