Trend in injury-related mortality and morbidity among adolescents across 30 countries from 2002 to 2010
Bibliographic record
Abstract
BACKGROUND: The aim was to examine temporal trends in injury mortality and morbidity across 30 countries in Europe and North America, and the impact of regional geography and adolescent risk behaviours (including substance use and physical fighting) on such trends. METHOD: s: Data were obtained for 30 countries in 2002, 2006 and 2010. Mortality data were obtained from the World Health Organization's (WHO) Health for all database. Trends over time were described by WHO Regions using standardized rates comparisons and Poisson regression analyses. RESULTS: Injury-related mortality, but not morbidity, declined over time across all countries (from 10 to 8 deaths per 100 000 between 2001 and 2010), with notable differences observed by Regions (e.g. from 48 to 39 deaths in Russia). Risk behaviours included in the models were consistently and significantly associated with injury morbidity, with substance increasing the risk for injury by 1.15 to 1.36 among girls, and physical fighting increasing the risk by 1.21 to 1.31 among boys across WHO Regions. Risk behaviours did not explain the observed temporal trends. CONCLUSIONS: Injury mortality and morbidity represent different health phenomena. Efforts that have been made to make societies safer for children have seemed to be successful in reducing injury morbidity.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".