Nurses’ Sleep Characteristics by Shift Type in a Tertiary Hospital With Flexible Working Arrangements
Bibliographic record
Abstract
Objectives: To improve the adaptation of shift work, various flexible shift patterns for nurses have introduced in addition to the existing eight-hour-three-shift system. This study aimed to compare the differences in rotating nurses’ sleep characteristics according to shift patterns. Methods: A total of 62 rotating nurses (all females, mean age 29.79±4.30 years) participated in sleep monitoring for consecutive two weeks. Objective sleep was obtained using Readiband (Fatigue Science, Inc., Vancouver, BC, Canada), a wrist-worn device that uses an accelerometer to distinguish sleep and wakefulness. Subjective sleep characteristics were measured using the Morningness-Eveningness questionnaire (MEQ), Bedtime Procrastination Scale (BPS), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI). Results: Mean BPS was 30.42±5.10, ESS was 10.42±4.27, and ISI was 11.58±4.16, and there were no statistical differences in sleep parameters except for total sleep time (TST). Although significant differences were not found in sleep parameters according to shift patterns, 27.4% of rotating nurses report subjective sleep problems. More than half (59.7%) suffer from excessive daytime sleepiness. ISI was negatively correlated with age (rho=-0.275, <i>p</i>=0.031) and shift work period (rho=-0.278, <i>p</i>=0.028), but it was positively correlated with ESS (rho=0.306, <i>p</i>=0.015). Furthermore, BPS was negatively correlated with MEQ (rho=-0.351, <i>p</i>=0.005) and TST (rho=-0.307, <i>p</i>=0.016). Conclusions: To improve the sleep of rotating nurses, more active interventions, such as sleep education to reduce bedtime procrastination and short naps during night shifts, are needed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".