Intensive care unit nurses' information needs and recommendations for integrated displays to improve nurses' situation awareness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Fatal errors can occur in intensive care units (ICUs). Researchers claim that information integration at the bedside may improve nurses' situation awareness (SA) of patients and decrease errors. However, it is unclear which information should be integrated and in what form. Our research uses the theory of SA to analyze the type of tasks, and their associated information gaps. We aimed to provide recommendations for integrated, consolidated information displays to improve nurses' SA. MATERIALS AND METHODS: Systematic observations methods were used to follow 19 ICU nurses for 38 hours in 3 clinical practice settings. Storyboard methods and concept mapping helped to categorize the observed tasks, the associated information needs, and the information gaps of the most frequent tasks by SA level. Consensus and discussion of the research team was used to propose recommendations to improve information displays at the bedside based on information deficits. RESULTS: Nurses performed 46 different tasks at a rate of 23.4 tasks per hour. The information needed to perform the most common tasks was often inaccessible, difficult to see at a distance or located on multiple monitoring devices. Current devices at the ICU bedside do not adequately support a nurse's information-gathering activities. Medication management was the most frequent category of tasks. DISCUSSION: Information gaps were present at all levels of SA and across most of the tasks. Using a theoretical model to understand information gaps can aid in designing functional requirements. CONCLUSION: Integrated information that enhances nurses' Situation Awareness may decrease errors and improve patient safety in the future.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it