Risk of firearm injuries among children and youth of immigrant families
Bibliographic record
Abstract
BACKGROUND: Firearm injuries contribute to substantial morbidity and mortality. The immigrant paradox suggests that, despite being more socially disadvantaged, immigrants are less likely than nonimmigrants to have poor outcomes. We tested the association of immigrant characteristics with firearm injuries among children and youth. METHODS: We conducted a population-based cohort study involving residents of Ontario aged 24 years and younger from 2008-2012 using health and administrative databases. We estimated rate ratios of unintentional and assault-related firearm injuries by immigrant status using Poisson regression models with Generalized Estimating Equations. RESULTS: We included 15 866 954 nonimmigrant and 4 551 291 immigrant person-years in our analysis. Nonimmigrant males had 1032 unintentional (12.4 per 100 000, 95% confidence interval [CI] 11.7-13.2) and 304 assault-related (3.6 per 100 000, 95% CI 3.2-4.0) firearm injuries. Immigrant males had 148 unintentional (7.2 per 100 000, 95% CI 6.1-8.5) and 113 assault-related (5.5 per 100 000, 95% CI 4.5-6.6) firearm injuries. Compared with nonimmigrants, immigrants had a lower rate of unintentional firearm injury (adjusted rate ratio 0.5, 95% CI 0.4-0.6) but a similar rate of assault-related firearm injury. Among immigrants, refugees had a 43% higher risk of assault-related firearm injury compared with nonrefugees (adjusted rate ratio 1.4, 95% CI 1.0-2.0). Immigrants from Central America and Africa accounted for 68% of immigrants with assault-related firearm injuries. INTERPRETATION: Compared with nonimmigrants, immigrant children and youth had a lower risk of unintentional firearm injury, although the risk of assault-related firearm injury was higher among refugees and immigrants from Central America and Africa. The results suggest that prevention strategies for firearm safety should target nonimmigrant youth as well as these newly identified high-risk immigrant populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".