Association of Virtual Nurses’ Workflow and Cognitive Fatigue During Inpatient Encounters: Cross-Sectional Study
Bibliographic record
Abstract
Background: The virtual nursing delivery model enables the provision of expert nursing care from a remote location, using technology such as audio and video communication, remote monitoring devices, and access to electronic health records. Virtual nurses spend an extensive amount of time on computers to provide care, and little is known about how this workflow may affect and contribute to cognitive fatigue. Objective: This study aimed to use eye tracking technology and pupil size variation to determine instances of virtual nurse cognitive fatigue during their typical workflow. Methods: This study examined the virtual nursing workflow by recording and analyzing virtual nurse encounters using eye tracking. This cross-sectional study was conducted during regular 12-hour shifts at a major Southeastern health center in the United States. Results: The study found that 75% (22/29) of virtual nursing encounters demonstrated a first fatigue instance at 9.8 minutes during patient discharges and at 11.9 minutes during patient admissions. Conclusions: This study provides valuable insights into virtual nursing workflow design and how it may impact the cognitive fatigue levels of nurses providing inpatient virtual care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".