Hand hygiene adherence among Iranian nurses: A systematic review and meta-analysis
Bibliographic record
Abstract
Background & Aim: Adherence of nurses to hand hygiene is important for the prevention of healthcare-associated infections. This study aims to systematically review the existing studies that assess Iranian nurses' adherence to hand hygiene and estimate their amount with meta-analysis. Methods & Materials: We performed a systematic search for peer-reviewed journals published from 2005 to 2018. The systematic search was conducted using both international (Google Scholar, PubMed, SCOPUS, and Web of Sciences) and Iranian databases (Scientific Information Database, IranMedex, Magiran, and MedLib). The search was carried out using a combination of the following terms: “adherence”, “compliance”, “hand hygiene”, “nurse”, “Iran”, “nursing practice”, “nursing”, and “guidelines”. The combinations of these words with Boolean operators like ‘AND’, ‘OR’ and ‘NOT’ were used. Results: A total of 22 articles were used in the final analyses. The pooled proportion of hand hygiene adherence was 40.5 percent [95% confidence interval [CI]: 31.1–49.8]. Sensitivity analysis confirmed that the overall estimated pooled proportion of hand hygiene adherence did not vary significantly with the elimination of any of the 22 studies, observer or self-reported hand hygiene, instrument of measurement, unit of measurements, sample size, and time. Conclusion: The level of adherence of Iranian nurses to hand hygiene can potentially increase the chance of healthcare-associated infections and put patients and nurses at risk. Hospital and nursing managers should take practical steps to investigate factors contributing to the failure of hand hygiene adherence among nursing staff. They should ensure the continuous implementation of hand hygiene improvement strategy.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".