Sleep, Sleepiness, Fatigue, and Performance of 12-Hour-Shift Nurses
Bibliographic record
Abstract
Nurses working 12-h shifts complain of fatigue and insufficient/poor-quality sleep. Objectively measured sleep times have not been often reported. This study describes sleep, sleepiness, fatigue, and neurobehavioral performance over three consecutive 12-h (day and night) shifts for hospital registered nurses. Sleep (actigraphy), sleepiness (Karolinska Sleepiness Scale [KSS]), and vigilance (Performance Vigilance Task [PVT]), were measured serially in 80 registered nurses (RNs). Occupational fatigue (Occupational Fatigue Exhaustion Recovery Scale [OFER]) was assessed at baseline. Sleep was short (mean 5.5 h) between shifts, with little difference between day shift (5.7 h) and night shift (5.4 h). Sleepiness scores were low overall (3 on a 1-9 scale, with higher score indicating greater sleepiness), with 45% of nurses having high level of sleepiness (score > 7) on at least one shift. Nurses were progressively sleepier each shift, and night nurses were sleepier toward the end of the shift compared to the beginning. There was extensive caffeine use, presumably to preserve or improve alertness. Fatigue was high in one-third of nurses, with intershift fatigue (not feeling recovered from previous shift at the start of the next shift) being most prominent. There were no statistically significant differences in mean reaction time between day/night shift, consecutive work shift, and time into shift. Lapsing was traitlike, with rare (39% of sample), moderate (53%), and frequent (8%) lapsers. Nurses accrue a considerable sleep debt while working successive 12-h shifts with accompanying fatigue and sleepiness. Certain nurses appear more vulnerable to sleep loss than others, as measured by attention lapses.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".