Unintentional injuries in children and youth from immigrant families in Ontario, Canada: a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Unintentional injury is the leading cause of childhood death. Injury is associated with a number of sociodemographic characteristics, but little is known about risk in immigrants. Our objective was to examine the association between family immigrant status and unintentional injury in children and youth. METHODS: We performed a population-based, cross-sectional study involving children and youth (age 0-24 yr) residing in Ontario from 2008 to 2012. Multiple linked health and administrative databases were used to describe unintentional injuries by family immigrant status. Unintentional injury events (e.g., emergency department visits, admissions to hospital, deaths) were analyzed using Poisson regression models to estimate rate ratios (RRs) for injury by immigrant status. RESULTS: Annualized injury rates were 11 749 emergency department visits per 100 000 population, 267 hospital admissions per 100 000 population and 12 deaths per 100 000 population. Injury rates were lower among immigrants across all causes of unintentional injury (adjusted RR 0.56, 95% confidence interval [CI] 0.54-0.59). Among nonimmigrants, lowest neighbourhood income quintile was associated with the highest rates (RR 1.13, 95% CI 1.08-1.18, quintile 5 v. 1); among immigrants, lowest income quintile was associated with the lowest rates of injury (RR 0.88, 95% CI 0.82-0.94, quintile 5 v. 1). Highest rates of injury for nonimmigrants were among adolescents (age 10-14 yr, RR 1.23, 95% CI 1.18-1.28; v. 20-24 yr), but for immigrants, was highest among young children (0-4 yr RR 1.23, 95% CI 1.16-1.31; v. 20-24 yr). INTERPRETATION: Rates of unintentional injury are lower among immigrant than among Canadian-born children, supporting a healthy immigrant effect. Socioeconomic status and age have different associations with injury risk, suggesting alternative causal pathways for injuries in immigrant children and youth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".