Medication errors among nurses in teaching hospitals in the west of Iran: what we need to know about prevalence, types, and barriers to reporting
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the prevalence and types of medication errors (MEs), as well as barriers to reporting MEs, among nurses working in 7 teaching hospitals affiliated with Kermanshah University of Medical Sciences in 2016. METHODS: A convenience sampling method was used to select the study participants (n=500 nurses). A self-constructed questionnaire was employed to collect information on participants' socio-demographic characteristics (10 items), their perceptions about the main causes of MEs (31 items), and barriers to reporting MEs to nurse managers (11 items). Data were collected from September 1 to November 30, 2016. Negative binomial regression was used to identify the main predictors of the frequency of MEs among nurses. RESULTS: =0.001). CONCLUSIONS: Our study documented a high prevalence of MEs among nurses in the west of Iran. A heavy workload was considered to be the most important barrier to reporting MEs among nurses. Thus, appropriate strategies (e.g., reducing the nursing staff workload) should be developed to address MEs and improve patient safety in hospital settings in Iran.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".