Fatal agricultural injuries in preschool children: risks, injury patterns and strategies for prevention
Bibliographic record
Abstract
BACKGROUND: Agricultural injuries are an important health concern for pediatric populations and particularly for children of pre school age. This study was conducted to estimate rates and determine patterns of fatal agricultural injury among young children exposed to agricultural hazards and to identify strategies to prevent such injuries. METHODS: A national case series was assembled retrospectively for the years 1990-2001. We identified children aged 1-6 years who were fatally injured during the course of agricultural work or through contact with a hazard of an agricultural worksite. Using a standardized survey instrument, we collected data from provincial coroners' and medical examiners' case files. Fatal agricultural injury rates (calculated with denominator data from the Canada Census of Agriculture) were compared with national all-cause, unintentional fatal injury rates in the general population of Canadian children during the same period (calculated with denominator data from the Canada Census of Population). RESULTS: The annual rate of fatal agricultural injury was substantially higher than that of all-cause, unintentional fatal injury among Canadian children aged 1-6 years (14.9 v. 8.7 per 100,000 person-years, respectively). Differences in risk were attributed to elevated fatal agricultural injury rates among boys. Most injuries occurred in the agricultural worksite, largely (84/115 [73%]) the result of 3 mechanisms: being run over by agricultural machinery as a bystander (29%) or as an extra rider who fell from the machine (22%), or asphyxia due to drowning (23%). Major crush injuries (of the head, chest and abdomen) and asphyxia from drowning were the most frequent mechanisms of injury. INTERPRETATION: Preschool-aged children exposed to agricultural worksites are at high risk of fatal injuries. Prevention strategies should focus on restricting children's access to these worksites. Physicians and allied health care professionals who care for rural families could take on a proactive role in communicating the nature and magnitude of these risks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".