Can the Use of Turn-Assist Surfaces Reduce the Physical Burden on Caregivers When Performing Patient Turning?
Bibliographic record
Abstract
OBJECTIVE: To quantify differences in physical workload afforded by turn-assist surfaces relative to manual patient turns, and between nursing caregivers (turn-away vs. turn-toward) while performing partnered patient turning. BACKGROUND: Nurse caregivers experience an increased risk of musculoskeletal injuries at the back or shoulders when performing patient-handling activities. Use of turn-assist surfaces can reduce the physical burden and risk on caregivers. METHOD: Whole-body motion capture and hand force measures were collected from 25 caregivers (17 female) while performing partnered manual and technology-facilitated turns. Shoulder and low back angles and L4/L5 joint contact forces were calculated at the instant of peak hand force application for both caregivers. RESULTS: Hand force requirements for the turn-away caregiver were 93% of the estimated maximum acceptable force when performing a manual turn. Use of a turn-assist surface eliminated hand forces required to initiate the patient turn for the turn-away caregiver, where their role was reduced to inserting appropriate wedging behind the patient once the facilitated turn was complete. This reduced shoulder moments by 21.3 Nm for the turn-away caregiver, a reduction in exposure from 70% of maximum shoulder strength capacity to 15%. Spine compression exposures were reduced by 302.1 N for the turn-toward caregiver when using a turn-assist surface. CONCLUSION: Use of a turn-assist surface reduced peak hand force and shoulder-related exposures for turning away and reduced spine-related exposures for turning toward. APPLICATION: Turn-assist devices should be recommended to decrease the risk of musculoskeletal disorder hazards for both caregivers when performing a partnered patient turn.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".