The impact of mechanical devices for lifting and transferring of patients on low back pain and musculoskeletal injuries in health care personnel—A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Heavy lifting in nursing is highly associated with low back pain (LBP) and musculoskeletal injuries (MSI). We aimed to evaluate the impact of mechanical devices used for patient lifting and transferring on risk of LBP and MSI of health care personnel. METHODS: We conducted a systematic review and meta-analysis. The literature search was performed during 1st and 12th September 2021 using 17 electronic databases and handsearching of bibliographies of included studies. Twenty studies were included in the qualitative synthesis and eight studies with in total 2087 participants in the meta-analysis. Dependent on the study design, risk of bias was assessed by Cochrane RoB 2.0, EPOC, and MINORS. We conducted random-effects meta-analyses assessing Hedges's g and 95% CI of MSI rate, perceived LBP, and peak compressive spinal load. We calculated prediction intervals and conducted a cost-benefit analysis (CBA). RESULTS: All outcomes showed significant, adjusted pooled effect sizes (MSI rate: g = 1.11, 95% CI 0.914-1.299; perceived LBP: g = 1.54, 95% CI -0.016-3.088; peak compressive spinal load: g = 1.04, 95% CI -0.315 to 2.391). True effect sizes in 95% of all comparable populations fell in the following prediction intervals: MSI rate = -1.07-3.28, perceived LBP = -0.522-3.594, and peak compressive spinal load = -15.49 to 17.57. CBA revealed cost-benefit ratios of 1.2 and 3.29 between cumulative total savings and investment costs of intervention. CONCLUSIONS: Prediction intervals confirmed strong true effect sizes for MSI rate and perceived LBP in 95% of all comparable populations but not for peak compressive spinal load. Mechanical lifting and transferring devices displayed a favorable cost-benefit ratio and should be considered for clinical implementation.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.051 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".