Injury among 1107 Canadian students with self-identified disabilities
Bibliographic record
Abstract
PURPOSE: Students with disabilities are at risk for poor health outcomes; however, the causes and consequences of injury in this group are not well understood. The epidemiologies of injuries among students with and without disabilities were profiled and compared. METHODS: The cross-sectional, 2002 Health Behaviour in School-aged Children Survey, was administered to a representative sample of 7235 students (grades 6-10) from Canada. Students who reported at least one functional difficulty due to a health condition were classified as having a disability. Primary outcomes were: (i) Medically attended injury; (ii) multiple injuries, and (iii) serious injury experiences during a 12-month period. RESULTS: Some 16.3% of students reported a disability. Injuries were more common in students with disabilities compared to those without disabilities (67% vs. 51% annually, p < 0.01). Students with disabilities experienced 30% increases in the risk for medically attended injury, multiple injury, and serious injury as compared to their peers. Consistent and statistically significant associations (p < 0.05) were identified between different types of disability and all injury outcomes. CONCLUSIONS: Canadian students who report disabilities experience higher risks for injury than their peers, perhaps due to an inability to perceive and avoid environmental hazards. Injury prevention programmes are needed to address these unique risk profiles in order to prevent additional disability or secondary conditions.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".