Socioeconomic differences in childhood injury: a population based epidemiologic study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine whether risks for childhood injury vary according to socioeconomic gradients. DESIGN: Population based, retrospective study. The percentage of individuals living below the poverty line (described ecologically using census data) was the primary measure of socioeconomic status. SETTING: Catchment area of a tertiary medical centre that provides emergency services to all area residents. Area residents aged 0-19 years during 1996 were included. OBSERVATIONS: Injuries that occurred during 1996 were identified by an emergency department based surveillance system. The study population was divided into socioeconomic grades based upon percentages of area residents living below the poverty line. Multiple Poisson regression analyses were used to quantify associations and assess the statistical significance of trends. RESULTS: 5894 childhood injuries were identified among 35380 eligible children; 985 children with missing socioeconomic data were excluded. A consistent relation between poverty and injury was evident. Children in the highest grade (indicating higher poverty levels) experienced injury rates that were 1.67 (95% confidence interval 1.48 to 1.89) higher than those in the lowest grade (adjusted relative risk for grades 1-V: 1.00,1.10,1.22,1.42, 1.67; Ptrend < 0.001). These patterns were observed within age/sex strata; for home, recreational, and fall injuries; and for injuries of minor and moderate severities. CONCLUSIONS: Socioeconomic differences in childhood injury parallel mortality and morbidity gradients identified in adult populations. This study confirms that this health gradient is observable in a population of children using emergency department data. Given the population based nature of this study, these findings are likely to be reflected in other settings. The results suggest the need for targeted injury prevention efforts among children from economically disadvantaged populations, although the exact requirements of the optimal prevention approach remain elusive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 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".