A population-based comparison of injuries among farm children to non-farm children in Alberta, 1999-2010
Bibliographic record
Abstract
Objectives - To systematically review literature on rural-urban differences in pediatric injury incidence and to examine incidence of all-cause injury, agricultural injury, and injury-related health care utilization for farm children compared to several groups of non-farm children under 18 years of age in Alberta, Canada. Methods – A systematic review examined population-based observational studies published from 1970 to August 2013, that compared rates or health care outcomes of injury between rural and urban children (<18) living in Canada or the United States. Three of population-based retrospective cohort studies followed farm, rural, First Nations (FN), urban children from 1999 to 2010 to examine incidence of injury and related health services using the linkage of four administrative health databases (data from physician visits to deaths). Person-time incidence rates and adjusted hazard ratios were calculated based on injury episode. Results – Systematic review demonstrated that rural children sustained a higher rate of overall injury, particularly from MVC and suicide than urban children. Primary studies showed farm and rural children, especially rural FN children, had higher rates and greater utilizations of overall injury, especially for severe injuries, than urban children. This trend was consistent for most injury mechanisms but more notably for other land transport (e.g., ATVs, animal riding, agricultural vehicle-related injuries), natural/environmental (e.g., bees, insects, animals-related), and unintentional firearm-related injuries. Farm and rural non-FN children were at a greater risk of agricultural injuries, more outstandingly for farm-animal and machinery-related injuries, than rural FN and urban children. Agricultural injuries appeared to be more unintentional and lethal. Rural FN children, followed by rural non-FN and farm children, experienced greater utilization of higher levels of medical facilities, thinner shapes of injury pyramid, and greater proportions of pre-hospital deaths. Conclusions – Greater burden of injury for farm and rural children and specific patterns per group indicate a need for targeted and specialized injury prevention strategies for higher-risk mechanisms in each group, attention for agricultural injury controls to extended populations, comprehensive intervention strategies for underlying inter-related causes of injury in rural areas, and an advanced pediatric trauma care for serious injuries the ED for efficient and timely care in rural areas.
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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 | 0.000 | 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".