Impact of the resident duty hours on in-training examination score: A nationwide study in Japan
Bibliographic record
Abstract
The relationship between duty hours (DH) and the performance of postgraduate residents is needed to establish appropriate DH limits. This study explores their relationship using the General Medicine In-training Examination (GM-ITE). In this cross-sectional study, GM-ITE examinees of 2019 had participated. We analyzed data from the examination and questionnaire, including DH per week (eight categories). We examined the association between DH and GM-ITE score, using random-intercept linear models with and without adjustments. Five thousand five hundred and ninety-three participants (50.7% PGY-1, 31.6% female, 10.0% university hospitals) were included. Mean GM-ITE scores were lower among residents in Category 2 (45–50 h; mean score difference, −1.05; p p = 0.008) compared with residents in Category 5 (60–65 h; Reference). PGY-2 residents in Categories 2–4 had lower GM-ITE scores compared to those in Category 5. University residents in Category 1 and Category 5 showed a large mean difference (−3.43; p = 0.01). DH <60–65 h per week was independently associated with lower resident performance, but more DH did not improve performance. DH of 60–65 h per week may be the optimal balance for a resident's education and well-being.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".