Impact of general practice/family medicine training on Japanese junior residents:reflective writing analysis using text mining
Bibliographic record
Abstract
BACKGROUND: We previously reported the impact of general practice/family medicine training on postgraduate training in Japan using evaluation criteria standardized nationwide. However, there is a possibility that new insights may be gained by analyzing the reflective reports written by these residents. METHODS: Junior residents who participated in one-month general practice/family medicine training at one of five medical institutions with full-time family medicine specialists between 2019 and 2022 were enrolled in this study. They were assigned to submit a reflective report on their experiences and thoughts every day during the training. We analyzed these reflective writings using text mining and created a co-occurrence network map to see the relationship between the most frequently used words. RESULTS: Ninety junior residents participated in the study. The words that appeared most frequently in the sentences referring to clinical ability included "symptoms," "medical examination," "consultation," "treatment," and "examination." The words of "family" and "(patient) oneself" showed strong association in the co-occurrence network map. CONCLUSION: It was suggested that general practice/family medicine training greatly contributes to the acquisition of clinical abilities and deepens the learning of junior residents not only about patient care but also about family-oriented care.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".