Alcohol Consumption and Injury Among Canadian Adolescents: Variations by Urban–Rural Geographic Status
Bibliographic record
Abstract
CONTEXT: The impact of alcohol consumption on risks for injury among rural adolescents is an important and understudied public health issue. Little is known about whether relationships between alcohol consumption and injury vary between rural and urban adolescents. PURPOSE: To examine associations between alcohol and medically attended injuries by urban-rural geographic status using a representative national sample of Canadian adolescents. METHODS: The study involved a secondary analysis of a national sample of Canadian adolescents aged 11-15 years (n = 7,031) from the 2001-2002 Health Behavior in School-Aged Children Survey. Respondents were classified into 5 geographic categories of rural-urban status. Multiple logistic regression was used to examine the magnitude and homogeneity of associations between drinking patterns and adolescent injuries across these 5 geographic groupings. FINDINGS: Higher rates of alcohol consumption and adolescent injuries were observed in more rural areas. Alcohol consumption was significantly associated with higher risks for injury occurrence with evidence of a dose-related pattern of risk. Associations between alcohol consumption and injury were consistent by urban-rural geographic status. CONCLUSIONS: Misuse of alcohol is an important potential cause of injury. Adolescents whose lifestyle includes alcohol consumption experience higher risks for injury, and this association is observed consistently by urban-rural geographic status. Findings of this study emphasize a need to intervene with high-risk adolescents as a tertiary prevention strategy, irrespective of geographic background.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".