Unintentional injuries among refugee and immigrant children and youth in Ontario, Canada: a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Unintentional injuries are a leading reason for seeking emergency care. Refugees face vulnerabilities that may contribute to injury risk. We aimed to compare the rates of unintentional injuries in immigrant children and youth by visa class and region of origin. METHODS: Population-based, cross-sectional study of children and youth (0-24 years) from immigrant families residing in Ontario, Canada, from 2011 to 2012. Multiple linked health and administrative databases were used to describe unintentional injuries by immigration visa class and region of origin. Poisson regression models estimated rate ratios for injuries. RESULTS: There were 6596.0 and 8122.3 emergency department visits per 100 000 non-refugee and refugee immigrants, respectively. Hospitalisation rates were 144.9 and 185.2 per 100 000 in each of these groups. The unintentional injury rate among refugees was 20% higher than among non-refugees (adjusted rate ratio (ARR) 1.20, 95% CI 1.16, 1.24). In both groups, rates were lowest among East and South Asians. Young age, male sex, and high income were associated with injury risk. Compared with non-refugees, refugees had higher rates of injury across most causes, including for motor vehicle injuries (ARR 1.51, 95% CI 1.40, 1.62), poisoning (ARR 1.40, 95% CI 1.26, 1.56) and suffocation (ARR 1.39, 95% CI 1.04, 1.84). INTERPRETATION: The observed 20% higher rate of unintentional injuries among refugees compared with non-refugees highlights an important opportunity for targeting population-based public health and safety interventions. Engaging refugee families shortly after arrival in active efforts for injury prevention may reduce social vulnerabilities and cultural risk factors for injury in this population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".