Long-term Effect of Multifactor Transfer, Lifting, and Repositioning Intervention Program Among Health Care Workers
Bibliographic record
Abstract
PP-29-187 Background/Aims: Various injury prevention programs have shown effectiveness in reducing back pain and musculoskeletal injuries among health care workers. However, little is known about the long-term effect of those intervention programs. The objective of this study was to evaluate the long-term effect of a multifactor Transfer, Lifting, and Repositioning (TLR) intervention program on musculoskeletal injuries among health care workers. Methods: This was a retrospective, TLR intervention study with a nonrandomized control group. Data were collected from 6 hospitals in 2 Health Regions in Saskatchewan, Canada from 1 September 2001 to 1 December 2006. Logistic regression analyses were performed to estimate the odds ratio and 95% confidence interval. Results: A total of 1953 injury cases from 1471 individuals (n = 983 for the intervention and n = 970 for the control) occurred during the study period. Most of them were females and 75% were nurses. A number of subsequent, repeated injuries were 149 (15.3%) and 114 (11.5%) individuals for the control and the intervention groups, respectively. The medium- and small-sized hospitals of the intervention group had significantly less repeated injuries than the control group (P = 0.001 and 0.002, respectively). RN/GDN nurses had significantly less repeated injuries in the intervention group than in the control group (P = 0.016). By body part, the intervention group had significantly less than the control group in all-back injuries (P = 0.001). Multivariate analysis showed that the odds of repeated injury for health care workers was significantly reduced in the intervention group comparing to the control group after controlling for hospital size (odds ratio = 0.618, 95% confidence interval = 0.27–0.81; P = 0.0005). An interaction of hospital size and group was not observed in the multivariate analysis. Conclusion: The long-term effect of the multifactor TLR intervention program seemed to be more sustained in the medium- or small-sized hospitals than the large hospitals. The applicability of injury prevention programs to different healthcare settings, such as home care and critical care, and the synergistic relationships between components of multifactor intervention programs need to be further explored.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".