Fatigue in Residency Education: Understanding the Influence of Work Hours Regulations in Europe
Bibliographic record
Abstract
PURPOSE: Although one proposed solution to the problem of fatigued medical trainees is the implementation of work hours regulations, concerns about the effectiveness of these regulations are growing. Canada remains one of the few Western jurisdictions without legislated regulation. Recent research suggests that fatigue is a complex social construct, rather than simply a lack of sleep; thus, the authors explored how regulations and fatigue are understood in countries with established work hours frameworks to better inform other jurisdictions looking to address trainee fatigue. METHOD: Using constructivist grounded theory methodology, the authors conducted individual, semistructured interviews in 2015-2016 with 13 postgraduate medical trainees from four European countries with established work hours regulations. Data collection and analysis proceeded iteratively, and the authors used a constant comparative approach to analysis. RESULTS: Trainees reported that they were commonly fatigued and that they violated the work hours restrictions for various reasons, including educational pursuits. Although they understood the regulations were legislated specifically to ensure safe patient care and optimize trainee well-being, they also described implicit meanings (e.g., monitoring for trainee efficiency) and unintended consequences (e.g., losing a sense of vocation). CONCLUSIONS: Work hours regulations carry multiple, conflicting meanings for trainees that are captured by three predominant rhetorics: the rhetoric of patient safety, of well-being, and of efficiency. Tensions within each of those rhetorics reveal that managing fatigue within clinical training environments is complex. These findings suggest that straightforward solutions are unlikely to solve the problem of fatigue, assure patient safety, and improve trainee 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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".