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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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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. |
format | Online Article Text |
id | pubmed-7006181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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