Economic Burden of Agricultural Machinery Injuries in Ontario, 1985 to 1996
Bibliographic record
Abstract
CONTEXT: Agricultural injuries are an important and understudied category of occupational injuries. PURPOSE: This study estimated the economic burden of agricultural machinery injuries that occurred in Ontario, Canada's largest province, between 1985 and 1996. METHODS: Conventional methodology for estimating economic burden, as embodied in a computer program previously developed for this purpose, was applied to hospitalized, nonhospitalized, and fatal agricultural machinery injuries. FINDINGS: The total economic burden of these injuries over the 12-year study period was estimated to be 228.1 million dollars, or 19.0 million dollars annually (1995 Canadian dollars, 3.0% discount rate). By extrapolation, the economic burden of all farm injuries in Canada is estimated to be between 200 and 300 million dollars annually. CONCLUSIONS: Costing information about agricultural injuries provides support for the prioritization and development of injury-control initiatives.
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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".