Resident Physicians Have an Increased Risk of Adverse Driving Events Following Extended-Duration Work Shifts: A Systematic Review
Bibliographic record
Abstract
Resident physicians often work extended-duration work shifts (EDWSs) exceeding 16 hours. EDWSs are associated with fatigue, workplace errors, mental health problems, and motor vehicle incidents. A 2019 systematic review reported that resident physicians had an increased risk of motor vehicle collisions (MVCs) and of falling asleep at the wheel after EDWSs. This systematic review updates those findings with recent literature. Embase, PubMed, Cochrane Database, and Ovid Medline were searched for original research articles studying resident physician driving safety following EDWS. Two authors independently reviewed articles for inclusion. Both reviewers performed data extraction and quality appraisal for each included article. Six articles met the inclusion criteria. Three articles found associations between EDWS and increased sleepiness in resident physicians. Self-reported sleepiness was increased by 46% following an EDWS compared to a normal-length shift. Objective measures of sleepiness were also increased following an EDWS. Similarly, there was a three-fold increase in adverse driving events following an EDWS compared to pre-EDWS. One study found 3.90 higher odds of an adverse driving incident following an EDWS compared to a day shift. EDWS are associated with an increased risk of adverse driving incidents, including collisions and falling asleep while driving, in resident physicians. Possible solutions including compensation for ride-share and taxi services, scheduled breaks, education on risks of driving while fatigued, and the use of caffeine may lower the risk of adverse driving incidents post-EDWSs. Further research is needed to assess the impact of possible solutions.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".