Bibliographic record
Abstract
Background and objective: Children have a higher risk of being exposed to medication errors and are more prone to harm due to reasons such as differences in their growth and development, and their physiological and psychological characteristics which are different from those of adults. The purpose of this study is to determine pediatric nurses’ attitudes towards reporting of medication errors and causes of not reporting of medication errors and to determine their views on the incidence of medication errors.Methods: The study was conducted in a Children’s Hospital in the province of Izmir, with the participation of 179 pediatric nurses. To collect the data, two forms were used in the study, socio-demographic questionnaire and Questionnaire for Medication Errors.Results: While 34.6% (n = 62) of the nurses thought that medication errors never happened in the clinics over the past year. While 94.4% (n = 169) of the participating nurses did not report any medication errors within the past year, 5.6% reported 1-2 times. The highest proportion (75.4%) (n = 135) of the nurses, the reason for not reporting medication errors was the fear of receiving legal punishment.Conclusions: Reporting medication errors is low level. In conclusion, the main reason for not reporting medication errors was the perception of receiving punishment. Implications for nursing and/or health policy: Education to nurses should be given at regular intervals and in small groups by using case samples. If the managers are to promote reporting, they should eliminate the perception of receiving punishment, and make necessary arrangements to develop non-accusatory culture aiming to learn from the results of reported 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".