Factors influencing fatigue in Chinese nurses
Bibliographic record
Abstract
Factors predicting fatigue in Chinese nurses were examined in a descriptive, correlational study. The participants were 581 nurses working in general hospitals in Chengdu City, China. The study instruments included the Occupational Fatigue Exhaustion Recovery Scale, the Job Content Questionnaire, the Exposure to Hazards in Hospital Work Environments Scale, the Pittsburgh Sleep Quality Index, the Job Dissatisfaction Scale, the Beck Anxiety Inventory, and the Beck Depression Inventory. The data were analyzed by using descriptive statistics, Pearson's correlation, F statistics, and multiple regression. The findings revealed that 61.7% of the variance in chronic fatigue and 54.9% of the variance in acute fatigue were explained by the independent variables. Intershift recovery was the most important variable in the explanation of acute fatigue, while acute fatigue was the most important variable in the explanation of chronic fatigue. Different intervention strategies should be implemented regarding the different influencing factors of acute and chronic fatigue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".