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影响卡塔尔医学生学习方法的因素。

Factors influencing medical students' approaches to learning in Qatar.

机构信息

Weill Cornell Medicine-Qatar, P.O. Box 24144, Doha, Qatar.

Curtin University, Perth, Australia.

出版信息

BMC Med Educ. 2022 Jun 9;22(1):446. doi: 10.1186/s12909-022-03501-9.

Abstract

BACKGROUND

This study investigated the relevance of the revised 2-factor study process questionnaire (R-SPQ-2F) for exploring medical students' approaches to learning in Qatar and identify how factors like gender, age, educational attainment, and prior experience with health care influence students' adoption of deep approaches to learning.

METHODS

The sample consisted of 108 medical students (44% male, 56% female) from all four years of medical school at Weill Cornell Medicine-Qatar (WCM-Q). Participants completed the 20-item R-SPQ-2F questionnaire to measure their learning approaches through a structural model contrasting deep and surface learning. Participants also completed a survey collecting demographic information.

RESULTS

Statistical analysis revealed significant differences in deep learning approaches across year levels for both men and women. Additionally, educational attainment played a significant role in students' approaches to learning.

CONCLUSIONS

Based on structural equation modeling, this cross-verification study supports the R-SPQ-2F instrument and offers additional evidence for its robustness and application to medical education. These findings may help educational and program leaders in Qatar better understand medical students' learning approaches to enhance their pedagogical practices.

摘要

背景

本研究旨在探讨修订后的 2 因素学习过程问卷(R-SPQ-2F)在探索卡塔尔医学生学习方法方面的相关性,并确定性别、年龄、教育程度和之前的医疗保健经验等因素如何影响学生对深度学习方法的采用。

方法

该样本包括来自卡塔尔威尔康奈尔医学院(WCM-Q)所有四个医学年级的 108 名医学生(44%为男性,56%为女性)。参与者完成了 20 项 R-SPQ-2F 问卷,通过对比深度学习和表面学习的结构模型来衡量他们的学习方法。参与者还完成了一份收集人口统计信息的调查。

结果

统计分析显示,男性和女性的深度学习方法在不同年级之间存在显著差异。此外,教育程度在学生的学习方法中起着重要作用。

结论

基于结构方程建模,这项交叉验证研究支持 R-SPQ-2F 工具,并为其稳健性及其在医学教育中的应用提供了额外的证据。这些发现可能有助于卡塔尔的教育和项目领导者更好地了解医学生的学习方法,以加强他们的教学实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c0a/9178850/cead2b757a33/12909_2022_3501_Fig1_HTML.jpg

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