Nursing Clinical Instructors’ Perceived Supports and Barriers to Reporting Medication Errors, Near Misses, and Discovered Errors
Bibliographic record
Abstract
Introduction: Medication administration errors (MAEs) are common in healthcare, and one of the leading causes of harm and death. Not only do these errors lead to a decrease in overall patient safety, but they are also a large financial burden globally. It is essential that nurses report MAEs so that healthcare systems can identify causative factors and implement preventative measures. Purpose: The purpose of this study was to explore clinical instructors’ perceptions of the supports and barriers experienced when prompting student nurses to report medication incidents during clinical rotations. Methods: This study utilized a descriptive, cross-sectional method and convenience sampling to recruit clinical instructors currently employed in a baccalaureate nursing program in Southwestern Ontario. A Qualtrics survey was emailed to all potential participants. Data was analyzed utilizing SPSS software. Results: A total of 28 surveys were completed out of the potential 96 participants, yielding a 29.1% response rate. The average years of experience was 17 years as a registered nurse and 6.5 years as a clinical instructor. A total of 86% of participants stated that they encourage their students to report all types of MAEs 76% - 100% of the time. The strongest supports identified were: “education at clinical meetings help me to understand the reporting system and importance of reporting” and “thank you for reporting email”. The largest barrier identified was “I don’t have the time to encourage reporting because I am busy with other clinical instructor responsibilities”. Conclusion: Due to the small sample size obtained and skewness of the data, further research is recommended. Clinical instructors are essential to the hands-on learning of nursing students. Decreasing the barriers and increasing the supports to reporting is a crucial strategy to decrease the number of MAEs in the future.
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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 | 0.005 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".