Fatigue as a primary and secondary factor in relation to shift-rotating and patient safety in nurses
Bibliographic record
Abstract
Objective: The role of nurses’ shift-rotations in predicting adverse patient events has received little attention. The effect of fatigue on patient safety as a primary factor and the impact of shift-working on fatigue as a secondary factor in hospital-based nurses was investigated in the present study.Methods: In this cross-sectional study set in Iraqi Kurdistan in 2018, 71 nurses (Range: 20-44 years) were recruited purposively who worked in rotating shifts, in four multi-specialty hospitals.Results: The mean age of the nurses was 30.24 years (SD: 4.81; range: 20-44 years). The majority of nurses worked in the public sector (63.4%). The nurses worked in morning shift (26.8%) and shift-rotations (39.4%) for between 7.75 and 9.13 hours. In addition, 59.4% and 18.3% of nurses reported that they injured “sometimes” and “frequently” (respectively) patients in their care either directly or indirectly. Similarly, 19.7% of them reported that these were medication errors “sometimes” and “frequently.” Patient information was recorded incompletely or incorrectly sometimes by 18.3% and frequently by 35.2%. Also, 36.6% and 31.0% of them reported that they delayed care to patients frequently and sometimes, respectively. The mean values of physical and psychological fatigue were 8.77 of 21 and 3.42 of 12, respectively. The physical and psychological fatigue were escalated in case of lower total psychological well-being (p = .009 and p = .018, respectively). The study showed that single-shift working is a predictor of delayed patients care; 95.3% vs. 60.7%; p < .001).Conclusions: Hospital administrators must be aware that nurses are not able to work effectively on short roosters or extended shifts. Protocols for better nurse health surveillance and social support in respect to 24 hours shift work must be prioritized in order to avoid mental and physical significant impairment on nurses and adverse outcomes for their clients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".