Benefits of Reporting and Analyzing Nursing Students' Near-Miss Medication Incidents
Bibliographic record
Abstract
BACKGROUND: Developing competencies in reporting medication errors and near-miss incidents is a critical component of nursing student education. The benefits of reporting near-miss incidents by nursing students are unknown. PURPOSE: The aim was to analyze nursing students' near-miss incident reports for types of incidents and their contributing factors, assess the effectiveness of current procedures in catching these errors, and offer guidance on curricular improvements for medication administration content. METHOD: This quality improvement project analyzed 3 years of near-miss incidents (N = 236) submitted through the school's incident reporting system. RESULTS: Five incident types accounted for 81.4% of incidents. Factors contributing to most incidents were communication (47.9%), competency and education (44.1%), environmental/human limitations (35.2%), and policies/procedures (29.2%). CONCLUSION: Safety experts emphasize that near-miss reports offer free lessons to prevent future errors. Nursing students' near-miss reporting is beneficial for both students and nursing programs.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".