Enhancement of Critical Care Response Teams Through the Use of Electronic Nursing-mediated Vital Signs Surveillance
Bibliographic record
Abstract
Failure to recognize changes in a patientâs clinical condition is a barrier to the effectiveness of CCRT outreach programs. The development of a vital signs capture and decision system could alert care providers and CCRTs when a patientâs clinical condition deteriorates. However, point-of-care vital signs capture and documentation is often problematic in clinical practice. \nEthnographic research was conducted to understand the difficulties of replacing pen and paper charts and barriers to electronic nursing documentation systems. Analysis of workflows directed the design of two solutions; 1) Apple iPhone facilitated manual vital signs entry, 2) Motorola MC55 enabled automatic data capturing from physiological monitors. \nNurses participated in high-fidelity usability testing, comparing the traditional method of paper documentation with the two electronic solutions. As a result of user-centered design process, both solutions were comparable to the efficiency of paper methods, were found acceptable to nurses, and could be successfully incorporated into current workflows.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".