Changing the Sheets
Bibliographic record
Abstract
BACKGROUND: Manually repositioning patients puts healthcare providers at risk for injury; this may be reduced by using low-friction bedsheets. OBJECTIVES: The aim of this study was to evaluate the physical properties and the physiological measures of muscle activity and perceptual participant accounts between a new slider sheet system and traditional hospital bedsheet makeup (soaker pad with a jersey bottom sheet). METHOD: Surface electromyography was recorded from the arm and shoulder muscles of five healthcare providers executing a patient repositioning (boosting and turning) in a controlled laboratory setting to gain an indication of muscle activity required for two types of bedsheets (slider system and traditional sheet makeup). The Borg Scale was used to establish rating of perceived exertion for these repositioning tasks on the two types of bedsheet makeup. To evaluate the sheets independent of human interaction and contact, the physical resistive characteristics of the sheets were calculated by determining the coefficient of friction. RESULTS: Patient repositioning on traditional sheets, compared with the slider system, resulted in 16% greater electromyography burst numbers and 11% longer duration for both boosting and turning. Moreover, ratings of perceived exertion for repositioning patients on traditional sheets versus on slider sheets were more than double. The coefficient of friction of the traditional sheets was 65% less in the slider sheet system. DISCUSSION: This study suggests that manually repositioning patients on a low-friction slider system reduces muscular and perceived effort. Proper usage of this type of bedsheets may reduce the risks associated with musculoskeletal strain and injuries of the healthcare providers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".