Avoiding Medication Errors Caused by Nurses in the Emergency Department in Saudi Arabia
Bibliographic record
Abstract
Background: Medication errors are pervasive in healthcare, especially in emergency rooms, with diverse causes that warrant critical investigation due to the potential repercussions for both patients and healthcare providers. Aim: This research explores nurses' perspectives on medication errors in the emergency department. Methods: A descriptive cross-sectional design involved 96 nurses, using a questionnaire that covered demographic data and nurses' perceptions of error causes, reporting practices, and barriers. Results: The average age of the nurses who participated in this study was 27.7 ± 3.4 years, with 7.3 ± 1.9 years of experience. Most nurses (87.2%) were women. The majority held bachelor's degrees (88.3%) and worked fixed shifts (54.2%), and 46.8% reported medication errors in the past year, primarily occurring once (69.04%). They reported no complications in 97.5% of errors. Conclusion: Common error types included infusion rate errors, double dosing, and medication omission. Although errors are widespread, adverse consequences are infrequent, mainly occurring during prescribing or administration stages. Encouraging disclosure by nurses and fostering positive responses from hospital management are crucial for enhancing patient safety. Awareness of recovery mechanisms informs potential interventions to minimise overall safety.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".