The Relationship between Individual Characteristics, Work Shift and Mental Workload with Work Fatigue in Nurses at Wava Husada Hospital
Bibliographic record
Abstract
Nurses are one of the professions that have a high risk of work fatigue. Almost 80% of nurses in Canada experience work fatigue. Work fatigue occurs due to an imbalance between task demands and work capacity. This study included quantitative research with an analytical observational type of research. The research design used was cross-sectional. The population of this study was all inpatient nurses of Wava Husada Hospital with a sample of 136 respondents. The result showed that 12.5% of nurses experienced low category work fatigue, 69.1% of nurses experienced moderate category work fatigue, 16.9% of nurses experienced high category work fatigue and 1.5% of nurses experienced very high category work fatigue. There was a moderate relationship between age (r=0.509) and work fatigue. There was no relationship between sex (r=-0.055) and work fatigue and no relationship between length of service (r=0.127) and work fatigue. Then there was a moderate relationship between nutritional status (r=0.402) and work fatigue, a strong relationship between work shift (r=-0.547) with work fatigue and a moderate relationship between mental workload (r=0.360) and work fatigue. Gender and length of service are weakly associated with work fatigue. While age, nutritional status and mental workload are associated with moderate work fatigue, and work shift is strongly associated with work fatigue. Hospitals should periodically measure work fatigue to nurses and provide counselling and training related to work fatigue and prevention efforts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".