Biomechanical evaluation and perceived exertion of a lateral patient-handling task
Bibliographic record
Abstract
BACKGROUND: Slide sheets (SS) are friction-reducing devices used during patient-handling tasks. A modified SS position (modSS), with the slider placed beneath the regular bedsheet, may decrease a caregiver's workload and reduce low back injuries, as the SS could remain in place for longer periods o f time, thus reducing patient re-positioning frequency. OBJECTIVE: To investigate the efficacy of modSS use on back muscle activity, pulling force, and perceived effort during lateral patient-handling tasks, and determine whether lumbar electromyography (EMG) correlates with perceived effort (RPE) during such tasks. METHODS: Ten females completed 9 lateral patient-handling tasks with 3 simulated patients (45 kg, 68 kg and 91 kg) and 3 SS conditions (absent, normal, modSS). Outcomes included peak pulling force, back muscle EMG, RPE and subjective reports of low-back discomfort and preference. RESULTS: ModSS use was as effective as or better than normal SS use at reducing back muscle EMG, pulling force, RPE and perceived discomfort in all 9 conditions, when compared to no SS (p< 0.05). The relationship between RPE and EMG was moderately strong (r= 0.75). CONCLUSION: ModSS use may reduce caregiver injury rates, as it reduces biomechanical and perceived demands associated with lateral patient-handling tasks at least as well as normal SS use, if not better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".