Perceptions of Nurses about Medication Errors: A Cross-Sectional Study
Bibliographic record
Abstract
Purpose: The aim of this study is to assess nurses’ perception of medication errors nurses in Maternity and Child Hospital in Najran city, Saudi Arabia. Study Design: A cross-sectional study. Subjects and Methods: This descriptive study was carried out among 72 nurses in Maternity and child Hospital in Najran city, Saudi Arabia. Data were collected through a questionnaire, consisting of two parts: Part 1 covers demographical data, which includes age, gender, educational level, and years of experience and place of work in the hospital. Part 2 of the questionnaire consists of (23) questions about the nurses' perception of the causes, reporting medication error, and perceptions of barriers to reporting medication errors. Data were analyzed by using a statistical package for social science (SPSS) version 20. Results: The results of the study indicate that most of the participants had a good perception of the causes of medication errors. Nevertheless, the data analysis showed that many of the participants had reporting medication errors. More importantly, the participants indicated that there exist multiple barriers to reporting medication errors. Two-thirds of them had moderate barriers to concerns over the consequences of reporting. More than half of them had minor barriers to blaming nurses if patients are harmed, while, about one-quarter of them had major barriers to fear of punishment. There was no statistically significant relationship between the studied nurses’ perception of the causes of medication errors and their characteristics (P value > 0.05). Conclusions: It is concluded that nurses at Maternity and Child Hospital in Najran city, Saudi Arabia, Had a good perception of the causes of medication errors. In addition, there was no statistically significant relationship between the participants’ reporting medication errors and their characteristics except age and years of experience.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".