Surface characteristics, equipment height, and the occurrence and severity of playground injuries
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether surface characteristics (absorption level (g-max), material) and the height of play equipment are related to the occurrence and severity of injuries from falls. SETTING AND METHODS: During the summers of 1991 and 1995, conformity of play equipment to Canadian standards was assessed in a random sample (n = 102) of Montreal public playgrounds. Surface absorption (g-max) was tested using a Max Hic instrument and the height of equipment was measured. Concurrently, all injuries presenting at the emergency department of Montreal's two children's hospitals were recorded and parents were interviewed. Inspected equipment was implicated in 185 injuries. The g-max measurements (1995 only) were available for 110 of these playground accidents. RESULTS: One third of falls (35 %) occurred on a surface exceeding 200 g and the risk of injury was three times greater than for g level lower than 150 (95% confidence interval (CI) 1.45 to 6.35). On surfaces having absorption levels between 150 g and 200 g, injuries were 1.8 times more likely (95% CI 0.91 to 3.57). Injuries were 2.56 times more likely to occur on equipment higher than 2 m compared with equipment lower than 1.5 m. Analysis of risk factors by severity of injury failed to show any positive relationships between the g-max or height and severity, whereas surface material was a good predictor of severity. CONCLUSIONS: This study confirms the relationships between risk of injury, surface resilience, and height of equipment, as well as between type of material and severity of injury. Our data suggest that acceptable limits for surface resilience be set at less than 200 g, and perhaps even less than 150 g, and not exceed 2 m for equipment height. These findings reinforce the importance of installing recommended materials, such as sand, beneath play equipment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".