Self-reported injuries among Canadian adolescents: rates and key correlates
Bibliographic record
Abstract
INTRODUCTION: Injuries sustained by adolescents in Canada represent a costly public health issue. Much of the limited research in this area uses administrative data, which underestimate injury prevalence by ignoring injuries that are not treated by the health care system. Self-reported data provide population-based estimates and include contextual information that can be used to identify injury correlates and possible targets for public health interventions aimed at decreased injury burden. METHODS: The 2017 wave of the Canadian Community Health Survey was used to calculate the prevalence of self-reported total, intentional and unintentional injuries. We compared injury prevalence according to age, sex, employment status, presence of a mood disorder, presence of an anxiety disorder, smoking and binge drinking. Analyses were performed using logistic regression to identify significantly different injury prevalence estimates across key correlates. RESULTS: Overall past-12-month injury prevalence among adolescents living in Canada was 31.4% (95% CI: 29.4%-33.5%). Most injuries were unintentional. All provinces had estimates within a few percentage points, except Saskatchewan, which had substantially higher prevalence for both overall and unintentional injury. Smoking and binge drinking were significantly associated with higher injury prevalence in most jurisdictions. Remaining correlates exhibited nonsignificant or inconsistent associations with injury prevalence. CONCLUSION: The data suggest that injury prevention interventions aimed at reducing alcohol consumption, particularly binge drinking, may be effective in reducing adolescent injury across Canada. Future research is needed to determine how provincial context (such as mental health support for adolescents or programs and policies aimed at reducing substance use) impacts injury rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".