The Effectiveness of Ventilator-Associated Pneumonia Prevention Strategies Among Critical Care Nurses: A Systematic Review
Bibliographic record
Abstract
Background: Ventilator-associated pneumonia (VAP) remains a major clinical challenge in intensive care units (ICUs), contributing to increased morbidity, mortality, and healthcare costs among mechanically ventilated patients. Critical care nurses play a central role in preventing VAP through the implementation of evidence-based interventions; however, variations in knowledge, compliance, and organizational resources continue to influence practice outcomes. Understanding the effectiveness of these prevention strategies is essential for improving patient safety and strengthening nursing performance in critical care settings. Aim: This systematic review aimed to evaluate the effectiveness of VAP prevention strategies implemented by critical care nurses and to identify factors influencing compliance and patient outcomes. Method: A systematic search was conducted across PubMed, CINAHL, Scopus, ScienceDirect, and Google Scholar for studies published between 2021 and 2024. Ten primary studies met the eligibility criteria and were evaluated using the Joanna Briggs Institute (JBI) critical appraisal tools. A narrative synthesis approach was applied due to heterogeneity in study designs and outcome measures. Results: Three major themes emerged: effectiveness of VAP prevention bundles, nurse knowledge and compliance, and organizational influences on practice. Most studies demonstrated that consistent implementation of VAP bundles significantly reduced infection rates. Higher knowledge and training frequency were linked to improved adherence, while staffing shortages, heavy workloads, and limited institutional support were common barriers to implementation. Despite variations across settings, evidence consistently supported the critical role of nurse-driven interventions VAP prevention is most effective when evidence-based interventions are supported by ongoing education, adequate staffing, and strong organizational resources. Strengthening these components may enhance compliance and improve patient outcomes in ICU settings.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".