Examining outdoor play associations in Canadian early learning and child care centres: Cross-sectional insights from the Measuring Early Childhood Outside survey
Bibliographic record
Abstract
Abstract Canada lacks national data on the current provision of outdoor play (OP) in Early Learning and Child Care (ELCC) programs. In this study, we report results of the Measuring Early Childhood Outside (MECO) national survey to fill this gap and examine the factors that are associated with children’s OP and risky play in ELCC programs. Respondents included ELCC centres providing full-day licensed group care (birth to school entry) in Canada. Primary outcomes measured were OP frequency, OP duration and risky play occurrence. Hierarchical multiple regressions were used to examine relationships and interaction effects between the primary outcomes and 14 variables encompassing centre, staff, physical environment and OP provision characteristics, for infant/toddler-aged and preschool-aged programs separately. A total of 1,187 ELCC centres responded to the MECO survey (9.8% response rate), of which 67.2% were non-profit providers. Most centres went outdoors every day, regardless of the season, though they spent less time outdoors in the winter than in the summer. Risky play was limited, with play at heights being the most common, and use of fire the least common. Variables that emerged as positively associated with most outcomes across programs related to training of centre directors and educators, giving children the autonomy to make decisions about going outdoors, providing all-weather gear, including diverse affordances (loose parts, gardening elements, fixed equipment), having outdoor spaces larger than required by licensing requirements, and the use of off-site spaces. Information about the current state of OP in ELCC centres is important at a time of considerable expansion in the sector, helping inform evidence-based policy development to enhance OP opportunities across Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".