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, p=0.031) and shift work period (rho=-0.278, p=0.028), but it was positively correlated with ESS (rho=0.306, p=0.015). Furthermore, BPS was negatively correlated with MEQ (rho=-0.351, p=0.005) and TST (rho=-0.307, p=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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".