Impact of general practice / family medicine clerkships on Japanese medical students: Using text mining to analyze reflective writing
Bibliographic record
Abstract
BACKGROUND: In order for general practice / family medicine clerkships to be improved in undergraduate medical education, it is necessary to clarify the impacts of general practice / family medicine clerkships. Using text mining to analyze the reflective writing of medical students may be useful for further understanding the impacts of clinical clerkships on medical students. METHODS: The study involved 125 fifth-year Fukushima Medical University School of Medicine students in the academic year 2018-2019. The settings were three clinics and the study period was 5 days. The clerkships included outpatient and home visits. Students' reflective writing on their clerkship experience was collected on the final day. Text mining was used to extract the most frequent words (nouns) from the reflective writing. A co-occurrence network map was created to illustrate the relationships between the most frequent words. RESULTS: 124 students participated in the study. The total number of sentences extracted was 321 and the total number of words was 10,627. The top five frequently-occurring words were patient, home-visit, medical practice, medical care, and family. From the co-occurrence network map, a co-occurrence relationship was recognized between home-visit and family. CONCLUSION: Data suggest that medical students may learn the necessity of care for the family as well as the patient in a home-care setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".