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(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.