Agricultural injuries among farm and non‐farm children and adolescents in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Understanding of the specific risk of agricultural injury sustained by different populations of children and adolescents is needed for effective safety intervention. OBJECTIVE: To compare the rates and patterns of agricultural injury incidence (fatal and non-fatal injury) between farm and non-farm children less than 18 years of age in Alberta, Canada. METHODS: A total of 115 378 children (five subgroups: two groups of farm children and three groups of non-farm children) in Alberta were followed from 1999 to 2010 to examine injury incidence using the linkage of three administrative health databases. A recurrent event survival analysis using Cox proportional hazards regression was carried out. RESULTS: A total of 1 849 agricultural injury episodes (1 616 emergency department visits, 225 hospitalizations, and 8 deaths) were identified from 1999 to 2010. The age- and gender-adjusted rate (per 100 000 person years) of agricultural injury was 672.3 for rural-living farm children, 369.4 for urban-living farm children, 180.2 for rural non-First Nations (FN) children, 64.4 for rural FN children, and 23.7 for urban children in descending order. CONCLUSION: Specific strategies for different children's populations to prevent agricultural injuries and to extend agricultural injury controls to non-farming populations are needed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".