Neighbourhood income gradients in hospitalisations due to motor vehicle traffic incidents among Canadian children
Bibliographic record
Abstract
OBJECTIVE: To investigate income gradients in motor vehicle traffic injury hospitalisation for vehicle occupants and pedestrians/cyclists among children in urban and rural Canada. DESIGN: Four years (2001/02-2004/05) of acute-care hospitalisation discharge records for children aged 0-19 years were analysed. International Classification of Disease codes were used to determine hospitalisations due to motor vehicle traffic incidents for occupants and pedestrians/cyclists. Rates of injury (per 10 000 person years) were calculated by neighbourhood income quintiles for urban and rural areas. RESULTS: Among children (0-19 years), rates of vehicle occupant hospitalisation were higher in rural (5.07, 95% CI 4.90 to 5.25) than urban areas (2.08, 95% CI 2.03 to 2.14). In rural areas, children from lower income neighbourhoods had higher vehicle occupant hospitalisation rates than those from the richest neighbourhoods (5.52, 95% CI 5.13 to 5.93 vs 4.30, 95% CI 3.97 to 4.66). In urban areas vehicle occupant hospitalisation rates were similar among children from the poorest and richest neighbourhoods--but higher among children from middle income neighbourhoods. In urban areas, but not rural areas, the hospitalisation rate for pedestrians/cyclists systematically increased with decreasing neighbourhood income. In urban areas the pedestrian/cyclist hospitalisation rate was four times higher for children from the poorest (1.40, 95% CI 1.25 to 1.57) than from the richest (0.34, 95% CI 0.28 to 0.43) neighbourhoods. CONCLUSIONS: While vehicle occupant and pedestrian/cyclist motor vehicle traffic injuries are more frequent among children from lower income neighbourhoods, gradients are most pronounced for pedestrians/cyclists in urban 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".