Assessing Global Nursing Interventions in Reducing Hospital-Acquired Infections: A Meta-Analysis
Bibliographic record
Abstract
Background: Hospital-acquired infections (HAIs) raise worldwide morbidity, death, and healthcare expenditures. Preventing and managing HAIs requires nursing interventions such hand hygiene, personal protective equipment (PPE) usage, environmental cleaning, and antimicrobial stewardship. This meta-analysis examined how nursing interventions reduced HAIs in different hospital settings. Methods: A complete PubMed, Scopus, and Web of Science search was undertaken for January 2000–December 2023 research. Studies on HAI-reducing nursing interventions were included. Study quality was evaluated using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. The random-effects model was used to construct pooled risk ratios (RRs) with 95% CIs in meta-analysis. We also performed subgroup, sensitivity, and publication bias analyses. Results: Fourteen trials with 2540 individuals were included. In the pooled study, nursing interventions significantly reduced HAI incidence (RR = 0.42, 95% CI: 0.35-0.50, P < 0.001). Subgroup analysis indicated that hand hygiene, PPE usage, environmental cleaning, and antimicrobial stewardship reduced HAIs. Sensitivity analysis verified these results' reliability. Egger's test showed no publication bias (P = 0.78). Over time, cumulative meta-analysis showed constant effect sizes. Conclusion: Nursing interventions significantly reduce HAIs. Hand hygiene, PPE, environmental cleaning, and antimicrobial stewardship are essential to infection control. Healthcare institutions should prioritise these actions and resolve compliance hurdles to enhance patient outcomes and minimise HAIs. Research is needed to explore innovative approaches and identify factors influencing compliance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".