Effectiveness of a multifactorial ergonomic intervention and exercise conditioning kinesiology program for subsequent work related musculoskeletal disorder prevention
Bibliographic record
Abstract
BACKGROUND: Health care workers are known to be at high risk for occupational musculoskeletal disorders and injuries. Many different intervention strategies have been implemented in order to reduce the risk of injury to health care workers, however not many have been proven to be effective. A continuing care establishment in Ottawa, Canada, implemented a multidimensional kinesiology intervention program for injured employees. OBJECTIVE: To evaluate the effectiveness of the kinesiology intervention program on reducing subsequent rates for the nursing personnel and analyze the impact of age, position (RN, RPN, PCA) and sex on subsequent injuries. METHODS: The number of recorded work related musculoskeletal disorders (WRMSD) for each patient care worker was counted from 2007-2011. The control group included injured employees that were not referred to the kinesiologists, or who chose not to participate in the program. The intervention group included injured employees that were referred to the kinesiology service and followed their treatment program. RESULTS: The intervention group showed a statistically significant reduction of subsequent acute cases when compared to the control group. Nevertheless there was no significant impact regarding age, position or sex on subsequent acute cases. CONCLUSION: The kinesiology intervention program within the health care facility was effective at reducing subsequent WRMSD rates within the nursing personnel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".