Occupational management in the workplace and impact on injury claims, duration, and cost: a prospective longitudinal cohort
Bibliographic record
Abstract
Few workplaces have prospectively reviewed workplace and worker issues simultaneously and assessed their impact on Workers' Compensation Board (WCB) claims. In January of 2014, each worker in a large workplace in Saskatchewan, Canada, was prospectively followed for 1 year to determine factors that impact injury claim incidence, recovery, and costs. In total, 207 out of 245 workers agreed to complete the baseline survey (84.5%). In 2014, 82.5% of workers had self-reported pain, but only 35.5% submitted a WCB claim. Binary logistic regression was used to compare those with pain who did not submit a WCB injury claim to those with pain who did initiate a WCB claim. Independent risk factors associated with WCB claim incidence included depressed mood (odds ratio [OR] =2.75, 95% confidence interval [CI] 1.44-9.78) and lower job satisfaction (OR =1.70, 95% CI 1.08-10.68). Higher disability duration was independently associated with higher depressed mood (OR =1.60, 95% CI 1.05-4.11) and poor recovery expectation (OR =1.31, 95% CI 1.01-5.78). Higher cost disability claims were independently associated with higher depressed mood (OR =1.51, 95% CI 1.07-6.87) and pain catastrophizing (OR =1.11, 95% CI 1.02-8.11). Self-reported pain, physically assessed injury severity, and measured ergonomic risk of workstation did not significantly predict injury claim incidence, duration, or costs. In January 2015, the workplace implemented a new occupational prevention and management program. The injury incidence rate ratio reduced by 58% from 2014 to 2015 (IRR =1.58, 95% CI =1.28-1.94). The ratio for disability duration reduced by 139% from 2014 to 2015 (RR =2.39, 95% CI =2.16-2.63). Costs reduced from $114,149.07 to $56,528.14 per year. In summary, WCB claims are complex. Recognizing that nonphysical factors, such as depressed mood, influence injury claim incidence, recovery, and costs, can be helpful to claims management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".