An Evidence-based Framework for Reporting Student Nurse Medication Incidents: Errors, Near Misses and Discovered Errors
Bibliographic record
Abstract
Purpose: To share an evidence-based framework for reporting and analysing three types of medication incidents in an undergraduate nursing program. Incident types include errors, near misses and discovered errors. Background: Medication errors are underreported. Published studies on errors by nursing students indicate that although errors occur during clinical placements, there is a lack of consensus on how the factors that contributed to the errors are reported and analyzed. This limits our understanding of the factors that impact safe medication administration and reduces our ability to apply this knowledge to education and practice. Method: Quality improvement project. Results: Our reporting framework quantifies system factors that are supported by the literature as contributing to errors but not usually captured in incident reporting. Contributing factors for errors and near misses varied. This finding has not been documented in the literature. Conclusion: Nursing schools should prepare nursing students with a strong commitment to report all incidents and provide them with the competencies and a reporting system that allows them to report efficiently and effectively. As these graduates enter the workforce, they can influence the reporting practices of seasoned nurses. The ten factor framework provides nursing schools with the ability to quantify the individual and system factors that influence the safety of the student nurse medication administration process and the opportunity to implement strategies to reduce and/or prevent these incidents from occurring.
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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 | 0.285 | 0.349 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.073 | 0.019 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.015 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".