Multiple Risk Behavior and Injury
Bibliographic record
Abstract
BACKGROUND: Multiple risk behavior plays an important role in the social etiology of youth injury, yet the consistency of this observation has not been examined multinationally. OBJECTIVE: To examine reports from young people in 12 countries, by country, age group, sex, and injury type, to quantify the strength and consistency of this association. SETTING: World Health Organization collaborative cross-national survey of health behavior in school-aged children. PARTICIPANTS: A multinational representative sample of 49 461 students aged 11, 13, and 15 years. MAIN EXPOSURE MEASURES: Additive score consisting of counts of self-reported health risk behaviors: smoking, drinking, nonuse of seat belts, bullying, excess time with friends, alienation at school and from parents, truancy, and an unusually poor diet. MAIN OUTCOME MEASURE: Self-report of a medically treated injury. RESULTS: Strong gradients in risk for injury were observed according to the numbers of risk behaviors reported. Overall, youth reporting the largest number (> or =5 health risk behaviors) experienced injury rates that were 2.46 times higher (95% confidence interval, 2.27-2.67) than those reporting no risk behaviors (adjusted odds ratios for 0 to > or =5 reported behaviors: 1.00, 1.22, 1.48, 1.73, 1.98, and 2.46, respectively; P<.001 for trend). Similar gradients in risk for injury were observed among youth in all 12 countries and within all demographic subgroups. Risk gradients were especially pronounced for nonsports, fighting-related, and severe injuries. CONCLUSIONS: Gradients in risk for youth injury increased in association with numbers of risk behaviors reported in every country examined. This cross-cultural finding indicates that the issue of multiple risk behavior, as assessed via an additive score, merits attention as an etiological construct. This concept may be useful in future injury control research and prevention efforts conducted among populations of young people.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".