How many injured workers do not file claims for workers' compensation benefits?
Bibliographic record
Abstract
BACKGROUND: Anecdotal evidence suggests that there are injured workers who do not file for workers' compensation (WC). Several recent studies support this, and we aim to quantify the extent of under-reporting. METHODS: A Canadian survey asked about work injuries in the previous year, and several questions established eligibility for WC and whether a claim had been filed. The proportion of eligible injuries with a claim was estimated. Logistic regression identified predictors of claim submission. RESULTS: Of 2,500 respondents, 143 had incurred an eligible injury, of whom 57 (40%, 95% CI 32-48%) had not filed a WC claim. Severity of injury was the strongest predictor of not claiming. CONCLUSIONS: Survey respondents reported a substantial degree of under-claiming of WC benefits, contrasting with public attention on fraudulent over-claiming. Policy makers should ensure that all relevant parties are aware of their obligations to report work injuries. This will create a more accurate picture of work safety.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".