Patterns of youth injury: a comparison across the northern territories and other parts of Canada
Bibliographic record
Abstract
BACKGROUND: Injury is the leading cause of death for young people in Canada. For those living in the northern territories (Yukon, Nunavut, and the Northwest Territories), injury represents an even greater problem, with higher rates of injury for people of all ages in northern areas compared with the rest of Canada; however, no such comparative studies have focussed specifically on non-fatal injury in youth. OBJECTIVES: To profile and examine injuries and their potential causes among youth in the northern territories as compared with other parts of Canada. DESIGN: Cross-sectional data from the 2009/2010 Health Behaviour in School-aged Children survey (youth aged 11-15 years) were examined for the Canadian northern territories and the provinces (n=26,078). Individual survey records were linked to community-level data to profile injuries and then study possible determinants via multilevel regression modelling. RESULTS: The prevalence of injury reported by youth was similar in northern populations and other parts of Canada. There were some minimal differences by injury type: northern youth experienced a greater percentage of neighbourhood (p<0.001) and fighting (p=0.02) injuries; youth in the Canadian provinces had a greater proportion of sport-related injuries (p=0.01). Among northern youth, female sex (RR=0.87, 95% CI 0.81-0.94), average (RR=0.88, 95% CI 0.80-0.97) or above-average affluence (RR=0.84, 95% CI 0.76-0.91), not being drunk in the past 12 months (RR=0.77, 95% CI 0.69-0.85), not riding an all-terrain vehicle (RR=0.81, 95% CI 0.68-0.97) and not having permanent road access (RR=0.89, 95% CI 0.80-0.98) were protective against injury; sport participation increased risk (RR=1.45, 95% CI 1.33-1.59). CONCLUSIONS: Patterns of injury were similar across youth from the North and other parts of Canada. Given previous research, this was unexpected. When implementing injury prevention initiatives, individual and community-level risk factors are essential to understand; however, specific positive safety assets that might exist in different community contexts must also be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".