The Use of Multiple Methods to Explore the Impact of Interruptions on Intravenous (IV) Push Delivery
Bibliographic record
Abstract
Despite the safety-critical nature of healthcare, it is an interrupt-laden domain. Patient safety organizations have long called for a reduction in interruptions to healthcare workers, in an effort to reduce the likelihood of preventable medical error occurring during patient care. The goal of this research was to examine the impact that interruptions have on nurses’ task performance during medication administration, specifically intravenous (IV) push delivery, and the development of appropriately designed interventions to mitigate the potential harmful effects of interruptions on patient safety. IV push administration errors have been found to be common, and occur when doses are administered faster or slower than recommended. Direct observations found that nurses were interrupted every time they administered an IV push, sometimes more than once. Furthermore, the percentage of nurses who made errors when performing IV pushes during simulated scenarios was significantly higher when interrupted than in the uninterrupted condition. Data collected through focus groups qualitatively described perceptions and preferences that led to the design of a successful and appropriate intervention to solve this important patient safety problem.
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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 | 0.030 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".