Factors Influencing Novice and Beginner Nurses’ Intention to Report Medication Errors and Near Misses
Bibliographic record
Abstract
INTRODUCTION: Novice and beginner nurses make more medical errors than senior nurses. However, there is significant underreporting of medication errors and near misses among novice and beginner nurses. OBJECTIVE: To identify the factors that influence the intention of novice and beginner nurses to report medication errors and near misses METHODS: A cross-sectional exploratory study was carried out among third-year nursing students in a Quebec university (n = 143). Data was collected through a self-reported questionnaire based on the adapted Theory of Planned Behavior. Simple descriptive analyses and a series of contingency analyses were performed using Chi-2 or Fisher exact tests. Correction of multiple tests was done using Bonferroni test. RESULTS: All theoretical constructs were significantly associated with intention. Sociodemographic factors (age, sex, experience and education program) were also associated with intention. DISCUSSION AND CONCLUSION: Further studies are needed to identify the determinants of intention to report medication errors and near misses among novice and beginner nurses. More attention is required in nursing practice and education to act on these factors, thus encouraging novice and beginner nurses to report medication errors and near misses.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".