Investigating the Causes of Medication Errors and Strategies to Prevention of Them from Nurses and Nursing Student Viewpoint
Bibliographic record
Abstract
INTRODUCTION & AIM: Medication errors as a serious problem in world and one of the most common medical errors that threaten patient safety and may lead to even death of them. The purpose of this study was to investigate the causes of medication errors and strategies to prevention of them from nurses and nursing student viewpoint. MATERIALS & METHODS: This cross-sectional descriptive study was conducted on 327 nursing staff of khatam-al-anbia hospital and 62 intern nursing students in nursing and midwifery school of Zahedan, Iran, enrolled through the availability sampling in 2015. The data were collected by the valid and reliable questionnaire. To analyze the data, descriptive statistics, T-test and ANOVA were applied by use of SPSS16 software. FINDINGS: The results showed that the most common causes of medications errors in nursing were tiredness due increased workload (97.8%), and in nursing students were drug calculation, (77.4%). The most important way for prevention in nurses and nursing student opinion, was reducing the work pressure by increasing the personnel, proportional to the number and condition of patients and also creating a unit as medication calculation. Also there was a significant relationship between the type of ward and the mean of medication errors in two groups. CONCLUSION: Based on the results it is recommended that nurse-managers resolve the human resources problem, provide workshops and in-service education about preparing medications, side-effects of drugs and pharmacological knowledge. Using electronic medications cards is a measure which reduces medications errors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".