Napping during breaks on night shift: critical care nurse managers' perceptions.
Bibliographic record
Abstract
BACKGROUND: Fatigue associated with shiftwork can threaten the safety and health of nurses and the patients in their care. Napping during night shift breaks has been shown to be an effective strategy to decrease fatigue and enhance performance in a variety of work environments, but appears to have mixed support within health care. PURPOSE: The purpose of this study was to explore critical care unit managers'perceptions of and experiences with their nursing staff's napping practices on night shift, including their perceptions of the benefits and barriers to napping/not napping in terms of patient safety and nurses'personal health and safety. METHODS: A survey design was used. Forty-seven Canadian critical care unit managers who were members of the Canadian Association of Critical Care Nurses responded to the web-based survey. Data analysis involved calculation of frequencies and percentages for demographic data, use of the Friedman rank test for comparison of managers' perceptions, and content analysis for responses to open-ended questions. RESULTS: The findings of this study offer valuable insights into the complexities and conflicts perceived by managers with respect to napping on night shift breaks by nursing staff Staff and patient health and safety issues, work and break expectations and experiences, and strengths and deficits related to organizational napping resources and policy are considerations that will be instrumental in the development of effective napping strategies and guidelines.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".