Nursing Duty Hours’ Length and the Perceived Outcomes of Care
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Working long shifts are associated with fatigue, medical errors and poor outcomes of care. However, there is a lack of guide that can provide policy-makers the optimal duty length in the Malaysian hospitals. The study aims to investigate the impact of nursing duty hours’ length on the quality and safety of care delivered in the “Medical-Surgical Wards” in Malaysia.METHOD: Cross-sectional study was carried out on 12 private hospitals. Data was collected, through questionnaires, from 652 nurses (61.8 % response rate). Stratified random sampling was used in the study. Regression analyses were conducted to explore the impact of the nursing duty hours’ length on the care quality and safety.FINDINGS: The length of nurses’ duty hours is not significantly affecting care quality (F = 1.27 and P value = 0.28) and patient safety (F = 1.81 and P value = 0.13), at p<0.05 significance level.CONCLUSION: Nurse working in hospitals with 10-hours night shift had perceived poor quality (B=-0.11, t=-1.64, p=0.10); and unsafe care (B=-0.17, t=-2.40, p=0.02). Policy makers in Malaysian hospitals can benefit from the study by restructuring duty hours’ length in their hospital.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".