Recent trends in child and youth emergency department visits because of pedestrian motor vehicle collisions by socioeconomic status in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Children in lower-income households have higher injury rates. Trends in emergency department (ED) visits by children 0-19 years because of pedestrian motor vehicle collisions (PMVCs) in Ontario, Canada (2008-2015) by socioeconomic status were examined. METHODS: PMVC ED data were obtained from the Institute for Clinical Evaluative Sciences for children age 0-19 years over the period 2008-2015. Age-adjusted rates were calculated using Ontario census data. Household income quintiles were determined from the Registered Persons Database. Poisson regression was used to model ED visit rates by year, age and income quintile. RESULTS: The frequency of child PMVC ED visits in Ontario decreased from 1562 in 2008 to 1281 in 2015. Age-adjusted rates were unchanged over time (IRR 1.00, 95% CI 0.99 to 1.00); however, rate disparities by income status persisted with an IRR of 0.52 (0.50 to 0.55) comparing the highest with the lowest income level. CONCLUSIONS: Exposure to traffic may play a role in rate disparities by income status in child PMVC; however, less safe traffic environments in lower income areas may also be strong contributors. These findings highlight the potential impact of roadway safety modifications in lower income areas to mitigate disparities in injury rates by socioeconomic status.
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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) | 0.003 | 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".