Use of nursing care bundles for the prevention of ventilator‐associated pneumonia in low‐middle income countries: A scoping review
Bibliographic record
Abstract
BACKGROUND: Ventilator-associated pneumonia (VAP) is a significant concern in low-middle-income countries (LMICs), where the burden of hospital-acquired infections is high, and resources are low. Evidence-based guidelines exist for preventing VAP; however, these guidelines may not be adequately utilized in intensive care units of LMICs. AIM: This scoping review examined the literature regarding the use of nursing care bundles for VAP prevention in LMICs, to understand the knowledge, practice and compliance of nurses to these guidelines, as well as the barriers preventing the implementation of these guidelines. STUDY DESIGN: The review was conducted using Arksey and O'Malley's (2005) five-stage framework and the PRISMA-ScR guidelines guided reporting. Searches were performed across six databases: CINAHL, Medline, Embase, Global Health, Scopus and Cochrane, resulting in 401 studies. RESULTS: After screening all studies against the eligibility criteria, 21 studies were included in the data extraction stage of the review. Across the studies, the knowledge and compliance of nurses regarding VAP prevention were reported as low to moderate. Several factors, ranging from insufficient knowledge to a lack of adequate guidelines for VAP management, served as contributing factors. Multiple barriers prevented nurses from adhering to VAP guidelines effectively, including a lack of audit/surveillance, absence of infection prevention and control (IPC) teams and inadequate training opportunities. CONCLUSIONS: This review highlights the need for adequate quality improvement procedures and more efforts to conduct and translate research into practice in intensive care units in LMIC. RELEVANCE TO CLINICAL PRACTICE: IPC practices are vital to protect vulnerable patients in intensive care units from developing infections and complications that worsen their prognosis. Critical care nurses should be trained and reinforced to practice effective bundle care to prevent VAP.
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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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".