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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".