ICU nurses’ knowledge, attitude and behavior about ICU acquired weakness
Bibliographic record
Abstract
Background and objective: Intensive care unit (ICU) acquired weakness (ICU-AW) is muscle weakness caused by neuromuscular dysfunction, which can cause muscle mass and muscle strength decline, and is one of the common complications of ICU. ICU-AW can affect patients' daily activities and long-term quality of life. We sought to understand the current knowledge, attitude and behavior of ICU nurses relative to ICU-AW, and analyze the factors influencing the knowledge, attitude and behavior.Methods: Design: A cross-sectional survey. A survey was conducted of 236 ICU nurses using a self-designed questionnaire.Results: The knowledge, attitude and behavior scores of ICU nurses were 17.05 ± 6.003, 17.47 ± 5.538 and 19.84 ± 5.100, respectively. Multivariate analysis showed that education level, previous exposure to ICU-AW and ICU-AW-related knowledge were the three factors affecting ICU nurses' knowledge of ICU-AW. Education level and previous exposure to ICU-AW were the two factors affecting attitude. Education level and ICU-AW-related knowledge were the factors influencing nurses’ behavior (p < .05).Conclusions: ICU nurses have a lower knowledge and attitude score on ICU-AW and a higher score on ICU-AW intervention practice. Relevance to clinical practice: ICU nurses need to acquire more theoretical knowledge and deepen their understanding of ICU-AW, so as to prevent the incidence of ICU-AW.
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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 | 0.006 |
| 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) | 0.002 | 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".