Nursing Perception of the Impact of Automated Dispensing Cabinets on Patient Safety and Ergonomics in a Teaching Health Care Center
Bibliographic record
Abstract
PURPOSE: To evaluate how nursing staff felt about the impact of automated dispensing cabinets (ADCs) on the safe delivery of health care and workplace ergonomics. To identify the main issues involved in the use of this technology and to describe the corrective measures implemented. METHODS: Cross-sectional descriptive study with quantitative and qualitative components. A questionnaire that consisted of 33 statements about ADC was distributed from May 24 to June 3, 2011. RESULTS: A total of 172 (46%) of 375 nurses completed the questionnaire. Nursing staff considered the introduction of ADC made their work easier (level of agreement of 90%), helped to safely provide patients with care (91%), and helped to reduce medication incidents/accidents (81%). Nursing staff was particularly satisfied by the narcotic drugs management with the ADCs. Nursing staff were not satisfied with the additional delays in the preparation and administration of a medication dose and the inability to prevent a medication from being administered when stopped on the medication administration record (48%). CONCLUSION: The nursing staff members were satisfied with the use of ADC and believed it made their work easier, promoted safe patient care, and were perceived to reduce medication incidents/accidents.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".