Prevention of central line‐associated bloodstream infections: ICU nurses' knowledge and barriers
Bibliographic record
Abstract
BACKGROUND: Central line-associated bloodstream infections (CLABSI) have been a significant challenge in care, increasing healthcare costs and leading to adverse outcomes, including mortality. AIM: The present study aimed to assess the knowledge of intensive care unit (ICU) nurses on the prevention of CLABSI and the implementation barriers of evidence-based guidelines in practice. DESIGN: A cross-sectional study. METHODS: Data were collected from adult, paediatric, and neonatal ICU nurses working in seven hospitals in Iran, using census sampling from April to July 2020. RESULTS: A number 209 out of 220 ICU nurses participated in the present study (response rate of 95%). The median score of knowledge of ICU nurses towards the prevention of CLABSI was 3.00 out of 11. 50.72% of ICU nurses had insufficient knowledge. The most critical implementation barriers of evidence-based guidelines were high workload, shortage of necessary equipment, and lack of CLABSI prevention workshops. CONCLUSIONS: Overall, the knowledge of ICU nurses towards the prevention of CLABSI was insufficient. Study findings suggest that the knowledge of ICU nurses may be improved by reducing the workload, increasing the number of nursing staff in the ICU, having an adequate supply of equipment needed to ensure safe practice in the ICU, and providing regular related educational workshops for nurses working in the ICU. RELEVANCE TO CLINICAL PRACTICE: The present study's findings suggest that regular training programs should be developed to improve the knowledge of ICU nurses in the care and prevention of CLABSI. Nursing policymakers and managers need to identify and address implementation barriers of evidence-based guidelines to improve nursing, such as high workload, shortage of necessary equipment, and lack of CLABSI prevention workshops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".