School Injury Among Ottawa‐Area Children: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: Injuries are the leading cause of death among Canadian children and are responsible for a substantial proportion of hospitalizations and emergency department visits. This investigation sought to identify the factors associated with the likelihood of sustaining an injury at school among Ottawa-area children. METHODS: Children presenting to Ottawa-area hospitals and urgent care clinics from January to December 2002 (n = 24,074) were included for analysis. The frequency of school injuries by sex, age group, type of injury, and hospitalization was analyzed. Multivariate logistic regression was used to assess the factors associated with sustaining an injury at school. The school activities most associated with injury and the most frequent types of school injuries were assessed. RESULTS: A total of 4287 Ottawa-area children were injured at school in 2002, representing 18% of all injuries. Children aged 5-9 years and 10-14 years were more likely to have school injuries than older children (aged 15-19 years) (OR = 3.07, 95% CI = 2.77-3.40 and OR = 3.10, 95% CI = 2.83-3.37, respectively). The most frequently encountered school injuries were fractures (n = 1132) and musculoskeletal injury (n = 907). The most frequent mechanisms of school injuries were "playing" (n = 1004) and "informal sports" (n = 1503). CONCLUSIONS: Many children get hurt at school, particularly during informal recreation activities. Environmental modification and increased supervision are strategies that may reduce school injuries.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".