Bibliographic record
Abstract
Objective To investigate nursing workload and related factors of Coronary Care Unit (CCU),and to provide evidence for rationally arranging nursing human resources and improving nursing efficiency.Methods 77 inpatients of a third-grade class-A hospital were surveyed by means of Intensive Care Nursing Scoring System (ICNSS) to investigate workload of CCU nurses.Results Total score of CCU nursing workload was (25.5714 ± 6.48) points.ICNSS scores of the four seasons were (27.24 ± 4.438 ),( 25.62 ±4.535),(22.33 ±9.403),(27.24 ±6.953 ),respectively,the difference was statistically significant (F =11.867,P ＜0.01 ).The LSD multiple comparison showed that there were significant differences of ICNSS scores between the first quarter and the second quarter,the first quarter and the third quarter,the second quarter and the third quarter,the third quarter and the fourth quarter,the difference was statistically significant (P ＜0.05 ).The workload of the nurses who took care of the patients admitted by means of wheel chair and stretcher was (20.74 ± 4.663 ) and (25.89 ± 6.508 ) points,and the difference was statistically significant ( t =0.319,P ＜0.05 ).There was significant difference of nursing workload regarding length of stay and percutaneous coronary intervention (P ＜ 0.05 ).Among same type of disease,workload of CCU nurses was the highest on admission and on the day of percutaneous coronary intervention.Nursing workload was found negatively correlated with length of stay of the patients ( P ＜ 0.05 ).Of the 36 patients undergone percutaneous coronary intervention,the workload of CCU nurses was (26.35 ± 4.057 ) scores on the day of intervention,(23.58 ±3.525) scores on day 1,( 22.16 ± 4.537 ) scores on day 2 and ( 19.17 ± 3.637 ) points after day3,the difference was statistically significant ( F =21.024,P ＜ 0.05 ).Conclusions A flexible staff arrangement should be adopted according to the characters of the disease,patients' conditions and the nursing workload. Key words: Coronary care unit; Nursing workload; Related factors; Human resource management
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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".