Neighbourhood variation in hospitalization for unintentional injury among children and teenagers.
Bibliographic record
Abstract
BACKGROUND: Research suggests that living in more affluent neighbourhoods positively influences children's health. Relationships with injury are less clear. This study examines variations in rates of unintentional injury hospitalization by neighbourhood income for the population aged 0 to 19 in urban Canada. DATA AND METHODS: Acute-care inpatient hospitalization discharge records from 2001/2002 through 2004/2005 for 0-to 19-year-olds were examined. Injuries were classified using the International Classification of Diseases. Census Dissemination Areas were used as neighbourhood proxies; income quintiles were calculated from the 2001 Census. Age-standardized rates of hospitalization per 10,000 person-years at risk were calculated for each type of injury, by sex, age group and neighbourhood income quintile. RESULTS: Children and teenagers in the lowest neighbourhood income quintile generally had a higher rate of unintentional injury hospitalization than did those in the highest. The pattern was particularly evident among children aged 0 to 9 in lower-income neighbourhoods for injuries due to land transportation, poisoning, fire, drowning/ suffocation, being cut or pierced, and the natural environment. INTERPRETATION: Canadian children in lower-income neighbourhoods generally have higher rates of hospitalization due to unintentional injuries, compared with children in higher-income neighbourhoods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".