Intensive Care Nurses’ Knowledge and Practice of Evidence-Based Recommendations for Endotracheal Suctioning: A Multisite Cross-Sectional Study in Changsha China
Bibliographic record
Abstract
Abstract Background: Endotracheal suctioning is one of the most frequently operated invasive procedures by intensive care nurses. Nurses should have adequate knowledge and skills to perform endotracheal suctioning based on the best evidence. Litter is known about intensive care nurses' knowledge and practice of evidence-based endotracheal suctioning in Chinese hospitals. The purpose of this study is to investigate intensive care nurses' knowledge and practice of the evidence-based recommendations regarding endotracheal suctioning. Specifically, the study aims to examine 1) intensive care nurses' awareness of and adherence to the endotracheal suctioning guideline; and 2) their influencing factors.Methods: The cross-sectional online questionnaire survey was distributed to 310 intensive care nurses working in intensive care units of five tertiary hospitals in Changsha, China. Results: 281 nurses completed and returned the survey (response rate= 90.6%). Participants' awareness of and adherence to the evidence-based guideline was at a poor to moderate level. There was a significant difference regarding the awareness of the guideline between experienced and inexperienced nurses. Nurses who worked 6-15 years in intensive care units had a higher awareness of evidence-based endotracheal suctioning practices than nurses who worked within five years and over 16 years. Nurses with endotracheal suctioning training demonstrated significantly higher awareness of endotracheal suctioning recommendations and higher adherence levels than those untrained nurses.Conclusion: There are considerable evidence-practice gaps in ETS among Chinese intensive care practice. further research should emphasis on revealing barriers and facilitators of implementing the evidence-based endotracheal suctioning practices, developing context-suitable interventions for the guideline implementation. We suggest a systematic training of the ETS guidelines along with innovative implementation strategies from implementation science to promote the ETS practice changes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".