Comparison of data sources for the surveillance of work injury
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compare the incidence of work-related injury and illness presenting to Ontario emergency departments to the incidence of worker's compensation claims reported to the Ontario Workplace Safety & Insurance Board over the period 2004-2008. METHODS: Records of work-related injury were obtained from two administrative data sources in Ontario for the period 2004-2008: workers' compensation lost-time claims (N=435,336) and records of non-scheduled emergency department visits where the main problem was attributed to a work-related exposure (N=707,963). Denominator information required to compute the risk of work injury per 2,000,000 work hours, stratified by age and gender was estimated from labour force surveys conducted by Statistics Canada. RESULTS: The frequency of emergency department visits for all work-related conditions was approximately 60% greater than the incidence of accepted lost-time compensation claims. When restricted to injuries resulting in fracture or concussion, gender-specific age differences in injury incidence were similar in the two data sources. Between 2004 and 2008, there was a 14.5% reduction in emergency department visits attributed to work-related causes and a 17.8% reduction in lost-time compensation claims. There was evidence that younger workers were more likely than older workers to seek treatment in an emergency department for work-related injury. CONCLUSIONS: In this setting, emergency department records available for the complete population of Ontario residents are a valid source of surveillance information on the incidence of work-related disorders. Occupational health and safety authorities should give priority to incorporating emergency department records in the routine surveillance of the health of workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".