Multilevel analysis of associations between socioeconomic status and injury among Canadian adolescents
Bibliographic record
Abstract
STUDY OBJECTIVE: To determine the contribution of individual and area level measures of socioeconomic status (SES) to the occurrence of various injury types among Canadian adolescents. DESIGN AND SETTING: Cross sectional Canadian data were used from two sources: (1) the 2001/02 health behaviour in school aged children survey (individual level SES measures, injury measures), and (2) the 2001 Canada census of population (area level SES measures). Injury outcomes included: medically treated injury, injury hospitalisation, sport/recreational injury, and fighting injury. Multilevel logistic regression models were used to examine individual and area level SES measures as potential determinants of adolescent injury. PARTICIPANTS: 7235 students in grades 6-10 from 170 schools across Canada. MAIN RESULTS: Associations between SES and injury were identified for each injury outcome examined, although a clear direction of association was not present for the overall measure of medically treated injury. In general, lower SES was associated with increased risk for hospitalised and fighting injury. Higher SES was associated with increased risks for sport/recreational injury. Independent contributions of individual and area level measures of SES were seen for hospitalised and fighting injury. CONCLUSIONS: Associations between SES and adolescent injury exist; however, the direction of these relations becomes more apparent with particular indicators of SES and when homogenous injury outcomes are evaluated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".