A Randomized Controlled Trial to Prevent Patient Lift and Transfer Injuries of Health Care Workers
Bibliographic record
Abstract
STUDY DESIGN: Randomized controlled trial (RCT). OBJECTIVES: To compare the effectiveness of training and equipment to reduce musculoskeletal injuries, increase comfort, and reduce physical demands on staff performing patient lifts and transfers at a large acute care hospital. SUMMARY OF BACKGROUND DATA: Back injury to nursing staff during patient handling tasks is a major issue in health care. The value of mechanical assistive devices in reducing injuries to these workers is unclear. METHODS: This three-armed RCT consisted of a "control arm," a "safe lifting" arm, and a "no strenuous lifting" arm. A medical, surgical, and rehabilitation ward were each randomly assigned to each arm. Both intervention arms received intensive training in back care, patient assessment, and handling techniques. Hence, the "safe lifting" arm used improved patient handling techniques using manual equipment, whereas the "no strenuous lifting" arm aimed to eliminate manual patient handling through use of additional mechanical and other assistive equipment. RESULTS: Frequency of manual patient handling tasks was significantly decreased on the "no strenuous lifting" arm. Self-perceived work fatigue, back and shoulder pain, safety, and frequency and intensity of physical discomfort associated with patient handling tasks were improved on both intervention arms, but staff on the mechanical equipment arm showed greater improvements. Musculoskeletal injury rates were not significantly altered. CONCLUSIONS: The "no strenuous lifting" program, which combined training with assured availability of mechanical and other assistive patient handling equipment, most effectively improved comfort with patient handling, decreased staff fatigue, and decreased physical demands. The fact that injury rates were not statistically significantly reduced may reflect the less sensitive nature of this indicator compared with the subjective indicators.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".