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疫情期间医学实习生和住院医师学习动机、策略和效果的比较分析。

Comparative analysis of learning motivation, strategies, and effectiveness between medical interns and PGY during the pandemic.

机构信息

Department of Business Administration, School of Management, National Chung Cheng University, Chiayi, Taiwan.

Medical Education Department, Chang Gung Memorial Hospital, Chiayi, Taiwan.

出版信息

Medicine (Baltimore). 2024 Sep 13;103(37):e39604. doi: 10.1097/MD.0000000000039604.

DOI:10.1097/MD.0000000000039604
PMID:39287236
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11404964/
Abstract

In the post-pandemic era, medical education faces significant shifts in learning modes. This study, employing cross-sectional research from 2021 to 2022, surveyed 214 participants, including 104 medical interns and 110 Post-Graduate Year trainees in Taiwan. Findings revealed notable differences between the groups in age and current internship hospital. Medical interns spent significantly more time in daily self-directed learning, with a higher proportion exceeding 3 hours post-work. Although weekly self-directed learning hours did not show statistical significance, more medical students studied over 7 hours weekly. In terms of learning attitudes and motivations, medical interns outscored Post-Graduate Year trainees, indicating a substantial contrast. The study suggests strategic integration of online and traditional education, tailored to course characteristics. Future research should further explore the effectiveness of online learning, aiming to optimize digital learning while preserving traditional education values.

摘要

在后疫情时代,医学教育面临学习模式的重大转变。本研究采用 2021 年至 2022 年的横断面研究,调查了 214 名参与者,包括台湾的 104 名实习医生和 110 名住院医师培训生。研究结果表明,两组在年龄和当前实习医院方面存在显著差异。实习医生在日常自主学习上花费的时间明显更多,超过 3 小时的比例更高。虽然每周自主学习时间没有统计学意义,但每周学习超过 7 小时的医学生比例更高。在学习态度和动机方面,实习医生的得分高于住院医师培训生,表明存在显著差异。研究建议将在线和传统教育进行战略性整合,根据课程特点进行定制。未来的研究应进一步探索在线学习的有效性,旨在优化数字学习的同时保留传统教育的价值。

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PLoS One. 2022 Mar 14;17(3):e0265356. doi: 10.1371/journal.pone.0265356. eCollection 2022.
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Predicting in-hospital death in pneumonic COPD exacerbation BAP-65, CURB-65 and machine learning.预测肺炎型慢性阻塞性肺疾病急性加重期的院内死亡:BAP-65、CURB-65与机器学习
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