Bibliographic record
Abstract
BACKGROUND: Exception reporting is the main process in England to resolve issues related to junior doctor working hours. Concerns have been raised regarding variable report submission, but no significant exploration has occurred. This study assesses frequency of exception reporting and correlates it with frequency of working beyond rostered hours and overall satisfaction. METHODS: National training survey (NTS) scores for 'Overall Satisfaction' and frequency of working beyond rostered hours was obtained for 26 randomly identified trusts throughout England and correlated with exception reporting frequency from guardian of safe working (guardian) quarterly reports covering April 2018. RESULTS: Guardian reports were obtained for 24 trusts. NTS data suggest trainees worked beyond their rostered hours 12.1 times per quarter (interquartile range (IQR) 10.0-12.9) whereas guardian reports show they exception reported 0.15 times per quarter (IQR 0.084-0.25). Trainees exception report 1.2% of the time they work beyond rostered hours (IQR 0.8-2.4%).Frequency of exception reporting correlates poorly with the frequency with which trainees work beyond rostered hours (coefficient -0.22) and with a marker of overall satisfaction (coefficient -0.21). CONCLUSION: The current exception reporting process significantly under-reports trainee working hours although there is regional variation.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".