The effect of surface and season on playground injury rates
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of season on playground surface injury rates. METHODS: Injuries were identified through student incident report forms used in school districts in Calgary (Alberta) and the surrounding area. Playground surface exposure data were estimated based on school enrollment. RESULTS: A total of 539 injuries were reported during the 2007/2008 school year. Abrasions, bruises and inflammation were the most frequently reported injuries. The head, neck or face were most commonly injured. Injury rates per 1000 student days ranged between 0.018 (rubber crumb in spring) and 0.08 (poured-in-place and natural rock in the fall). Rubber crumb surfacing, compared with natural rock, had a significantly lower rate of injury in the spring, but no other season-surface comparisons were statistically significant. CONCLUSIONS: Rates of injury were similar for natural rock, poured-in-place, and crushed rock in the fall and winter. There was some evidence of a lower rate of injury on rubber crumb surfaces in the spring.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".