Napping During Night Shift: Practices, Preferences, and Perceptions of Critical Care and Emergency Department Nurses
Bibliographic record
Abstract
BACKGROUND: Nurses working night shifts are at risk for sleep deprivation, which threatens patient and nurse safety. Little nursing research has addressed napping, an effective strategy to improve performance, reduce fatigue, and increase vigilance. OBJECTIVE: To explore nurses' perceptions, experiences, barriers, and safety issues related to napping/not napping during night shift. METHODS: A convenience sample of critical care nurses working night shift were interviewed to explore demographics, work schedule and environment, and napping/ not napping experiences, perceptions, and barriers. Transcripts were constantly compared, and categories and themes were identified. RESULTS: Participants were 13 critical care nurses with an average of 17 years' experience. Ten nurses napped regularly; 2 avoided napping because of sleep inertia. The need for and benefits of napping or not during night shift break were linked to patient and nurse safety. Ability to nap was affected by the demands of patient care and safety, staffing needs, and organizational and environmental factors. CONCLUSIONS: Nurses identified personal health, safety, and patient care issues supporting the need for a restorative nap during night shift. Barriers to napping exist within the organization/work environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".