A Randomized Controlled Trial to Prevent Patient Lift and Transfer Injuries of Health Care Workers
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it