Physicians' attentional performance following a 24-hour observation period: do we need to regulate sleep prior to work?
Bibliographic record
Abstract
BACKGROUND: The tradition of physicians working while sleep deprived is increasingly criticised. Medical regulatory bodies have restricted resident physician duty-hours, not addressing the greater population of physicians. We aimed to assess factors such as sleep duration prior to a 24-hour observation period on physicians' attention. METHODS: We studied 70 physicians (mean age 38 years old (SD 10.8 years)): 36 residents and 34 faculty from call rosters at the University of Alberta. Among 70 physicians, 52 (74%) performed overnight call; 18 did not perform overnight call and were recruited to control for the learning effect of repetitive neuropsychological testing. Attentional Network Test (ANT) measured physicians' attention at the beginning and end of the 24-hour observation period. Participants self-reported ideal sleep needs, sleep duration in the 24 hours prior to (ie, baseline) and during the 24-hour observation period (ie, follow-up). Median regression models examined effects on ANT parameters. RESULTS: Sleep deprivation at follow-up was associated with reduced attentional accuracy following the 24-hour observation period, but only for physicians more sleep deprived at baseline. Other components of attention were not associated with sleep deprivation after adjusting for repetitive testing. Age, years since medical school and caffeine use did not impact changes in ANT parameters. CONCLUSIONS: Our study suggests that baseline sleep before 24 hours of observation impacts the accuracy of physicians' attentional testing at 24 hours. Further study is required to determine if optimising physician sleep prior to overnight call shifts is a sustainable strategy to mitigate the effects of sleep deprivation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".