Cross national study of injury and social determinants in adolescents
Bibliographic record
Abstract
OBJECTIVES: To compare estimates of the prevalence of injury among adolescents in 35 countries, and to examine the consistency of associations cross nationally between socioeconomic status then drunkenness and the occurrence of adolescent injury. DESIGN: Cross sectional surveys were obtained from national samples of students in 35 countries. Eight countries asked supplemental questions about injury. SETTING: Surveys administered in classrooms. SUBJECTS: Consenting students (n = 146 440; average ages 11-15 years) in sampled classrooms. 37 878 students (eight countries) provided supplemental injury data.Exposure measures: Socioeconomic status (material wealth, poverty) and social risk taking (drunkenness). OUTCOME MEASURES: Specific types and locations of medically treated injury. RESULTS: By country, reports of medically treated injuries ranged from 33% (1060/3173) to 64% (1811/2833) of boys and 23% (740/3172) to 51% (1485/2929) of girls, annually. Sports and recreation were the most common activities associated with injury. High material wealth was positively (OR>1.0; p<0.05) and consistently (6/8 countries) associated with medically treated and sports related injuries. Poverty was positively associated with fighting injuries (6/8 countries). Drunkenness (social risk taking) was positively (p<0.01) and consistently (8/8 countries) associated with medically treated, street, and fighting injuries, but not school and sports related injuries. CONCLUSION: The high prevalence of adolescent injury confirms its importance as a health problem. Social gradients in risk for adolescent injury were illustrated cross nationally for some but not all types of adolescent injury. These gradients were most evident when the etiologies of specific types of adolescent injury were examined. Prevention initiatives should focus upon the etiologies of specific injury types, as well as risk oriented social contexts.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".