Injuries in Ontario farm children: a population based study
Bibliographic record
Abstract
OBJECTIVES: To evaluate injury rates, patterns, and risk factors in 4,916 Ontario farm children aged 0-18 years. SETTING: 1,765 full time family operated Ontario farms with a husband-wife couple where the wife was of reproductive age. METHODS: Injury details were obtained from mothers, while parents and farm operators provided risk factor information retrospectively in a population based mail survey. Rates were calculated based on injury occurrence and person years at risk in different age groups. Descriptive analyses used cross tabulations of injury details by age, sex, and season. Injury risk factors were assessed using multiple logistic regression. RESULTS: Age specific injury rates ranged from 6.3-22.6 per thousand person years, peaking in 1-4 year olds. Although consistently higher for boys, both sexes showed similar trends in age specific rates. Rates likely represent underestimates due to diminished recall of past events. Open wounds to the head/face region were the most prevalent type of injury (17.1%) followed by fractures/dislocations to the upper extremities (14.9%). Mechanism differed by age group, though falls and machinery consistently ranked in the top three. Occurrence peaked in summer. Regression analyses indicated child's sex and parental education were associated with injury risk across age categories. Other risk factors, such as numbers of livestock, parental owner/operator status, and mother's off-site employment, differed between ages. CONCLUSIONS: Patterns and risk factors for injuries to farm children are heterogenous across age categories. Observed age differences are useful for targeting 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".