Nursing Perception of the Impact of Medication Carts on Patient Safety and Ergonomics in a Teaching Health Care Center
Bibliographic record
Abstract
OBJECTIVES: In our Quebec (Canada) University Hospital Center, 68 medication carts have been implemented as part of a nationally funded project on drug distribution technologies. There are limited data published about the impact of medication carts in point-of-care units. Our main objective was to assess nursing staff's perception and satisfaction of medication carts on patient safety and ergonomics. METHOD: Quantitative and qualitative cross-sectional study. Data were gathered from a printed questionnaire administered to nurses and an organized focus group composed of nurses and pharmacists. RESULTS: A total of 195 nurses completed the questionnaire. Eighty percent of the nurses agreed that medication carts made health care staff's work easier and 64% agreed that it helped to reduce medication incidents/accidents. Only 27% and 43% agreed that carts' location reduces the risk of patients' interruptions and colleagues' interruptions, respectively. A total of 17 suggestions were extracted from the focus group (n = 7 nurses; n = 3 pharmacist) and will be implemented in the next year. CONCLUSIONS: This descriptive study confirms the positive perception and satisfaction of nurses exposed to medication carts. However, interruptions are a major concern and source of dissatisfaction. The focus group has revealed many issues which will be improved.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".