Evaluation of evidence-based practice of catheter associated urinary tract infections prevention in a critical care setting: An integrative review
Bibliographic record
Abstract
Background and objective: An estimated 449,300 catheter-associated tract urinary infection (CAUTI) incidents affecting Americans and 13,000 CAUTI-related deaths in the United States every year. The purpose of the review was the appraisal and integration of the best evidence practice for preventing CAUTI interventions and strategies to guide safety and quality initiatives in order to improve patient care.Methods: A total of 20 articles complied with the exclusion and inclusion criteria. The articles were studied, and the chosen articles were categorized in two areas of study: CAUTI prevention, and nurse education and knowledge improvement.Results: The articles selected were reviewed to encompass a review on the articles offering the most applicable corresponding information involving catheter-associated urinary tract infections and competency-based education.Conclusions: Analysis of the data from the literature search indicates the potential lack of compliance of CAUTI infection control practices is an issue for CAUTI problem. So implementing the best evidence to enforce CAUTI bundles compliance for CAUTI prevention is a key to reduce CAUTI rates.
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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".