ii Understanding and Mitigating the Interruptions Experienced by Intensive Care Unit Nurses
Bibliographic record
Abstract
Intensive Care Unit (ICU) nurses get interrupted frequently. Although interruptions take cognitive resources from a primary task and may hinder performance, they may also convey critical information. Effective management of interruptions in ICUs requires the understanding of interruption characteristics, the context in which interruption happens, and interruption content (3 Cs of interruption). Using this proposed framework, two observational studies were conducted in a cardiovascular ICU (CVICU) at a Canadian teaching hospital. The focus of the first study was to understand the anatomy of interruptions, whereas the second one evaluated an awareness-display designed to help minimize interruptions that occur at inopportune times. Finally, a laboratory study was conducted to study a phenomenon that was observed during the observational studies, namely, nested interruptions. The first observational study revealed that the rate of interruptions with personal content observed during low-severity tasks (outcome if an error occurs) was significantly higher compared to medium- and high-severity tasks. This finding suggested that other personnel may tend to regulate their interruptions based on nurses ’ tasks. However, given that nurses ’ tasks are
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".