Effect of Transfer, Lifting, and Repositioning (TLR) Injury Prevention Program on Musculoskeletal Injury Among Direct Care Workers
Bibliographic record
Abstract
Musculoskeletal injuries among health care workers is very high, particularly so in direct care workers involved in patient handling. Efforts to reduce injuries have shown mixed results, and strong evidence for intervention effectiveness is lacking. The purpose of our study was to evaluate the effectiveness of a Transfer, Lifting and Repositioning (TLR) program to reduce musculoskeletal injuries (MSI) among direct health care workers. This study was a pre- and post-intervention design, utilizing a nonrandomized control group. Data were collected from the intervention group (3 hospitals; 411 injury cases) and the control group (3 hospitals; 355 injury cases) for periods 1 year pre- and post-intervention. Poisson regression analyses were performed. Of a total 766 TLR injury cases, the majority of injured workers were nurses, mainly with back, neck, and shoulder body parts injured. Analysis of all injuries and time-loss rates (number of injuries/100 full-time employees), rate ratios, and rate differences showed significant differences between the intervention and control groups. All-injuries rates for the intervention group dropped from 14.7 pre-intervention to 8.1 post-intervention. The control group dropped from 9.3 to 8.4. Time-loss injury rates decreased from 5.3 to 2.5 in the intervention group and increased in the control group (5.9 to 6.5). Controlling for group and hospital size, the relative rate of all-injuries and time-loss injuries for the pre- to post-period decreased by 30% (RR = 0.693; 95% CI = 0.60-0.80) and 18.6% (RR = 0.814; 95% CI = 0.677-0.955), respectively. The study provides evidence for the effectiveness of a multifactor TLR program for direct care health workers, especially in small hospitals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".