Injury statistics in outdoor compared to conventional early childhood education (ECE) programmes in Canada
Bibliographic record
Abstract
The benefits of outdoor play are well-established, yet safety concerns can limit outdoor play opportunities in early childhood education (ECE) programmes. Whether injury risk is higher in outdoor versus conventional ECE settings is unknown. This study examined injury rates and patterns in both settings. A survey was administered to 150 conventional and 160 outdoor ECE programmes in Canada in January-February 2023. The survey captured programme size, location, injury frequency/severity, and activity. Differences in minor, moderate and severe injury rates between settings were examined. Thirty-nine (13 conventional and 26 outdoor) programmes reported 855 minor injuries, with 72% occurring outdoors. Conventional programmes had a higher relative rate of minor outdoor injuries per hour per child compared to outdoor programmes (p = .009). No differences were found in moderate or high-severity injury rates (p > .05). Running and climbing were the most common activities linked to injuries in both settings. Boys and girls had equal prevalence of low-severity injuries, whereas boys had higher prevalence of medium and high-severity injuries. Outdoor-focused programmes had lower minor injury rates, though larger samples are needed to confirm this finding. These findings provide a foundation for future studies on injury rates in outdoor ECEs in Canada and internationally.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".