Factors influencing the reporting of medication errors and near misses among nurses: A systematic mixed methods review
Bibliographic record
Abstract
AIM: This study aimed to systematically review empirical evidence on factors influencing nurses to report medication errors and near misses. BACKGROUND: There is underreporting of medication errors among nurses, in particular among novice and beginner nurses. To improve quality of care, factors influencing the reporting of medication errors and near misses should be documented. METHOD: A systematic mixed methods review was conducted. CINAHL, Cochrane Collaboration, Embase, Medline, PsycINFO and Web of Science databases were explored and analysed from December 1990 to December 2023. Two reviewers independently selected and extracted data using a standardized data extraction grid. Data were analysed using thematic analysis based on the adapted theory of planned behaviour. RESULTS: Forty-two studies met the eligibility criteria. Principal factors influencing the reporting of medication errors and near misses among nurses were associated with perceived behavioural control, subjective norm and attitude. Few studies examined factors influencing reporting medication errors and near misses among novice and beginner nurses, and sociodemographic and professional factors. CONCLUSION: To understand factors influencing reporting of medication errors and near misses, further studies should be conducted to investigate sociodemographic and professional factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".