Prevalence and Types of Medication Errors in Intensive Care Unit Nurses in Iran: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background & Aims: Medication errors pose a significant threat to patient safety in healthcare settings.This study aimed to determine the prevalence and types of medication errors among intensive care unit (ICU) nurses in Iran through a systematic review and meta-analysis.Materials & Methods: Electronic databases, including PubMed, Scopus, Web of Science, Science Direct, SID, and Magiran, were searched for studies on medication errors among ICU nurses up to February 2025.A manual search of references from primary and review studies was also conducted.Studies were limited to Persian and English languages.Bias assessment was performed using the Newcastle-Ottawa Scale.Data analysis was conducted using Comprehensive Meta-Analysis software (version 3).Results: A total of 19 observational studies were selected and included in the study.The results of the meta-analysis showed that the prevalence of medication errors among intensive care unit nurses was 64.8% (95% confidence interval [CI]: 0.54-0.74,P<0.05).The prevalence of incorrect dosage, incorrect injection rate, incorrect time of administration, and incorrect medication were 15.6% (95% CI: 0.08-0.27,P<0.05), 18.5% (95% CI: 0.08-0.37,P<0.05), 11.8% (95% CI: 0.08-0.15,P<0.05), and 9.5% (95% CI: 0.05-0.16,P<0.05), respectively.Publication bias was not observed in the study (P=0.49).Conclusion: The high prevalence of medication errors among ICU nurses in Iran highlights an urgent need for intervention.Proper nurse training, adoption of advanced technologies, improved working conditions, and enhanced communication can significantly reduce medication errors in hospitals.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.010 | 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) | 0.002 | 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".