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运用文本挖掘分析日本医学生在农村社区实习后的反思性文章。

Using text mining to analyze reflective essays from Japanese medical students after rural community placement.

机构信息

General Studies Department, Jichi Medical University, Tochigi, Japan.

Division of Community and Family Medicine, Center for Community Medicine, Jichi Medical University, Tochigi, Japan.

出版信息

BMC Med Educ. 2020 Feb 6;20(1):38. doi: 10.1186/s12909-020-1951-x.

Abstract

BACKGROUND

Following community clinical placements, medical students use reflective writing to discover the story of their journey to becoming medical professionals. However, because of assessor bias analyzing these writings qualitatively to generalize learner experiences may be problematic. This study uses a process-oriented text mining approach to better understand meanings of learner experiences by connecting key concepts in extended student reflective essays.

METHODS

Text mining quantitative analysis is used on self-evaluative essays (n = 47, unique word count range 43-575) by fifth-year students at a regional quota-system university in Japan that specializes in training general practitioners for underserved communities. First, six highly-occurring key words were identified: patient, systemic treatment, locale, hospital, care, and training. Then, standardized keyword frequency analysis robust to overall essay length and keyword volume used individual keywords as "nodes" to calculate per-keyword values for each essay. Finally, Principle Components Analysis and regression were used to analyze key word relationships.

RESULTS

Component loadings were strongest for the keyword area, indicating most shared variance. Multiply regressing three of the remaining keywords hospital, systemic treatment, and training yielded R = 0.45, considered high for this exploratory study. In contrast, direct patient experience for students was difficult to generalize.

CONCLUSIONS

Impressions of the practicing area environment were strongest in students, and these impressions were influenced by hospital workplace, treatment provision, and training. Text mining can extract information from larger samples of student essays in an efficient and objective manner, as well as identify patterns between learning situations to create models of the learning experience. Possible implications for community-based clinical learning may be greater understanding of student experiences for on-site precepts benefitting their roles as mentors.

摘要

背景

在社区临床实习之后,医学生会通过反思性写作来发现自己成为医学专业人员的历程。然而,由于评估者的偏见,对这些写作进行定性分析以概括学习者的经验可能会有问题。本研究使用面向过程的文本挖掘方法,通过连接扩展学生反思性文章中的关键概念,更好地理解学习者经验的含义。

方法

文本挖掘的定量分析应用于日本一所地区配额制大学五年级学生的自我评估性论文(n=47,独特单词计数范围为 43-575),该大学专门为服务不足的社区培训全科医生。首先,确定了六个高频出现的关键词:患者、系统治疗、地点、医院、护理和培训。然后,使用标准化的关键词频率分析方法,针对文章的整体长度和关键词数量,对每个关键词进行了规范化处理,计算出每个论文的关键词值。最后,使用主成分分析和回归分析来分析关键词之间的关系。

结果

关键词区域的成分负荷最强,表明具有最多的共享方差。多元回归分析将剩下的三个关键词——医院、系统治疗和培训进行回归,得出的 R 值为 0.45,对于这项探索性研究来说是较高的。相比之下,学生直接接触患者的经验很难进行概括。

结论

学生对实习环境的印象最强烈,这些印象受到医院工作场所、治疗提供和培训的影响。文本挖掘可以从大量学生论文中以高效和客观的方式提取信息,并识别学习情境之间的模式,从而为学习体验创建模型。这可能对基于社区的临床学习具有重要意义,可以更好地理解学生的经验,从而使现场导师受益于他们的指导角色。

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