Level of function mobility scale for nurse‐driven early mobilisation in people with acute cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: There are currently no validated tools that are reliable and easy to use for nurses to assess mobility in people with acute cardiovascular disease in the Cardiovascular Intensive Care Unit (CICU). METHODS: A multidisciplinary team at an academic tertiary care centre developed the Level of Function (LOF) Mobility Scale for use in a nurse-driven early progressive mobilisation in the CICU. To determine inter-rater reliability, the prehospital and admission LOF were assessed independently by two CICU nurses. Pairwise comparisons between raters were evaluated using Cohen's kappa statistic. To determine convergence validity, the LOF and Activity Measure for Post-Acute Care 6-Clicks score upon admission were compared with Spearman's correlation. To determine feasibility, a 9-item mobility scale questionnaire was distributed to CICU nurses with and without experience using the LOF Mobility Scale. The STROBE reporting guidelines were used. RESULTS: The LOF Mobility Scale had good inter-rater reliability for assessment of LOF prior to hospitalisation (N = 131, kappa = 0.66, p < .001) and at the time of CICU admission (N = 131, kappa = 0.71, p < .001). There was a moderate correlation (N = 79 observations; correlation coefficient = 0.525; p < .01) between the bedside nurses LOF and the 6-Clicks score. All nurses surveyed (N = 54; 100%) thought that the LOF Mobility Scale was clear and unambiguous, the LOFs were well-defined and the scale was an appropriate length. Nearly all of the nurses with experience using the scale (N = 22/24; 92%) felt that the scale took less than one minute to complete, compared with about half (N = 14/30; 47%) in the group of nurses without experience using the scale. CONCLUSION: The LOF Mobility Scale is reliable and feasible for mobility assessment in a nurse-driven early progressive mobilisation programme in patients with acute cardiovascular disease in the CICU. RELEVANCE TO CLINICAL PRACTICE: A nurse-driven EM programme can be implemented in the CICU.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".