Nature of Injury and Risk of Multiple Claims Among Workers in Manitoba Health Care
Bibliographic record
Abstract
In industrial societies, work-related musculoskeletal disorders are common among workers, frequently resulting in recurrent injuries, work disability, and multiple compensation claims. The risk of idiopathic musculoskeletal injuries is thought to be more than twice the risk of any other health problem among workers in the health care sector. This risk is highly prevalent particularly among workers whose job involves frequent physical tasks, such as patient lifting and transfer. Workers with recurrent occupational injuries are likely to submit multiple work disability claims and progress to long-term disability. The objective of this study was to explore the influence of injury type and worker characteristics on multiple compensation claims, using workers' compensation claims data. This retrospective study analyzed 11 years of secondary claims data for health care workers. Workers' occupational groups were classified based on the nature of physical tasks associated with their jobs, and the nature of work injuries was categorized into non-musculoskeletal, and traumatic and idiopathic musculoskeletal injuries. The result shows that risk of multiple injury claims increased with age, and the odds were highest for older workers aged 55 to 64 (odds ratio [OR] = 3.5). A large proportion of those who made an injury claim made multiple claims that resulted in more lost time than single injury claims. The study conclusion is that the nature of injury and work tasks are probably more significant risk factors for multiple claims than worker characteristics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".