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

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 s...

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Autores principales: Lebowitz, Adam, Kotani, Kazuhiko, Matsuyama, Yasushi, Matsumura, Masami
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006181/
https://www.ncbi.nlm.nih.gov/pubmed/32028939
http://dx.doi.org/10.1186/s12909-020-1951-x
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author Lebowitz, Adam
Kotani, Kazuhiko
Matsuyama, Yasushi
Matsumura, Masami
author_facet Lebowitz, Adam
Kotani, Kazuhiko
Matsuyama, Yasushi
Matsumura, Masami
author_sort Lebowitz, Adam
collection PubMed
description 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(2) = 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.
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spelling pubmed-70061812020-02-11 Using text mining to analyze reflective essays from Japanese medical students after rural community placement Lebowitz, Adam Kotani, Kazuhiko Matsuyama, Yasushi Matsumura, Masami BMC Med Educ Research Article 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(2) = 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. BioMed Central 2020-02-06 /pmc/articles/PMC7006181/ /pubmed/32028939 http://dx.doi.org/10.1186/s12909-020-1951-x Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lebowitz, Adam
Kotani, Kazuhiko
Matsuyama, Yasushi
Matsumura, Masami
Using text mining to analyze reflective essays from Japanese medical students after rural community placement
title Using text mining to analyze reflective essays from Japanese medical students after rural community placement
title_full Using text mining to analyze reflective essays from Japanese medical students after rural community placement
title_fullStr Using text mining to analyze reflective essays from Japanese medical students after rural community placement
title_full_unstemmed Using text mining to analyze reflective essays from Japanese medical students after rural community placement
title_short Using text mining to analyze reflective essays from Japanese medical students after rural community placement
title_sort using text mining to analyze reflective essays from japanese medical students after rural community placement
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006181/
https://www.ncbi.nlm.nih.gov/pubmed/32028939
http://dx.doi.org/10.1186/s12909-020-1951-x
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