Characteristics of work-related fatal and hospitalised injuries not captured in workers’ compensation data
Bibliographic record
Abstract
OBJECTIVES: (1) To identify work-related fatal and non-fatal hospitalised injuries using multiple data sources, (2) to compare case-ascertainment from external data sources with accepted workers' compensation claims and (3) to investigate the characteristics of work-related fatal and hospitalised injuries not captured by workers' compensation. METHODS: Work-related fatal injuries were ascertained from vital statistics, coroners and hospital discharge databases using payment and diagnosis codes and injury and work descriptions; and work-related (non-fatal) injuries were ascertained from the hospital discharge database using admission, diagnosis and payment codes. Injuries for British Columbia residents aged 15-64 years from 1991 to 2009 ascertained from the above external data sources were compared to accepted workers' compensation claims using per cent captured, validity analyses and logistic regression. RESULTS: The majority of work-related fatal injuries identified in the coroners data (83%) and the majority of work-related hospitalised injuries (95%) were captured as an accepted workers' compensation claim. A work-related coroner report was a positive predictor (88%), and the responsibility of payment field in the hospital discharge record a sensitive indicator (94%), for a workers' compensation claim. Injuries not captured by workers' compensation were associated with female gender, type of work (natural resources and other unspecified work) and injury diagnosis (eg, airway-related, dislocations and undetermined/unknown injury). CONCLUSIONS: Some work-related injuries captured by external data sources were not found in workers' compensation data in British Columbia. This may be the result of capturing injuries or workers that are ineligible for workers' compensation, or the result of injuries that go unreported to the compensation system. Hospital discharge records and coroner reports may provide opportunities to identify workers (or family members) with an unreported work-related injury and to provide them with information for submitting a workers' compensation claim.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".