Pediatric farm injuries involving non-working children injured by a farm work hazard: five priorities for primary prevention
Bibliographic record
Abstract
OBJECTIVES: To describe pediatric farm injuries experienced by children who were not engaged in farm work, but were injured by a farm work hazard and to identify priorities for primary prevention. DESIGN: Secondary analysis of data from a novel evaluation of an injury control resource using a retrospective case series. DATA SOURCES: Fatal, hospitalized, and restricted activity farm injuries from Canada and the United States. SUBJECTS: Three hundred and seventy known non-work childhood injuries from a larger case series of 934 injury events covering the full spectrum of pediatric farm injuries. METHODS: Recurrent injury patterns were described by child demographics, external cause of injury, and associated child activities. Factors contributing to pediatric farm injury were described. New priorities for primary prevention were identified. RESULTS: The children involved were mainly resident members of farm families and 233/370 (63.0%) of the children were under the age of 7 years. Leading mechanisms of injury varied by data source but included: bystander and passenger runovers (fatalities); drowning (fatalities); machinery entanglements (hospitalizations); falls from heights (hospitalizations); and animal trauma (hospitalizations, restricted activity injuries). Common activities leading to injury included playing in the worksite (all data sources); being a bystander to or extra rider on farm machinery (all data sources); recreational horseback riding (restricted activity injuries). Five priorities for prevention programs are proposed. CONCLUSIONS: Substantial proportions of pediatric farm injuries are experienced by children who are not engaged in farm work. These injuries occur because farm children are often exposed to an occupational worksite with known hazards. Study findings could lead to more refined and focused pediatric farm injury prevention initiatives.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".