Motor vehicle incidents in postgraduate trainees in British Columbia
Bibliographic record
Abstract
INTRODUCTION: Postgraduate medical trainees frequently work ≥ 24- hour shifts causing fatigue and adverse consequences such as motor vehicle incidents (MVIs). We aim to determine the incidence of MVIs during the commutes of trainees in British Columbia (BC) in the preceding year. METHODS: We completed a retrospective, cross-sectional survey of trainees regarding work hours, shifts, and MVIs in the previous year. MVIs included falling asleep while driving, sudden braking or swerving to avoid a collision, unintentionally running a red light or stop sign, or collisions. RESULTS: Of 273 respondents, over half (54.6%) reported ≥1 MVI, one in 14 were in a collision (7.0%), and two thirds (66.3%) reported that the safety of their commute had been impacted by fatigue in the past year. After adjustment for road exposure and shift-related factors, every ten km increase in commute length was associated with an increased risk of MVI (aOR=1.54;95%CI:1.15-2.12). Reported attentional failures, such as unintentionally running a red light and/or stop sign, increased for every ten hours on-call (aOR=1.44;95%CI:1.03-2.04) and for every additional past-midnight shift worked (aOR=1.13;95%CI:1.01-1.26). DISCUSSION: Trainees with longer and more frequent commutes had an increased risk of MVIs. Trainees who worked more hours on-call and more past-midnight shifts reported significantly more attentional failures while commuting. This study helps us understand factors affecting trainee commuter safety and supports calls for the provision of safe alternatives to commuting for postgraduate trainees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 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 teacher head, 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".