Muscle activity during patient transfers: a preliminary study on the influence of lift assists and experience
Bibliographic record
Abstract
The purpose of this study was to examine muscle activity patterns during patient handling during manual transfers, and transfers using floor and ceiling lifts. EMG patterns during transfers from bed to wheelchair and wheelchair to bed as well as patient repositioning in novices and experienced participants were examined. Surface EMG was recorded from the upper and lower erector spinae, latissimus dorsi and trapezius muscles bilaterally. Overall, normalized mean and peak muscle activity were lowest using the ceiling lift, increasing with the floor lift, which were lower than manual transfers (novices: all p < 0.01). Experienced patient handlers demonstrated approximately two times greater trapezius and latissimus dorsi activity than novices, combined with lower mean erector spinae activity (p < 0.05, for most tasks). Integrated EMG for all muscles was directly proportional to the transfer time and was lowest during the manual transfer followed by the ceiling lift, with the floor lift being highest. The difference between the muscle activity patterns between the experienced and novice patient handlers may suggest a learned behaviour to protect the spine by distributing load to the shoulder. Further examination of the muscle activation patterns differences between experience levels could improve training techniques to develop better patient handling strategies.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".