Impact of general practice / family medicine training on Japanese junior residents:a descriptive study
Bibliographic record
Abstract
BACKGROUND: Despite international recognition of the impact of general practice / family medicine training on postgraduate training outcomes, there have been few reports from Japan. METHODS: Junior residents who participated in community medicine training for one month between 2019 and 2022 were enrolled in the study. The settings were five medical institutions (one hospital and four clinics) that had full-time family doctors. The junior residents were assigned to one of these institutions. The training content mainly consisted of general ambulatory care, home medical care, community-based care, and reflection. The junior residents evaluated themselves at the beginning and end of their training, and the family doctors evaluated the junior residents at the end. The evaluation items were 36 items in 10 areas, based on the objectives outlined in the Guidelines for Residency Training - 2020 Edition, and were rated on a 10-point Likert scale. In the statistical analysis, Wilcoxon signed rank test of two related groups was performed to analyze changes between pre and post self-evaluation, and the effect size r was calculated. RESULTS: Ninety-one junior residents completed the study. Their self-evaluations showed statistically significant increases in all 36 items. The effect size was large in 33 items. The family doctors' evaluation was 8-9 points for all 36 items. CONCLUSION: General practice / family medicine training may greatly contribute to the acquisition of various required clinical abilities in postgraduate training even in Japan.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".