Mobility in a cardiac surgery intensive care unit: A behaviour mapping study
Bibliographic record
Abstract
• Observational studies are required to determine the current state of early mobility in the cardiac surgery intensive care unit. • Sitting in a chair was observed 10-fold more often than any other mode of mobility. • Family was observed to support mobility through active engagement and providing support more than healthcare professionals. • Research is required to support early mobility beyond sitting in a chair supported by family and the healthcare team. Mobilization within 24 h post-cardiac surgery (CS) supports improved patient health outcomes. Despite being safe and recommended, it is unknown how much mobility takes place post-CS in the intensive care unit (ICU). Behaviour mapping was used to describe patterns of patients’ mobility in one CS ICU. Behaviour mapping gathers information on behaviour regularly over a time period. Two authors observed one CS ICU over a sixteen-hour period (0630–2230 h) on four days. Observers collected data on patients’ mobility mode, location, and support at 15-minute intervals. Data aggregated into four-hour time blocks is described. A total of 1342 observations were collected over four days: 487 of mode, 485 of location, and 370 of support. Sitting in a chair was observed 430 of 487 observations, 10-fold more than any other mode of mobility. Mobility within the ICU room was observed in 448 of 485 observations. Family support for mobility was observed in 178 of 370 observations. The most common time block for mobilization was from 0630 to 1030, with 488 of 1342 observations. Research is required to support the integration of early mobility beyond sitting in a chair supported by more team members into local CS ICU clinical care. The existence of early mobility protocols does not mean that they are operational in the CS ICU. Integration of these protocols into CS ICU clinical care requires collaboration among researchers and clinicians.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".